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Posted to commits@hbase.apache.org by nd...@apache.org on 2015/05/12 00:46:49 UTC
[01/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Repository: hbase
Updated Branches:
refs/heads/branch-1 95f1fe52e -> 33fe79cf6
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/export_control.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+
+= Export Control
+
+This distribution uses or includes cryptographic software. The country in
+which you currently reside may have restrictions on the import, possession,
+use, and/or re-export to another country, of encryption software. BEFORE
+using any encryption software, please check your country's laws, regulations
+and policies concerning the import, possession, or use, and re-export of
+encryption software, to see if this is permitted. See the
+link:http://www.wassenaar.org/[Wassenaar Arrangement] for more
+information.
+
+The U.S. Government Department of Commerce, Bureau of Industry and Security
+(BIS), has classified this software as Export Commodity Control Number (ECCN)
+5D002.C.1, which includes information security software using or performing
+cryptographic functions with asymmetric algorithms. The form and manner of this
+Apache Software Foundation distribution makes it eligible for export under the
+License Exception ENC Technology Software Unrestricted (TSU) exception (see the
+BIS Export Administration Regulations, Section 740.13) for both object code and
+source code.
+
+Apache HBase uses the built-in java cryptography libraries. See Oracle's
+information regarding
+link:http://www.oracle.com/us/products/export/export-regulations-345813.html[Java cryptographic export regulations]
+for more details.
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http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/index.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Apache HBase™ Home
+
+.Welcome to Apache HBase(TM)
+link:http://www.apache.org/[Apache HBase(TM)] is the link:http://hadoop.apache.org[Hadoop] database, a distributed, scalable, big data store.
+
+.When Would I Use Apache HBase?
+Use Apache HBase when you need random, realtime read/write access to your Big Data. +
+This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
+
+Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's link:http://research.google.com/archive/bigtable.html[Bigtable: A Distributed Storage System for Structured Data] by Chang et al.
+
+Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
+
+.Features
+- Linear and modular scalability.
+- Strictly consistent reads and writes.
+- Automatic and configurable sharding of tables
+- Automatic failover support between RegionServers.
+- Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
+- Easy to use Java API for client access.
+- Block cache and Bloom Filters for real-time queries.
+- Query predicate push down via server side Filters
+- Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options
+- Extensible jruby-based (JIRB) shell
+- Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX
+
+.Where Can I Get More Information?
+See the link:book.html#arch.overview[Architecture Overview], the link:book.html#faq[FAQ] and the other documentation links at the top!
+
+.Export Control
+The HBase distribution includes cryptographic software. See the link:export_control.html[export control notice].
+
+== News
+Feb 17, 2015:: link:http://www.meetup.com/hbaseusergroup/events/219260093/[HBase meetup around Strata+Hadoop World] in San Jose
+
+January 15th, 2015:: link:http://www.meetup.com/hbaseusergroup/events/218744798/[HBase meetup @ AppDynamics] in San Francisco
+
+November 20th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/205219992/[HBase meetup @ WANdisco] in San Ramon
+
+October 27th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/207386102/[HBase Meetup @ Apple] in Cupertino
+
+October 15th, 2014:: link:http://www.meetup.com/HBase-NYC/events/207655552[HBase Meetup @ Google] on the night before Strata/HW in NYC
+
+September 25th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/203173692/[HBase Meetup @ Continuuity] in Palo Alto
+
+August 28th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/197773762/[HBase Meetup @ Sift Science] in San Francisco
+
+July 17th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/190994082/[HBase Meetup @ HP] in Sunnyvale
+
+June 5th, 2014:: link:http://www.meetup.com/Hadoop-Summit-Community-San-Jose/events/179081342/[HBase BOF at Hadoop Summit], San Jose Convention Center
+
+May 5th, 2014:: link:http://www.hbasecon.com[HBaseCon2014] at the Hilton San Francisco on Union Square
+
+March 12th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/160757912/[HBase Meetup @ Ancestry.com] in San Francisco
+
+View link:old_news.html[Old News]
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/metrics.adoc
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+////
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Apache HBase (TM) Metrics
+
+== Introduction
+Apache HBase (TM) emits Hadoop link:http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/metrics/package-summary.html[metrics].
+
+== Setup
+
+First read up on Hadoop link:http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/metrics/package-summary.html[metrics].
+
+If you are using ganglia, the link:http://wiki.apache.org/hadoop/GangliaMetrics[GangliaMetrics] wiki page is useful read.
+
+To have HBase emit metrics, edit `$HBASE_HOME/conf/hadoop-metrics.properties` and enable metric 'contexts' per plugin. As of this writing, hadoop supports *file* and *ganglia* plugins. Yes, the hbase metrics files is named hadoop-metrics rather than _hbase-metrics_ because currently at least the hadoop metrics system has the properties filename hardcoded. Per metrics _context_, comment out the NullContext and enable one or more plugins instead.
+
+If you enable the _hbase_ context, on regionservers you'll see total requests since last
+metric emission, count of regions and storefiles as well as a count of memstore size.
+On the master, you'll see a count of the cluster's requests.
+
+Enabling the _rpc_ context is good if you are interested in seeing
+metrics on each hbase rpc method invocation (counts and time taken).
+
+The _jvm_ context is useful for long-term stats on running hbase jvms -- memory used, thread counts, etc. As of this writing, if more than one jvm is running emitting metrics, at least in ganglia, the stats are aggregated rather than reported per instance.
+
+== Using with JMX
+
+In addition to the standard output contexts supported by the Hadoop
+metrics package, you can also export HBase metrics via Java Management
+Extensions (JMX). This will allow viewing HBase stats in JConsole or
+any other JMX client.
+
+=== Enable HBase stats collection
+
+To enable JMX support in HBase, first edit `$HBASE_HOME/conf/hadoop-metrics.properties` to support metrics refreshing. (If you've running 0.94.1 and above, or have already configured `hadoop-metrics.properties` for another output context, you can skip this step).
+[source,bash]
+----
+# Configuration of the "hbase" context for null
+hbase.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+hbase.period=60
+
+# Configuration of the "jvm" context for null
+jvm.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+jvm.period=60
+
+# Configuration of the "rpc" context for null
+rpc.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+rpc.period=60
+----
+
+=== Setup JMX Remote Access
+
+For remote access, you will need to configure JMX remote passwords and access profiles. Create the files:
+`$HBASE_HOME/conf/jmxremote.passwd` (set permissions
+ to 600):: +
+----
+monitorRole monitorpass
+controlRole controlpass
+----
+
+`$HBASE_HOME/conf/jmxremote.access`:: +
+----
+monitorRole readonly
+controlRole readwrite
+----
+
+=== Configure JMX in HBase startup
+
+Finally, edit the `$HBASE_HOME/conf/hbase-env.sh` script to add JMX support:
+[source,bash]
+----
+HBASE_JMX_OPTS="-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.ssl=false"
+HBASE_JMX_OPTS="$HBASE_JMX_OPTS -Dcom.sun.management.jmxremote.password.file=$HBASE_HOME/conf/jmxremote.passwd"
+HBASE_JMX_OPTS="$HBASE_JMX_OPTS -Dcom.sun.management.jmxremote.access.file=$HBASE_HOME/conf/jmxremote.access"
+
+export HBASE_MASTER_OPTS="$HBASE_JMX_OPTS -Dcom.sun.management.jmxremote.port=10101"
+export HBASE_REGIONSERVER_OPTS="$HBASE_JMX_OPTS -Dcom.sun.management.jmxremote.port=10102"
+----
+
+After restarting the processes you want to monitor, you should now be able to run JConsole (included with the JDK since JDK 5.0) to view the statistics via JMX. HBase MBeans are exported under the *`hadoop`* domain in JMX.
+
+
+== Understanding HBase Metrics
+
+For more information on understanding HBase metrics, see the link:book.html#hbase_metrics[metrics section] in the Apache HBase Reference Guide.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/old_news.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Old Apache HBase (TM) News
+
+February 10th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/163139322/[HBase Meetup @ Continuuity] in Palo Alto
+
+January 30th, 2014:: link:http://www.meetup.com/hbaseusergroup/events/158491762/[HBase Meetup @ Apple] in Cupertino
+
+January 30th, 2014:: link:http://www.meetup.com/Los-Angeles-HBase-User-group/events/160560282/[Los Angeles HBase User Group] in El Segundo
+
+October 24th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/140759692/[HBase User] and link:http://www.meetup.com/hackathon/events/144366512/[Developer] Meetup at HortonWorksin Palo Alto
+
+September 26, 2013:: link:http://www.meetup.com/hbaseusergroup/events/135862292/[HBase Meetup at Arista Networks] in San Francisco
+
+August 20th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/120534362/[HBase Meetup at Flurry] in San Francisco
+
+July 16th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/119929152/[HBase Meetup at Twitter] in San Francisco
+
+June 25th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/119154442/[Hadoop Summit Meetup].at San Jose Convention Center
+
+June 14th, 2013:: link:http://kijicon.eventbrite.com/[KijiCon: Building Big Data Apps] in San Francisco.
+
+June 13th, 2013:: link:http://www.hbasecon.com/[HBaseCon2013] in San Francisco. Submit an Abstract!
+
+June 12th, 2013:: link:http://www.meetup.com/hackathon/events/123403802/[HBaseConHackAthon] at the Cloudera office in San Francisco.
+
+April 11th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/103587852/[HBase Meetup at AdRoll] in San Francisco
+
+February 28th, 2013:: link:http://www.meetup.com/hbaseusergroup/events/96584102/[HBase Meetup at Intel Mission Campus]
+
+February 19th, 2013:: link:http://www.meetup.com/hackathon/events/103633042/[Developers PowWow] at HortonWorks' new digs
+
+January 23rd, 2013:: link:http://www.meetup.com/hbaseusergroup/events/91381312/[HBase Meetup at WibiData World HQ!]
+
+December 4th, 2012:: link:http://www.meetup.com/hackathon/events/90536432/[0.96 Bug Squashing and Testing Hackathon] at Cloudera, SF.
+
+October 29th, 2012:: link:http://www.meetup.com/hbaseusergroup/events/82791572/[HBase User Group Meetup] at Wize Commerce in San Mateo.
+
+October 25th, 2012:: link:http://www.meetup.com/HBase-NYC/events/81728932/[Strata/Hadoop World HBase Meetup.] in NYC
+
+September 11th, 2012:: link:http://www.meetup.com/hbaseusergroup/events/80621872/[Contributor's Pow-Wow at HortonWorks HQ.]
+
+August 8th, 2012:: link:http://www.apache.org/dyn/closer.cgi/hbase/[Apache HBase 0.94.1 is available for download]
+
+June 15th, 2012:: link:http://www.meetup.com/hbaseusergroup/events/59829652/[Birds-of-a-feather] in San Jose, day after:: link:http://hadoopsummit.org[Hadoop Summit]
+
+May 23rd, 2012:: link:http://www.meetup.com/hackathon/events/58953522/[HackConAthon] in Palo Alto
+
+May 22nd, 2012:: link:http://www.hbasecon.com[HBaseCon2012] in San Francisco
+
+March 27th, 2012:: link:http://www.meetup.com/hbaseusergroup/events/56021562/[Meetup @ StumbleUpon] in San Francisco
+
+January 19th, 2012:: link:http://www.meetup.com/hbaseusergroup/events/46702842/[Meetup @ EBay]
+
+January 23rd, 2012:: Apache HBase 0.92.0 released. link:http://www.apache.org/dyn/closer.cgi/hbase/[Download it!]
+
+December 23rd, 2011:: Apache HBase 0.90.5 released. link:http://www.apache.org/dyn/closer.cgi/hbase/[Download it!]
+
+November 29th, 2011:: link:http://www.meetup.com/hackathon/events/41025972/[Developer Pow-Wow in SF] at Salesforce HQ
+
+November 7th, 2011:: link:http://www.meetup.com/hbaseusergroup/events/35682812/[HBase Meetup in NYC (6PM)] at the AppNexus office
+
+August 22nd, 2011:: link:http://www.meetup.com/hbaseusergroup/events/28518471/[HBase Hackathon (11AM) and Meetup (6PM)] at FB in PA
+
+June 30th, 2011:: link:http://www.meetup.com/hbaseusergroup/events/20572251/[HBase Contributor Day], the day after the:: link:http://developer.yahoo.com/events/hadoopsummit2011/[Hadoop Summit] hosted by Y!
+
+June 8th, 2011:: link:http://berlinbuzzwords.de/wiki/hbase-workshop-and-hackathon[HBase Hackathon] in Berlin to coincide with:: link:http://berlinbuzzwords.de/[Berlin Buzzwords]
+
+May 19th, 2011: Apache HBase 0.90.3 released. link:http://www.apache.org/dyn/closer.cgi/hbase/[Download it!]
+
+April 12th, 2011: Apache HBase 0.90.2 released. link:http://www.apache.org/dyn/closer.cgi/hbase/[Download it!]
+
+March 21st, 2011:: link:http://www.meetup.com/hackathon/events/16770852/[HBase 0.92 Hackathon at StumbleUpon, SF]
+February 22nd, 2011:: link:http://www.meetup.com/hbaseusergroup/events/16492913/[HUG12: February HBase User Group at StumbleUpon SF]
+December 13th, 2010:: link:http://www.meetup.com/hackathon/calendar/15597555/[HBase Hackathon: Coprocessor Edition]
+November 19th, 2010:: link:http://huguk.org/[Hadoop HUG in London] is all about Apache HBase
+November 15-19th, 2010:: link:http://www.devoxx.com/display/Devoxx2K10/Home[Devoxx] features HBase Training and multiple HBase presentations
+
+October 12th, 2010:: HBase-related presentations by core contributors and users at:: link:http://www.cloudera.com/company/press-center/hadoop-world-nyc/[Hadoop World 2010]
+
+October 11th, 2010:: link:http://www.meetup.com/hbaseusergroup/calendar/14606174/[HUG-NYC: HBase User Group NYC Edition] (Night before Hadoop World)
+June 30th, 2010:: link:http://www.meetup.com/hbaseusergroup/calendar/13562846/[Apache HBase Contributor Workshop] (Day after Hadoop Summit)
+May 10th, 2010:: Apache HBase graduates from Hadoop sub-project to Apache Top Level Project
+
+April 19, 2010:: Signup for link:http://www.meetup.com/hbaseusergroup/calendar/12689490/[HBase User Group Meeting, HUG10] hosted by Trend Micro
+
+March 10th, 2010:: link:http://www.meetup.com/hbaseusergroup/calendar/12689351/[HBase User Group Meeting, HUG9] hosted by Mozilla
+
+January 27th, 2010:: Sign up for the link:http://www.meetup.com/hbaseusergroup/calendar/12241393/[HBase User Group Meeting, HUG8], at StumbleUpon in SF
+
+September 8th, 2010:: Apache HBase 0.20.0 is faster, stronger, slimmer, and sweeter tasting than any previous Apache HBase release. Get it off the link:http://www.apache.org/dyn/closer.cgi/hbase/[Releases] page.
+
+November 2-6th, 2009:: link:http://dev.us.apachecon.com/c/acus2009/[ApacheCon] in Oakland. The Apache Foundation will be celebrating its 10th anniversary in beautiful Oakland by the Bay. Lots of good talks and meetups including an HBase presentation by a couple of the lads.
+
+October 2nd, 2009:: HBase at Hadoop World in NYC. A few of us will be talking on Practical HBase out east at link:http://www.cloudera.com/hadoop-world-nyc[Hadoop World: NYC].
+
+August 7th-9th, 2009:: HUG7 and HBase Hackathon at StumbleUpon in SF: Sign up for the:: link:http://www.meetup.com/hbaseusergroup/calendar/10950511/[HBase User Group Meeting, HUG7] or for the link:http://www.meetup.com/hackathon/calendar/10951718/[Hackathon] or for both (all are welcome!).
+
+June, 2009:: HBase at HadoopSummit2009 and at NOSQL: See the link:http://wiki.apache.org/hadoop/HBase/HBasePresentations[presentations]
+
+March 3rd, 2009 :: HUG6 -- link:http://www.meetup.com/hbaseusergroup/calendar/9764004/[HBase User Group 6]
+
+January 30th, 2009:: LA Hbackathon: link:http://www.meetup.com/hbasela/calendar/9450876/[HBase January Hackathon Los Angeles] at link:http://streamy.com[Streamy] in Manhattan Beach
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/pseudo-distributed.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+
+= Running Apache HBase (TM) in pseudo-distributed mode
+This page has been retired. The contents have been moved to the link:book.html#distributed[Distributed Operation: Pseudo- and Fully-distributed modes] section in the Reference Guide.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/replication.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Apache HBase (TM) Replication
+
+This information has been moved to link:book.html#cluster_replication"[the Cluster Replication] section of the link:book.html[Apache HBase Reference Guide].
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/resources.adoc
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+////
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+= Other Apache HBase (TM) Resources
+
+== Books
+HBase: The Definitive Guide:: link:http://shop.oreilly.com/product/0636920014348.do[HBase: The Definitive Guide, _Random Access to Your Planet-Size Data_] by Lars George. Publisher: O'Reilly Media, Released: August 2011, Pages: 556.
+
+HBase In Action:: link:http://www.manning.com/dimidukkhurana[HBase In Action] By Nick Dimiduk and Amandeep Khurana. Publisher: Manning, MEAP Began: January 2012, Softbound print: Fall 2012, Pages: 350.
+
+HBase Administration Cookbook:: link:http://www.packtpub.com/hbase-administration-for-optimum-database-performance-cookbook/book[HBase Administration Cookbook] by Yifeng Jiang. Publisher: PACKT Publishing, Release: Expected August 2012, Pages: 335.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/sponsors.adoc
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+////
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Apache HBase(TM) Sponsors
+
+First off, thanks to link:http://www.apache.org/foundation/thanks.html[all who sponsor] our parent, the Apache Software Foundation.
+
+The below companies have been gracious enough to provide their commerical tool offerings free of charge to the Apache HBase(TM) project.
+
+* The crew at link:http://www.ej-technologies.com/[ej-technologies] have been letting us use link:http://www.ej-technologies.com/products/jprofiler/overview.html[JProfiler] for years now.
+
+* The lads at link:http://headwaysoftware.com/[headway software] have given us a license for link:http://headwaysoftware.com/products/?code=Restructure101[Restructure101] so we can untangle our interdependency mess.
+
+* link:http://www.yourkit.com[YourKit] allows us to use their link:http://www.yourkit.com/overview/index.jsp[Java Profiler].
+* Some of us use link:http://www.jetbrains.com/idea[IntelliJ IDEA] thanks to link:http://www.jetbrains.com/[JetBrains].
+
+== Sponsoring the Apache Software Foundation">
+To contribute to the Apache Software Foundation, a good idea in our opinion, see the link:http://www.apache.org/foundation/sponsorship.html[ASF Sponsorship] page.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/resources/.htaccess
----------------------------------------------------------------------
diff --git a/src/main/site/resources/.htaccess b/src/main/site/resources/.htaccess
new file mode 100644
index 0000000..20bf651
--- /dev/null
+++ b/src/main/site/resources/.htaccess
@@ -0,0 +1,8 @@
+
+# Redirect replication URL to the right section of the book
+# Rule added 2015-1-12 -- can be removed in 6 months
+Redirect permanent /replication.html /book.html#_cluster_replication
+
+# Redirect old page-per-chapter book sections to new single file.
+RedirectMatch permanent ^/book/(.*)\.html$ /book.html#$1
+RedirectMatch permanent ^/book/$ /book.html
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/resources/book/.empty
----------------------------------------------------------------------
diff --git a/src/main/site/resources/book/.empty b/src/main/site/resources/book/.empty
new file mode 100644
index 0000000..5513814
--- /dev/null
+++ b/src/main/site/resources/book/.empty
@@ -0,0 +1 @@
+# This directory is here so that we can have rewrite rules in our .htaccess to maintain old links. Otherwise we fall under some top-level niceness redirects because we have a file named book.html.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/resources/css/site.css
----------------------------------------------------------------------
diff --git a/src/main/site/resources/css/site.css b/src/main/site/resources/css/site.css
index f26d03c..17f0ff0 100644
--- a/src/main/site/resources/css/site.css
+++ b/src/main/site/resources/css/site.css
@@ -72,8 +72,10 @@ h4 {
#banner {
background: none;
+ padding: 10px;
}
+/*
#banner img {
padding: 10px;
margin: auto;
@@ -82,6 +84,7 @@ h4 {
float: center;
height:;
}
+ */
#breadcrumbs {
background-image: url();
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/resources/images/hbase_logo_with_orca.png
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diff --git a/src/main/site/resources/images/hbase_logo_with_orca.png b/src/main/site/resources/images/hbase_logo_with_orca.png
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diff --git a/src/main/site/resources/images/hbase_logo_with_orca.xcf b/src/main/site/resources/images/hbase_logo_with_orca.xcf
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index 0000000..8d88da2
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diff --git a/src/main/site/resources/images/jumping-orca_transparent_rotated.xcf b/src/main/site/resources/images/jumping-orca_transparent_rotated.xcf
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index 0000000..be9e3d9
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http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/resources/images/region_split_process.png
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diff --git a/src/main/site/resources/images/region_split_process.png b/src/main/site/resources/images/region_split_process.png
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http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/site.xml
----------------------------------------------------------------------
diff --git a/src/main/site/site.xml b/src/main/site/site.xml
index 0d60e00..b7debd3 100644
--- a/src/main/site/site.xml
+++ b/src/main/site/site.xml
@@ -19,18 +19,34 @@
*/
-->
-
<project xmlns="http://maven.apache.org/DECORATION/1.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/DECORATION/1.0.0 http://maven.apache.org/xsd/decoration-1.0.0.xsd">
+ <skin>
+ <groupId>lt.velykis.maven.skins</groupId>
+ <artifactId>reflow-maven-skin</artifactId>
+ <version>1.1.1</version>
+ </skin>
+ <custom>
+ <reflowSkin>
+ <theme>bootswatch-spacelab</theme>
+ <bottomNav maxSpan="9">
+ <column>Apache HBase Project</column>
+ <column>^Documentation</column>
+ <column>0.94 Documentation|ASF</column>
+ </bottomNav>
+ </reflowSkin>
+ </custom>
<bannerLeft>
<name>Apache HBase</name>
<src>images/hbase_logo.png</src>
<href>http://hbase.apache.org/</href>
</bannerLeft>
- <bannerRight />
- <version position="right" />
- <publishDate position="right" />
+ <bannerRight>
+ <name>Apache HBase Orca</name>
+ <src>images/hbasecon2015.30percent.png</src>
+ <href>http://hbasecon.com/</href>
+ </bannerRight>
<body>
<menu name="Apache HBase Project">
<item name="Overview" href="index.html"/>
@@ -47,35 +63,31 @@
<item name="Other resources" href="resources.html" />
</menu>
<menu name="Documentation">
- <item name="Getting Started" href="book/quickstart.html" />
- <item name="User API" href="apidocs/index.html" />
- <item name="Developer API" href="devapidocs/index.html" />
+ <item name="Reference Guide" href="book.html" target="_blank" />
+ <item name="Reference Guide (PDF)" href="apache_hbase_reference_guide.pdf" target="_blank" />
+ <item name="Getting Started" href="book.html#quickstart" target="_blank" />
+ <item name="User API" href="apidocs/index.html" target="_blank" />
+ <item name="Developer API" href="devapidocs/index.html" target="_blank" />
<item name="X-Ref" href="xref/index.html" />
- <item name="Ref Guide (multi-page)" href="book/book.html" />
- <item name="Ref Guide (single-page)" href="book.html" />
- <item name="中文参考指南(单页)" href="http://abloz.com/hbase/book.html" />
- <item name="FAQ" href="book/faq.html" />
- <item name="Videos/Presentations" href="book.html#other.info" />
- <item name="Wiki" href="http://wiki.apache.org/hadoop/Hbase" />
- <item name="ACID Semantics" href="acid-semantics.html" />
- <item name="Bulk Loads" href="book.html#arch.bulk.load" />
- <item name="Metrics" href="metrics.html" />
- <item name="HBase on Windows" href="cygwin.html" />
- <item name="Cluster replication" href="replication.html" />
+ <item name="中文参考指南(单页)" href="http://abloz.com/hbase/book.html" target="_blank" />
+ <item name="FAQ" href="book.html#faq" target="_blank" />
+ <item name="Videos/Presentations" href="book.html#other.info" target="_blank" />
+ <item name="Wiki" href="http://wiki.apache.org/hadoop/Hbase" target="_blank" />
+ <item name="ACID Semantics" href="acid-semantics.html" target="_blank" />
+ <item name="Bulk Loads" href="book.html#arch.bulk.load" target="_blank" />
+ <item name="Metrics" href="metrics.html" target="_blank" />
+ <item name="HBase on Windows" href="cygwin.html" target="_blank" />
+ <item name="Cluster replication" href="replication.html" target="_blank" />
</menu>
<menu name="0.94 Documentation">
- <item name="API" href="0.94/apidocs/index.html" />
- <item name="X-Ref" href="0.94/xref/index.html" />
- <item name="Ref Guide (single-page)" href="0.94/book.html" />
+ <item name="API" href="0.94/apidocs/index.html" target="_blank" />
+ <item name="X-Ref" href="0.94/xref/index.html" target="_blank" />
+ <item name="Ref Guide (single-page)" href="0.94/book.html" target="_blank" />
</menu>
<menu name="ASF">
- <item name="Apache Software Foundation" href="http://www.apache.org/foundation/" />
- <item name="How Apache Works" href="http://www.apache.org/foundation/how-it-works.html" />
- <item name="Sponsoring Apache" href="http://www.apache.org/foundation/sponsorship.html" />
+ <item name="Apache Software Foundation" href="http://www.apache.org/foundation/" target="_blank" />
+ <item name="How Apache Works" href="http://www.apache.org/foundation/how-it-works.html" target="_blank" />
+ <item name="Sponsoring Apache" href="http://www.apache.org/foundation/sponsorship.html" target="_blank" />
</menu>
</body>
- <skin>
- <groupId>org.apache.maven.skins</groupId>
- <artifactId>maven-stylus-skin</artifactId>
- </skin>
</project>
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/xdoc/index.xml
----------------------------------------------------------------------
diff --git a/src/main/site/xdoc/index.xml b/src/main/site/xdoc/index.xml
index 964d887..d7e1e4e 100644
--- a/src/main/site/xdoc/index.xml
+++ b/src/main/site/xdoc/index.xml
@@ -17,17 +17,21 @@
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/XDOC/2.0 http://maven.apache.org/xsd/xdoc-2.0.xsd">
<properties>
- <title>Apache HBase™ Home</title>
+ <title>Apache HBase™ Home</title>
<link rel="shortcut icon" href="/images/favicon.ico" />
</properties>
<body>
- <section name="Welcome to Apache HBase™">
- <p><a href="http://www.apache.org/">Apache</a> HBase™ is the <a href="http://hadoop.apache.org">Hadoop</a> database, a distributed, scalable, big data store.
+ <section name="Welcome to Apache HBase™">
+ <p><a href="http://www.apache.org/">Apache</a> HBase™ is the <a href="http://hadoop.apache.org">Hadoop</a> database, a distributed, scalable, big data store.
+ </p>
+ <h4>Download Apache HBase™</h4>
+ <p>
+ Click <b><a href="http://www.apache.org/dyn/closer.cgi/hbase/">here</a></b> to download Apache HBase™.
</p>
<h4>When Would I Use Apache HBase?</h4>
<p>
- Use Apache HBase when you need random, realtime read/write access to your Big Data.
+ Use Apache HBase™ when you need random, realtime read/write access to your Big Data.
This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's <a href="http://research.google.com/archive/bigtable.html">Bigtable: A Distributed Storage System for Structured Data</a> by Chang et al.
Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
@@ -68,6 +72,14 @@ Apache HBase is an open-source, distributed, versioned, non-relational database
</p>
</section>
<section name="News">
+ <p>May 7th, 2015 <a href="http://hbasecon.com/">HBaseCon2015</a> in San Francisco</p>
+ <p>February 17th, 2015 <a href="http://www.meetup.com/hbaseusergroup/events/219260093/">HBase meetup around Strata+Hadoop World</a> in San Jose</p>
+ <p>January 15th, 2015 <a href="http://www.meetup.com/hbaseusergroup/events/218744798/">HBase meetup @ AppDynamics</a> in San Francisco</p>
+ <p>November 20th, 2014 <a href="http://www.meetup.com/hbaseusergroup/events/205219992/">HBase meetup @ WANdisco</a> in San Ramon</p>
+ <p>October 27th, 2014 <a href="http://www.meetup.com/hbaseusergroup/events/207386102/">HBase Meetup @ Apple</a> in Cupertino</p>
+ <p>October 15th, 2014 <a href="http://www.meetup.com/HBase-NYC/events/207655552">HBase Meetup @ Google</a> on the night before Strata/HW in NYC</p>
+ <p>September 25th, 2014 <a href="http://www.meetup.com/hbaseusergroup/events/203173692/">HBase Meetup @ Continuuity</a> in Palo Alto</p>
+ <p>August 28th, 2014 <a href="http://www.meetup.com/hbaseusergroup/events/197773762/">HBase Meetup @ Sift Science</a> in San Francisco</p>
<p>July 17th, 2014 <a href="http://www.meetup.com/hbaseusergroup/events/190994082/">HBase Meetup @ HP</a> in Sunnyvale</p>
<p>June 5th, 2014 <a href="http://www.meetup.com/Hadoop-Summit-Community-San-Jose/events/179081342/">HBase BOF at Hadoop Summit</a>, San Jose Convention Center</p>
<p>May 5th, 2014 <a href="http://www.hbasecon.com">HBaseCon2014</a> at the Hilton San Francisco on Union Square</p>
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/xdoc/replication.xml
----------------------------------------------------------------------
diff --git a/src/main/site/xdoc/replication.xml b/src/main/site/xdoc/replication.xml
index 97aaf51..2633f08 100644
--- a/src/main/site/xdoc/replication.xml
+++ b/src/main/site/xdoc/replication.xml
@@ -26,520 +26,6 @@
</title>
</properties>
<body>
- <section name="Overview">
- <p>
- The replication feature of Apache HBase (TM) provides a way to copy data between HBase deployments. It
- can serve as a disaster recovery solution and can contribute to provide
- higher availability at the HBase layer. It can also serve more practically;
- for example, as a way to easily copy edits from a web-facing cluster to a "MapReduce"
- cluster which will process old and new data and ship back the results
- automatically.
- </p>
- <p>
- The basic architecture pattern used for Apache HBase replication is (HBase cluster) master-push;
- it is much easier to keep track of what’s currently being replicated since
- each region server has its own write-ahead-log (aka WAL or HLog), just like
- other well known solutions like MySQL master/slave replication where
- there’s only one bin log to keep track of. One master cluster can
- replicate to any number of slave clusters, and each region server will
- participate to replicate their own stream of edits. For more information
- on the different properties of master/slave replication and other types
- of replication, please consult <a href="http://highscalability.com/blog/2009/8/24/how-google-serves-data-from-multiple-datacenters.html">
- How Google Serves Data From Multiple Datacenters</a>.
- </p>
- <p>
- The replication is done asynchronously, meaning that the clusters can
- be geographically distant, the links between them can be offline for
- some time, and rows inserted on the master cluster won’t be
- available at the same time on the slave clusters (eventual consistency).
- </p>
- <p>
- The replication format used in this design is conceptually the same as
- <a href="http://dev.mysql.com/doc/refman/5.1/en/replication-formats.html">
- MySQL’s statement-based replication </a>. Instead of SQL statements, whole
- WALEdits (consisting of multiple cell inserts coming from the clients'
- Put and Delete) are replicated in order to maintain atomicity.
- </p>
- <p>
- The HLogs from each region server are the basis of HBase replication,
- and must be kept in HDFS as long as they are needed to replicate data
- to any slave cluster. Each RS reads from the oldest log it needs to
- replicate and keeps the current position inside ZooKeeper to simplify
- failure recovery. That position can be different for every slave
- cluster, same for the queue of HLogs to process.
- </p>
- <p>
- The clusters participating in replication can be of asymmetric sizes
- and the master cluster will do its “best effort” to balance the stream
- of replication on the slave clusters by relying on randomization.
- </p>
- <p>
- As of version 0.92, Apache HBase supports master/master and cyclic
- replication as well as replication to multiple slaves.
- </p>
- <img src="images/replication_overview.png"/>
- </section>
- <section name="Enabling replication">
- <p>
- The guide on enabling and using cluster replication is contained
- in the API documentation shipped with your Apache HBase distribution.
- </p>
- <p>
- The most up-to-date documentation is
- <a href="apidocs/org/apache/hadoop/hbase/replication/package-summary.html#requirements">
- available at this address</a>.
- </p>
- </section>
- <section name="Life of a log edit">
- <p>
- The following sections describe the life of a single edit going from a
- client that communicates with a master cluster all the way to a single
- slave cluster.
- </p>
- <section name="Normal processing">
- <p>
- The client uses an API that sends a Put, Delete or ICV to a region
- server. The key values are transformed into a WALEdit by the region
- server and is inspected by the replication code that, for each family
- that is scoped for replication, adds the scope to the edit. The edit
- is appended to the current WAL and is then applied to its MemStore.
- </p>
- <p>
- In a separate thread, the edit is read from the log (as part of a batch)
- and only the KVs that are replicable are kept (that is, that they are part
- of a family scoped GLOBAL in the family's schema, non-catalog so not
- hbase:meta or -ROOT-, and did not originate in the target slave cluster - in
- case of cyclic replication).
- </p>
- <p>
- The edit is then tagged with the master's cluster UUID.
- When the buffer is filled, or the reader hits the end of the file,
- the buffer is sent to a random region server on the slave cluster.
- </p>
- <p>
- Synchronously, the region server that receives the edits reads them
- sequentially and separates each of them into buffers, one per table.
- Once all edits are read, each buffer is flushed using HTable, the normal
- HBase client.The master's cluster UUID is retained in the edits applied at
- the slave cluster in order to allow cyclic replication.
- </p>
- <p>
- Back in the master cluster's region server, the offset for the current
- WAL that's being replicated is registered in ZooKeeper.
- </p>
- </section>
- <section name="Non-responding slave clusters">
- <p>
- The edit is inserted in the same way.
- </p>
- <p>
- In the separate thread, the region server reads, filters and buffers
- the log edits the same way as during normal processing. The slave
- region server that's contacted doesn't answer to the RPC, so the master
- region server will sleep and retry up to a configured number of times.
- If the slave RS still isn't available, the master cluster RS will select a
- new subset of RS to replicate to and will retry sending the buffer of
- edits.
- </p>
- <p>
- In the mean time, the WALs will be rolled and stored in a queue in
- ZooKeeper. Logs that are archived by their region server (archiving is
- basically moving a log from the region server's logs directory to a
- central logs archive directory) will update their paths in the in-memory
- queue of the replicating thread.
- </p>
- <p>
- When the slave cluster is finally available, the buffer will be applied
- the same way as during normal processing. The master cluster RS will then
- replicate the backlog of logs.
- </p>
- </section>
- </section>
- <section name="Internals">
- <p>
- This section describes in depth how each of replication's internal
- features operate.
- </p>
- <section name="Replication Zookeeper State">
- <p>
- HBase replication maintains all of its state in Zookeeper. By default, this state is
- contained in the base znode:
- </p>
- <pre>
- /hbase/replication
- </pre>
- <p>
- There are two major child znodes in the base replication znode:
- <ul>
- <li><b>Peers znode:</b> /hbase/replication/peers</li>
- <li><b>RS znode:</b> /hbase/replication/rs</li>
- </ul>
- </p>
- <section name="The Peers znode">
- <p>
- The <b>peers znode</b> contains a list of all peer replication clusters and the
- current replication state of those clusters. It has one child <i>peer znode</i>
- for each peer cluster. The <i>peer znode</i> is named with the cluster id provided
- by the user in the HBase shell. The value of the <i>peer znode</i> contains
- the peers cluster key provided by the user in the HBase Shell. The cluster key
- contains a list of zookeeper nodes in the clusters quorum, the client port for the
- zookeeper quorum, and the base znode for HBase
- (i.e. “zk1.host.com,zk2.host.com,zk3.host.com:2181:/hbase”).
- </p>
- <pre>
- /hbase/replication/peers
- /1 [Value: zk1.host.com,zk2.host.com,zk3.host.com:2181:/hbase]
- /2 [Value: zk5.host.com,zk6.host.com,zk7.host.com:2181:/hbase]
- </pre>
- <p>
- Each of these <i>peer znodes</i> has a child znode that indicates whether or not
- replication is enabled on that peer cluster. These <i>peer-state znodes</i> do not
- have child znodes and simply contain a boolean value (i.e. ENABLED or DISABLED).
- This value is read/maintained by the <i>ReplicationPeer.PeerStateTracker</i> class.
- </p>
- <pre>
- /hbase/replication/peers
- /1/peer-state [Value: ENABLED]
- /2/peer-state [Value: DISABLED]
- </pre>
- </section>
- <section name="The RS znode">
- <p>
- The <b>rs znode</b> contains a list of all outstanding HLog files in the cluster
- that need to be replicated. The list is divided into a set of queues organized by
- region server and the peer cluster the region server is shipping the HLogs to. The
- <b>rs znode</b> has one child znode for each region server in the cluster. The child
- znode name is simply the regionserver name (a concatenation of the region server’s
- hostname, client port and start code). These region servers could either be dead or alive.
- </p>
- <pre>
- /hbase/replication/rs
- /hostname.example.org,6020,1234
- /hostname2.example.org,6020,2856
- </pre>
- <p>
- Within each region server znode, the region server maintains a set of HLog replication
- queues. Each region server has one queue for every peer cluster it replicates to.
- These queues are represented by child znodes named using the cluster id of the peer
- cluster they represent (see the peer znode section).
- </p>
- <pre>
- /hbase/replication/rs
- /hostname.example.org,6020,1234
- /1
- /2
- </pre>
- <p>
- Each queue has one child znode for every HLog that still needs to be replicated.
- The value of these HLog child znodes is the latest position that has been replicated.
- This position is updated every time a HLog entry is replicated.
- </p>
- <pre>
- /hbase/replication/rs
- /hostname.example.org,6020,1234
- /1
- 23522342.23422 [VALUE: 254]
- 12340993.22342 [VALUE: 0]
- </pre>
- </section>
- </section>
- <section name="Configuration Parameters">
- <section name="Zookeeper znode paths">
- <p>
- All of the base znode names are configurable through parameters:
- </p>
- <table border="1">
- <tr>
- <td><b>Parameter</b></td>
- <td><b>Default Value</b></td>
- </tr>
- <tr>
- <td>zookeeper.znode.parent</td>
- <td>/hbase</td>
- </tr>
- <tr>
- <td>zookeeper.znode.replication</td>
- <td>replication</td>
- </tr>
- <tr>
- <td>zookeeper.znode.replication.peers</td>
- <td>peers</td>
- </tr>
- <tr>
- <td>zookeeper.znode.replication.peers.state</td>
- <td>peer-state</td>
- </tr>
- <tr>
- <td>zookeeper.znode.replication.rs</td>
- <td>rs</td>
- </tr>
- </table>
- <p>
- The default replication znode structure looks like the following:
- </p>
- <pre>
- /hbase/replication/peers/{peerId}/peer-state
- /hbase/replication/rs
- </pre>
- </section>
- <section name="Other parameters">
- <ul>
- <li><b>hbase.replication</b> (Default: false) - Controls whether replication is enabled
- or disabled for the cluster.</li>
- <li><b>replication.sleep.before.failover</b> (Default: 2000) - The amount of time a failover
- worker waits before attempting to replicate a dead region server’s HLog queues.</li>
- <li><b>replication.executor.workers</b> (Default: 1) - The number of dead region servers
- one region server should attempt to failover simultaneously.</li>
- </ul>
- </section>
- </section>
- <section name="Choosing region servers to replicate to">
- <p>
- When a master cluster RS initiates a replication source to a slave cluster,
- it first connects to the slave's ZooKeeper ensemble using the provided
- cluster key (that key is composed of the value of hbase.zookeeper.quorum,
- zookeeper.znode.parent and hbase.zookeeper.property.clientPort). It
- then scans the "rs" directory to discover all the available sinks
- (region servers that are accepting incoming streams of edits to replicate)
- and will randomly choose a subset of them using a configured
- ratio (which has a default value of 10%). For example, if a slave
- cluster has 150 machines, 15 will be chosen as potential recipient for
- edits that this master cluster RS will be sending. Since this is done by all
- master cluster RSs, the probability that all slave RSs are used is very high,
- and this method works for clusters of any size. For example, a master cluster
- of 10 machines replicating to a slave cluster of 5 machines with a ratio
- of 10% means that the master cluster RSs will choose one machine each
- at random, thus the chance of overlapping and full usage of the slave
- cluster is higher.
- </p>
- <p>
- A ZK watcher is placed on the ${zookeeper.znode.parent}/rs node of
- the slave cluster by each of the master cluster's region servers.
- This watch is used to monitor changes in the composition of the
- slave cluster. When nodes are removed from the slave cluster (or
- if nodes go down and/or come back up), the master cluster's region
- servers will respond by selecting a new pool of slave region servers
- to replicate to.
- </p>
- </section>
- <section name="Keeping track of logs">
- <p>
- Every master cluster RS has its own znode in the replication znodes hierarchy.
- It contains one znode per peer cluster (if 5 slave clusters, 5 znodes
- are created), and each of these contain a queue
- of HLogs to process. Each of these queues will track the HLogs created
- by that RS, but they can differ in size. For example, if one slave
- cluster becomes unavailable for some time then the HLogs should not be deleted,
- thus they need to stay in the queue (while the others are processed).
- See the section named "Region server failover" for an example.
- </p>
- <p>
- When a source is instantiated, it contains the current HLog that the
- region server is writing to. During log rolling, the new file is added
- to the queue of each slave cluster's znode just before it's made available.
- This ensures that all the sources are aware that a new log exists
- before HLog is able to append edits into it, but this operations is
- now more expensive.
- The queue items are discarded when the replication thread cannot read
- more entries from a file (because it reached the end of the last block)
- and that there are other files in the queue.
- This means that if a source is up-to-date and replicates from the log
- that the region server writes to, reading up to the "end" of the
- current file won't delete the item in the queue.
- </p>
- <p>
- When a log is archived (because it's not used anymore or because there's
- too many of them per hbase.regionserver.maxlogs typically because insertion
- rate is faster than region flushing), it will notify the source threads that the path
- for that log changed. If the a particular source was already done with
- it, it will just ignore the message. If it's in the queue, the path
- will be updated in memory. If the log is currently being replicated,
- the change will be done atomically so that the reader doesn't try to
- open the file when it's already moved. Also, moving a file is a NameNode
- operation so, if the reader is currently reading the log, it won't
- generate any exception.
- </p>
- </section>
- <section name="Reading, filtering and sending edits">
- <p>
- By default, a source will try to read from a log file and ship log
- entries as fast as possible to a sink. This is first limited by the
- filtering of log entries; only KeyValues that are scoped GLOBAL and
- that don't belong to catalog tables will be retained. A second limit
- is imposed on the total size of the list of edits to replicate per slave,
- which by default is 64MB. This means that a master cluster RS with 3 slaves
- will use at most 192MB to store data to replicate. This doesn't account
- the data filtered that wasn't garbage collected.
- </p>
- <p>
- Once the maximum size of edits was buffered or the reader hits the end
- of the log file, the source thread will stop reading and will choose
- at random a sink to replicate to (from the list that was generated by
- keeping only a subset of slave RSs). It will directly issue a RPC to
- the chosen machine and will wait for the method to return. If it's
- successful, the source will determine if the current file is emptied
- or if it should continue to read from it. If the former, it will delete
- the znode in the queue. If the latter, it will register the new offset
- in the log's znode. If the RPC threw an exception, the source will retry
- 10 times until trying to find a different sink.
- </p>
- </section>
- <section name="Cleaning logs">
- <p>
- If replication isn't enabled, the master's logs cleaning thread will
- delete old logs using a configured TTL. This doesn't work well with
- replication since archived logs passed their TTL may still be in a
- queue. Thus, the default behavior is augmented so that if a log is
- passed its TTL, the cleaning thread will lookup every queue until it
- finds the log (while caching the ones it finds). If it's not found,
- the log will be deleted. The next time it has to look for a log,
- it will first use its cache.
- </p>
- </section>
- <section name="Region server failover">
- <p>
- As long as region servers don't fail, keeping track of the logs in ZK
- doesn't add any value. Unfortunately, they do fail, so since ZooKeeper
- is highly available we can count on it and its semantics to help us
- managing the transfer of the queues.
- </p>
- <p>
- All the master cluster RSs keep a watcher on every other one of them to be
- notified when one dies (just like the master does). When it happens,
- they all race to create a znode called "lock" inside the dead RS' znode
- that contains its queues. The one that creates it successfully will
- proceed by transferring all the queues to its own znode (one by one
- since ZK doesn't support the rename operation) and will delete all the
- old ones when it's done. The recovered queues' znodes will be named
- with the id of the slave cluster appended with the name of the dead
- server.
- </p>
- <p>
- Once that is done, the master cluster RS will create one new source thread per
- copied queue, and each of them will follow the read/filter/ship pattern.
- The main difference is that those queues will never have new data since
- they don't belong to their new region server, which means that when
- the reader hits the end of the last log, the queue's znode will be
- deleted and the master cluster RS will close that replication source.
- </p>
- <p>
- For example, consider a master cluster with 3 region servers that's
- replicating to a single slave with id '2'. The following hierarchy
- represents what the znodes layout could be at some point in time. We
- can see the RSs' znodes all contain a "peers" znode that contains a
- single queue. The znode names in the queues represent the actual file
- names on HDFS in the form "address,port.timestamp".
- </p>
- <pre>
-/hbase/replication/rs/
- 1.1.1.1,60020,123456780/
- 2/
- 1.1.1.1,60020.1234 (Contains a position)
- 1.1.1.1,60020.1265
- 1.1.1.2,60020,123456790/
- 2/
- 1.1.1.2,60020.1214 (Contains a position)
- 1.1.1.2,60020.1248
- 1.1.1.2,60020.1312
- 1.1.1.3,60020, 123456630/
- 2/
- 1.1.1.3,60020.1280 (Contains a position)
- </pre>
- <p>
- Now let's say that 1.1.1.2 loses its ZK session. The survivors will race
- to create a lock, and for some reasons 1.1.1.3 wins. It will then start
- transferring all the queues to its local peers znode by appending the
- name of the dead server. Right before 1.1.1.3 is able to clean up the
- old znodes, the layout will look like the following:
- </p>
- <pre>
-/hbase/replication/rs/
- 1.1.1.1,60020,123456780/
- 2/
- 1.1.1.1,60020.1234 (Contains a position)
- 1.1.1.1,60020.1265
- 1.1.1.2,60020,123456790/
- lock
- 2/
- 1.1.1.2,60020.1214 (Contains a position)
- 1.1.1.2,60020.1248
- 1.1.1.2,60020.1312
- 1.1.1.3,60020,123456630/
- 2/
- 1.1.1.3,60020.1280 (Contains a position)
-
- 2-1.1.1.2,60020,123456790/
- 1.1.1.2,60020.1214 (Contains a position)
- 1.1.1.2,60020.1248
- 1.1.1.2,60020.1312
- </pre>
- <p>
- Some time later, but before 1.1.1.3 is able to finish replicating the
- last HLog from 1.1.1.2, let's say that it dies too (also some new logs
- were created in the normal queues). The last RS will then try to lock
- 1.1.1.3's znode and will begin transferring all the queues. The new
- layout will be:
- </p>
- <pre>
-/hbase/replication/rs/
- 1.1.1.1,60020,123456780/
- 2/
- 1.1.1.1,60020.1378 (Contains a position)
-
- 2-1.1.1.3,60020,123456630/
- 1.1.1.3,60020.1325 (Contains a position)
- 1.1.1.3,60020.1401
-
- 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
- 1.1.1.2,60020.1312 (Contains a position)
- 1.1.1.3,60020,123456630/
- lock
- 2/
- 1.1.1.3,60020.1325 (Contains a position)
- 1.1.1.3,60020.1401
-
- 2-1.1.1.2,60020,123456790/
- 1.1.1.2,60020.1312 (Contains a position)
- </pre>
- </section>
- </section>
- <section name="Replication Metrics">
- Following the some useful metrics which can be used to check the replication progress:
- <ul>
- <li><b>source.sizeOfLogQueue:</b> number of HLogs to process (excludes the one which is being
- processed) at the Replication source</li>
- <li><b>source.shippedOps:</b> number of mutations shipped</li>
- <li><b>source.logEditsRead:</b> number of mutations read from HLogs at the replication source</li>
- <li><b>source.ageOfLastShippedOp:</b> age of last batch that was shipped by the replication source</li>
- </ul>
- Please note that the above metrics are at the global level at this regionserver. In 0.95.0 and onwards, these
- metrics are also exposed per peer level.
- </section>
-
- <section name="FAQ">
- <section name="GLOBAL means replicate? Any provision to replicate only to cluster X and not to cluster Y? or is that for later?">
- <p>
- Yes, this is for much later.
- </p>
- </section>
- <section name="You need a bulk edit shipper? Something that allows you transfer 64MB of edits in one go?">
- <p>
- You can use the HBase-provided utility called CopyTable from the package
- org.apache.hadoop.hbase.mapreduce in order to have a discp-like tool to
- bulk copy data.
- </p>
- </section>
- <section name="Is it a mistake that WALEdit doesn't carry Put and Delete objects, that we have to reinstantiate not only when replicating but when replaying edits also?">
- <p>
- Yes, this behavior would help a lot but it's not currently available
- in HBase (BatchUpdate had that, but it was lost in the new API).
- </p>
- </section>
- <section name="Is there an issue replicating on Hadoop 1.0/1.1 when short-circuit reads are enabled?">
- <p>
- Yes. See <a href="https://issues.apache.org/jira/browse/HDFS-2757">HDFS-2757</a>.
- </p>
- </section>
- </section>
+ <p>This information has been moved to <a href="http://hbase.apache.org/book.html#cluster_replication">the Cluster Replication</a> section of the <a href="http://hbase.apache.org/book.html">Apache HBase Reference Guide</a>.</p>
</body>
</document>
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/xslt/configuration_to_asciidoc_chapter.xsl
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diff --git a/src/main/xslt/configuration_to_asciidoc_chapter.xsl b/src/main/xslt/configuration_to_asciidoc_chapter.xsl
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+<?xml version="1.0"?>
+<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
+
+
+<!--
+/**
+ * Copyright 2010 The Apache Software Foundation
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+This stylesheet is used making an html version of hbase-default.adoc.
+-->
+
+<xsl:output method="text"/>
+
+<!-- Normalize space -->
+<xsl:template match="text()">
+ <xsl:if test="normalize-space(.)">
+ <xsl:value-of select="normalize-space(.)"/>
+ </xsl:if>
+</xsl:template>
+
+<!-- Grab nodes of the <configuration> element -->
+<xsl:template match="configuration">
+
+<!-- Print the license at the top of the file -->
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[hbase_default_configurations]]
+=== HBase Default Configuration
+
+The documentation below is generated using the default hbase configuration file, _hbase-default.xml_, as source.
+
+ <xsl:for-each select="property">
+ <xsl:if test="not(@skipInDoc)">
+[[<xsl:apply-templates select="name"/>]]
+`<xsl:apply-templates select="name"/>`::
++
+.Description
+<xsl:apply-templates select="description"/>
++
+.Default
+<xsl:choose>
+ <xsl:when test="value != ''">`<xsl:apply-templates select="value"/>`
+
+</xsl:when>
+ <xsl:otherwise>none</xsl:otherwise>
+</xsl:choose>
+ </xsl:if>
+ </xsl:for-each>
+
+</xsl:template>
+</xsl:stylesheet>
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/xslt/configuration_to_docbook_section.xsl
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diff --git a/src/main/xslt/configuration_to_docbook_section.xsl b/src/main/xslt/configuration_to_docbook_section.xsl
deleted file mode 100644
index 57219ab..0000000
--- a/src/main/xslt/configuration_to_docbook_section.xsl
+++ /dev/null
@@ -1,66 +0,0 @@
-<?xml version="1.0"?>
-<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
-<xsl:output method="xml"/>
-<xsl:template match="configuration">
-<!--
-/**
- * Copyright 2010 The Apache Software Foundation
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-This stylesheet is used making an html version of hbase-default.xml.
--->
-<glossary xml:id="hbase_default_configurations"
-version="5.0" xmlns="http://docbook.org/ns/docbook"
- xmlns:xlink="http://www.w3.org/1999/xlink"
- xmlns:xi="http://www.w3.org/2001/XInclude"
- xmlns:svg="http://www.w3.org/2000/svg"
- xmlns:m="http://www.w3.org/1998/Math/MathML"
- xmlns:html="http://www.w3.org/1999/xhtml"
- xmlns:db="http://docbook.org/ns/docbook">
-<title>HBase Default Configuration</title>
-
-<para>
-The documentation below is generated using the default hbase configuration file,
-<filename>hbase-default.xml</filename>, as source.
-</para>
-
-<xsl:for-each select="property">
-<xsl:if test="not(@skipInDoc)">
-<glossentry>
- <xsl:attribute name="xml:id">
- <xsl:value-of select="name" />
- </xsl:attribute>
- <glossterm>
- <varname><xsl:value-of select="name"/></varname>
- </glossterm>
- <glossdef>
- <para><xsl:value-of select="description"/></para>
- <formalpara>
- <title>Default</title>
- <para><varname><xsl:value-of select="value"/></varname></para>
- </formalpara>
- </glossdef>
-</glossentry>
-</xsl:if>
-</xsl:for-each>
-
-</glossary>
-
-</xsl:template>
-</xsl:stylesheet>
[10/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/mapreduce.adoc
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diff --git a/src/main/asciidoc/_chapters/mapreduce.adoc b/src/main/asciidoc/_chapters/mapreduce.adoc
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+++ b/src/main/asciidoc/_chapters/mapreduce.adoc
@@ -0,0 +1,596 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[mapreduce]]
+= HBase and MapReduce
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+Apache MapReduce is a software framework used to analyze large amounts of data, and is the framework used most often with link:http://hadoop.apache.org/[Apache Hadoop].
+MapReduce itself is out of the scope of this document.
+A good place to get started with MapReduce is http://hadoop.apache.org/docs/r2.6.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html.
+MapReduce version 2 (MR2)is now part of link:http://hadoop.apache.org/docs/r2.3.0/hadoop-yarn/hadoop-yarn-site/[YARN].
+
+This chapter discusses specific configuration steps you need to take to use MapReduce on data within HBase.
+In addition, it discusses other interactions and issues between HBase and MapReduce jobs.
+
+.`mapred` and `mapreduce`
+[NOTE]
+====
+There are two mapreduce packages in HBase as in MapReduce itself: _org.apache.hadoop.hbase.mapred_ and _org.apache.hadoop.hbase.mapreduce_.
+The former does old-style API and the latter the new style.
+The latter has more facility though you can usually find an equivalent in the older package.
+Pick the package that goes with your MapReduce deploy.
+When in doubt or starting over, pick the _org.apache.hadoop.hbase.mapreduce_.
+In the notes below, we refer to o.a.h.h.mapreduce but replace with the o.a.h.h.mapred if that is what you are using.
+====
+
+[[hbase.mapreduce.classpath]]
+== HBase, MapReduce, and the CLASSPATH
+
+By default, MapReduce jobs deployed to a MapReduce cluster do not have access to either the HBase configuration under `$HBASE_CONF_DIR` or the HBase classes.
+
+To give the MapReduce jobs the access they need, you could add _hbase-site.xml_ to _$HADOOP_HOME/conf_ and add HBase jars to the _$HADOOP_HOME/lib_ directory.
+You would then need to copy these changes across your cluster. Or you can edit _$HADOOP_HOME/conf/hadoop-env.sh_ and add them to the `HADOOP_CLASSPATH` variable.
+However, this approach is not recommended because it will pollute your Hadoop install with HBase references.
+It also requires you to restart the Hadoop cluster before Hadoop can use the HBase data.
+
+The recommended approach is to let HBase add its dependency jars itself and use `HADOOP_CLASSPATH` or `-libjars`.
+
+Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself.
+The dependencies only need to be available on the local `CLASSPATH`.
+The following example runs the bundled HBase link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html[RowCounter] MapReduce job against a table named `usertable`.
+If you have not set the environment variables expected in the command (the parts prefixed by a `$` sign and surrounded by curly braces), you can use the actual system paths instead.
+Be sure to use the correct version of the HBase JAR for your system.
+The backticks (``` symbols) cause ths shell to execute the sub-commands, setting the output of `hbase classpath` (the command to dump HBase CLASSPATH) to `HADOOP_CLASSPATH`.
+This example assumes you use a BASH-compatible shell.
+
+[source,bash]
+----
+$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-server-VERSION.jar rowcounter usertable
+----
+
+When the command runs, internally, the HBase JAR finds the dependencies it needs for ZooKeeper, Guava, and its other dependencies on the passed `HADOOP_CLASSPATH` and adds the JARs to the MapReduce job configuration.
+See the source at `TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job)` for how this is done.
+
+The command `hbase mapredcp` can also help you dump the CLASSPATH entries required by MapReduce, which are the same jars `TableMapReduceUtil#addDependencyJars` would add.
+You can add them together with HBase conf directory to `HADOOP_CLASSPATH`.
+For jobs that do not package their dependencies or call `TableMapReduceUtil#addDependencyJars`, the following command structure is necessary:
+
+[source,bash]
+----
+$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase mapredcp`:${HBASE_HOME}/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(${HBASE_HOME}/bin/hbase mapredcp | tr ':' ',') ...
+----
+
+[NOTE]
+====
+The example may not work if you are running HBase from its build directory rather than an installed location.
+You may see an error like the following:
+
+----
+java.lang.RuntimeException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper
+----
+
+If this occurs, try modifying the command as follows, so that it uses the HBase JARs from the _target/_ directory within the build environment.
+
+[source,bash]
+----
+$ HADOOP_CLASSPATH=${HBASE_BUILD_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar:`${HBASE_BUILD_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_BUILD_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar rowcounter usertable
+----
+====
+
+.Notice to MapReduce users of HBase between 0.96.1 and 0.98.4
+[CAUTION]
+====
+Some MapReduce jobs that use HBase fail to launch.
+The symptom is an exception similar to the following:
+
+----
+Exception in thread "main" java.lang.IllegalAccessError: class
+ com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass
+ com.google.protobuf.LiteralByteString
+ at java.lang.ClassLoader.defineClass1(Native Method)
+ at java.lang.ClassLoader.defineClass(ClassLoader.java:792)
+ at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
+ at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
+ at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
+ at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
+ at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
+ at java.security.AccessController.doPrivileged(Native Method)
+ at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
+ at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
+ at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
+ at
+ org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:818)
+ at
+ org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToString(TableMapReduceUtil.java:433)
+ at
+ org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:186)
+ at
+ org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:147)
+ at
+ org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:270)
+ at
+ org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:100)
+...
+----
+
+This is caused by an optimization introduced in link:https://issues.apache.org/jira/browse/HBASE-9867[HBASE-9867] that inadvertently introduced a classloader dependency.
+
+This affects both jobs using the `-libjars` option and "fat jar," those which package their runtime dependencies in a nested `lib` folder.
+
+In order to satisfy the new classloader requirements, `hbase-protocol.jar` must be included in Hadoop's classpath.
+See <<hbase.mapreduce.classpath>> for current recommendations for resolving classpath errors.
+The following is included for historical purposes.
+
+This can be resolved system-wide by including a reference to the `hbase-protocol.jar` in Hadoop's lib directory, via a symlink or by copying the jar into the new location.
+
+This can also be achieved on a per-job launch basis by including it in the `HADOOP_CLASSPATH` environment variable at job submission time.
+When launching jobs that package their dependencies, all three of the following job launching commands satisfy this requirement:
+
+[source,bash]
+----
+$ HADOOP_CLASSPATH=/path/to/hbase-protocol.jar:/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass
+$ HADOOP_CLASSPATH=$(hbase mapredcp):/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass
+$ HADOOP_CLASSPATH=$(hbase classpath) hadoop jar MyJob.jar MyJobMainClass
+----
+
+For jars that do not package their dependencies, the following command structure is necessary:
+
+[source,bash]
+----
+$ HADOOP_CLASSPATH=$(hbase mapredcp):/etc/hbase/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(hbase mapredcp | tr ':' ',') ...
+----
+
+See also link:https://issues.apache.org/jira/browse/HBASE-10304[HBASE-10304] for further discussion of this issue.
+====
+
+== MapReduce Scan Caching
+
+TableMapReduceUtil now restores the option to set scanner caching (the number of rows which are cached before returning the result to the client) on the Scan object that is passed in.
+This functionality was lost due to a bug in HBase 0.95 (link:https://issues.apache.org/jira/browse/HBASE-11558[HBASE-11558]), which is fixed for HBase 0.98.5 and 0.96.3.
+The priority order for choosing the scanner caching is as follows:
+
+. Caching settings which are set on the scan object.
+. Caching settings which are specified via the configuration option `hbase.client.scanner.caching`, which can either be set manually in _hbase-site.xml_ or via the helper method `TableMapReduceUtil.setScannerCaching()`.
+. The default value `HConstants.DEFAULT_HBASE_CLIENT_SCANNER_CACHING`, which is set to `100`.
+
+Optimizing the caching settings is a balance between the time the client waits for a result and the number of sets of results the client needs to receive.
+If the caching setting is too large, the client could end up waiting for a long time or the request could even time out.
+If the setting is too small, the scan needs to return results in several pieces.
+If you think of the scan as a shovel, a bigger cache setting is analogous to a bigger shovel, and a smaller cache setting is equivalent to more shoveling in order to fill the bucket.
+
+The list of priorities mentioned above allows you to set a reasonable default, and override it for specific operations.
+
+See the API documentation for link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] for more details.
+
+== Bundled HBase MapReduce Jobs
+
+The HBase JAR also serves as a Driver for some bundled MapReduce jobs.
+To learn about the bundled MapReduce jobs, run the following command.
+
+[source,bash]
+----
+$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar
+An example program must be given as the first argument.
+Valid program names are:
+ copytable: Export a table from local cluster to peer cluster
+ completebulkload: Complete a bulk data load.
+ export: Write table data to HDFS.
+ import: Import data written by Export.
+ importtsv: Import data in TSV format.
+ rowcounter: Count rows in HBase table
+----
+
+Each of the valid program names are bundled MapReduce jobs.
+To run one of the jobs, model your command after the following example.
+
+[source,bash]
+----
+$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter myTable
+----
+
+== HBase as a MapReduce Job Data Source and Data Sink
+
+HBase can be used as a data source, link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html[TableInputFormat], and data sink, link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableOutputFormat.html[TableOutputFormat] or link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/MultiTableOutputFormat.html[MultiTableOutputFormat], for MapReduce jobs.
+Writing MapReduce jobs that read or write HBase, it is advisable to subclass link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableMapper.html[TableMapper] and/or link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableReducer.html[TableReducer].
+See the do-nothing pass-through classes link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/IdentityTableMapper.html[IdentityTableMapper] and link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/IdentityTableReducer.html[IdentityTableReducer] for basic usage.
+For a more involved example, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html[RowCounter] or review the `org.apache.hadoop.hbase.mapreduce.TestTableMapReduce` unit test.
+
+If you run MapReduce jobs that use HBase as source or sink, need to specify source and sink table and column names in your configuration.
+
+When you read from HBase, the `TableInputFormat` requests the list of regions from HBase and makes a map, which is either a `map-per-region` or `mapreduce.job.maps` map, whichever is smaller.
+If your job only has two maps, raise `mapreduce.job.maps` to a number greater than the number of regions.
+Maps will run on the adjacent TaskTracker/NodeManager if you are running a TaskTracer/NodeManager and RegionServer per node.
+When writing to HBase, it may make sense to avoid the Reduce step and write back into HBase from within your map.
+This approach works when your job does not need the sort and collation that MapReduce does on the map-emitted data.
+On insert, HBase 'sorts' so there is no point double-sorting (and shuffling data around your MapReduce cluster) unless you need to.
+If you do not need the Reduce, your map might emit counts of records processed for reporting at the end of the job, or set the number of Reduces to zero and use TableOutputFormat.
+If running the Reduce step makes sense in your case, you should typically use multiple reducers so that load is spread across the HBase cluster.
+
+A new HBase partitioner, the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/HRegionPartitioner.html[HRegionPartitioner], can run as many reducers the number of existing regions.
+The HRegionPartitioner is suitable when your table is large and your upload will not greatly alter the number of existing regions upon completion.
+Otherwise use the default partitioner.
+
+== Writing HFiles Directly During Bulk Import
+
+If you are importing into a new table, you can bypass the HBase API and write your content directly to the filesystem, formatted into HBase data files (HFiles). Your import will run faster, perhaps an order of magnitude faster.
+For more on how this mechanism works, see <<arch.bulk.load>>.
+
+== RowCounter Example
+
+The included link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html[RowCounter] MapReduce job uses `TableInputFormat` and does a count of all rows in the specified table.
+To run it, use the following command:
+
+[source,bash]
+----
+$ ./bin/hadoop jar hbase-X.X.X.jar
+----
+
+This will invoke the HBase MapReduce Driver class.
+Select `rowcounter` from the choice of jobs offered.
+This will print rowcounter usage advice to standard output.
+Specify the tablename, column to count, and output directory.
+If you have classpath errors, see <<hbase.mapreduce.classpath>>.
+
+[[splitter]]
+== Map-Task Splitting
+
+[[splitter.default]]
+=== The Default HBase MapReduce Splitter
+
+When link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html[TableInputFormat] is used to source an HBase table in a MapReduce job, its splitter will make a map task for each region of the table.
+Thus, if there are 100 regions in the table, there will be 100 map-tasks for the job - regardless of how many column families are selected in the Scan.
+
+[[splitter.custom]]
+=== Custom Splitters
+
+For those interested in implementing custom splitters, see the method `getSplits` in link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormatBase.html[TableInputFormatBase].
+That is where the logic for map-task assignment resides.
+
+[[mapreduce.example]]
+== HBase MapReduce Examples
+
+[[mapreduce.example.read]]
+=== HBase MapReduce Read Example
+
+The following is an example of using HBase as a MapReduce source in read-only manner.
+Specifically, there is a Mapper instance but no Reducer, and nothing is being emitted from the Mapper.
+There job would be defined as follows...
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config, "ExampleRead");
+job.setJarByClass(MyReadJob.class); // class that contains mapper
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+...
+
+TableMapReduceUtil.initTableMapperJob(
+ tableName, // input HBase table name
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper
+ null, // mapper output key
+ null, // mapper output value
+ job);
+job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+----
+
+...and the mapper instance would extend link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableMapper.html[TableMapper]...
+
+[source,java]
+----
+public static class MyMapper extends TableMapper<Text, Text> {
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException {
+ // process data for the row from the Result instance.
+ }
+}
+----
+
+[[mapreduce.example.readwrite]]
+=== HBase MapReduce Read/Write Example
+
+The following is an example of using HBase both as a source and as a sink with MapReduce.
+This example will simply copy data from one table to another.
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleReadWrite");
+job.setJarByClass(MyReadWriteJob.class); // class that contains mapper
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ null, // mapper output key
+ null, // mapper output value
+ job);
+TableMapReduceUtil.initTableReducerJob(
+ targetTable, // output table
+ null, // reducer class
+ job);
+job.setNumReduceTasks(0);
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+----
+
+An explanation is required of what `TableMapReduceUtil` is doing, especially with the reducer. link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableOutputFormat.html[TableOutputFormat] is being used as the outputFormat class, and several parameters are being set on the config (e.g., `TableOutputFormat.OUTPUT_TABLE`), as well as setting the reducer output key to `ImmutableBytesWritable` and reducer value to `Writable`.
+These could be set by the programmer on the job and conf, but `TableMapReduceUtil` tries to make things easier.
+
+The following is the example mapper, which will create a `Put` and matching the input `Result` and emit it.
+Note: this is what the CopyTable utility does.
+
+[source,java]
+----
+public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> {
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ // this example is just copying the data from the source table...
+ context.write(row, resultToPut(row,value));
+ }
+
+ private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
+ Put put = new Put(key.get());
+ for (KeyValue kv : result.raw()) {
+ put.add(kv);
+ }
+ return put;
+ }
+}
+----
+
+There isn't actually a reducer step, so `TableOutputFormat` takes care of sending the `Put` to the target table.
+
+This is just an example, developers could choose not to use `TableOutputFormat` and connect to the target table themselves.
+
+[[mapreduce.example.readwrite.multi]]
+=== HBase MapReduce Read/Write Example With Multi-Table Output
+
+TODO: example for `MultiTableOutputFormat`.
+
+[[mapreduce.example.summary]]
+=== HBase MapReduce Summary to HBase Example
+
+The following example uses HBase as a MapReduce source and sink with a summarization step.
+This example will count the number of distinct instances of a value in a table and write those summarized counts in another table.
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleSummary");
+job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ Text.class, // mapper output key
+ IntWritable.class, // mapper output value
+ job);
+TableMapReduceUtil.initTableReducerJob(
+ targetTable, // output table
+ MyTableReducer.class, // reducer class
+ job);
+job.setNumReduceTasks(1); // at least one, adjust as required
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+----
+
+In this example mapper a column with a String-value is chosen as the value to summarize upon.
+This value is used as the key to emit from the mapper, and an `IntWritable` represents an instance counter.
+
+[source,java]
+----
+public static class MyMapper extends TableMapper<Text, IntWritable> {
+ public static final byte[] CF = "cf".getBytes();
+ public static final byte[] ATTR1 = "attr1".getBytes();
+
+ private final IntWritable ONE = new IntWritable(1);
+ private Text text = new Text();
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ String val = new String(value.getValue(CF, ATTR1));
+ text.set(val); // we can only emit Writables...
+ context.write(text, ONE);
+ }
+}
+----
+
+In the reducer, the "ones" are counted (just like any other MR example that does this), and then emits a `Put`.
+
+[source,java]
+----
+public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> {
+ public static final byte[] CF = "cf".getBytes();
+ public static final byte[] COUNT = "count".getBytes();
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ int i = 0;
+ for (IntWritable val : values) {
+ i += val.get();
+ }
+ Put put = new Put(Bytes.toBytes(key.toString()));
+ put.add(CF, COUNT, Bytes.toBytes(i));
+
+ context.write(null, put);
+ }
+}
+----
+
+[[mapreduce.example.summary.file]]
+=== HBase MapReduce Summary to File Example
+
+This very similar to the summary example above, with exception that this is using HBase as a MapReduce source but HDFS as the sink.
+The differences are in the job setup and in the reducer.
+The mapper remains the same.
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleSummaryToFile");
+job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and reducer
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ Text.class, // mapper output key
+ IntWritable.class, // mapper output value
+ job);
+job.setReducerClass(MyReducer.class); // reducer class
+job.setNumReduceTasks(1); // at least one, adjust as required
+FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust directories as required
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+----
+
+As stated above, the previous Mapper can run unchanged with this example.
+As for the Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting Puts.
+
+[source,java]
+----
+public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ int i = 0;
+ for (IntWritable val : values) {
+ i += val.get();
+ }
+ context.write(key, new IntWritable(i));
+ }
+}
+----
+
+[[mapreduce.example.summary.noreducer]]
+=== HBase MapReduce Summary to HBase Without Reducer
+
+It is also possible to perform summaries without a reducer - if you use HBase as the reducer.
+
+An HBase target table would need to exist for the job summary.
+The Table method `incrementColumnValue` would be used to atomically increment values.
+From a performance perspective, it might make sense to keep a Map of values with their values to be incremented for each map-task, and make one update per key at during the `cleanup` method of the mapper.
+However, your mileage may vary depending on the number of rows to be processed and unique keys.
+
+In the end, the summary results are in HBase.
+
+[[mapreduce.example.summary.rdbms]]
+=== HBase MapReduce Summary to RDBMS
+
+Sometimes it is more appropriate to generate summaries to an RDBMS.
+For these cases, it is possible to generate summaries directly to an RDBMS via a custom reducer.
+The `setup` method can connect to an RDBMS (the connection information can be passed via custom parameters in the context) and the cleanup method can close the connection.
+
+It is critical to understand that number of reducers for the job affects the summarization implementation, and you'll have to design this into your reducer.
+Specifically, whether it is designed to run as a singleton (one reducer) or multiple reducers.
+Neither is right or wrong, it depends on your use-case.
+Recognize that the more reducers that are assigned to the job, the more simultaneous connections to the RDBMS will be created - this will scale, but only to a point.
+
+[source,java]
+----
+public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
+
+ private Connection c = null;
+
+ public void setup(Context context) {
+ // create DB connection...
+ }
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ // do summarization
+ // in this example the keys are Text, but this is just an example
+ }
+
+ public void cleanup(Context context) {
+ // close db connection
+ }
+
+}
+----
+
+In the end, the summary results are written to your RDBMS table/s.
+
+[[mapreduce.htable.access]]
+== Accessing Other HBase Tables in a MapReduce Job
+
+Although the framework currently allows one HBase table as input to a MapReduce job, other HBase tables can be accessed as lookup tables, etc., in a MapReduce job via creating an Table instance in the setup method of the Mapper.
+[source,java]
+----
+public class MyMapper extends TableMapper<Text, LongWritable> {
+ private Table myOtherTable;
+
+ public void setup(Context context) {
+ // In here create a Connection to the cluster and save it or use the Connection
+ // from the existing table
+ myOtherTable = connection.getTable("myOtherTable");
+ }
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ // process Result...
+ // use 'myOtherTable' for lookups
+ }
+----
+
+[[mapreduce.specex]]
+== Speculative Execution
+
+It is generally advisable to turn off speculative execution for MapReduce jobs that use HBase as a source.
+This can either be done on a per-Job basis through properties, on on the entire cluster.
+Especially for longer running jobs, speculative execution will create duplicate map-tasks which will double-write your data to HBase; this is probably not what you want.
+
+See <<spec.ex,spec.ex>> for more information.
[14/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/datamodel.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/datamodel.adoc b/src/main/asciidoc/_chapters/datamodel.adoc
new file mode 100644
index 0000000..b76adc8
--- /dev/null
+++ b/src/main/asciidoc/_chapters/datamodel.adoc
@@ -0,0 +1,562 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[datamodel]]
+= Data Model
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+In HBase, data is stored in tables, which have rows and columns.
+This is a terminology overlap with relational databases (RDBMSs), but this is not a helpful analogy.
+Instead, it can be helpful to think of an HBase table as a multi-dimensional map.
+
+.HBase Data Model Terminology
+
+Table::
+ An HBase table consists of multiple rows.
+
+Row::
+ A row in HBase consists of a row key and one or more columns with values associated with them.
+ Rows are sorted alphabetically by the row key as they are stored.
+ For this reason, the design of the row key is very important.
+ The goal is to store data in such a way that related rows are near each other.
+ A common row key pattern is a website domain.
+ If your row keys are domains, you should probably store them in reverse (org.apache.www, org.apache.mail, org.apache.jira). This way, all of the Apache domains are near each other in the table, rather than being spread out based on the first letter of the subdomain.
+
+Column::
+ A column in HBase consists of a column family and a column qualifier, which are delimited by a `:` (colon) character.
+
+Column Family::
+ Column families physically colocate a set of columns and their values, often for performance reasons.
+ Each column family has a set of storage properties, such as whether its values should be cached in memory, how its data is compressed or its row keys are encoded, and others.
+ Each row in a table has the same column families, though a given row might not store anything in a given column family.
+
+Column Qualifier::
+ A column qualifier is added to a column family to provide the index for a given piece of data.
+ Given a column family `content`, a column qualifier might be `content:html`, and another might be `content:pdf`.
+ Though column families are fixed at table creation, column qualifiers are mutable and may differ greatly between rows.
+
+Cell::
+ A cell is a combination of row, column family, and column qualifier, and contains a value and a timestamp, which represents the value's version.
+
+Timestamp::
+ A timestamp is written alongside each value, and is the identifier for a given version of a value.
+ By default, the timestamp represents the time on the RegionServer when the data was written, but you can specify a different timestamp value when you put data into the cell.
+
+[[conceptual.view]]
+== Conceptual View
+
+You can read a very understandable explanation of the HBase data model in the blog post link:http://jimbojw.com/wiki/index.php?title=Understanding_Hbase_and_BigTable[Understanding HBase and BigTable] by Jim R. Wilson.
+Another good explanation is available in the PDF link:http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/9353-login1210_khurana.pdf[Introduction to Basic Schema Design] by Amandeep Khurana.
+
+It may help to read different perspectives to get a solid understanding of HBase schema design.
+The linked articles cover the same ground as the information in this section.
+
+The following example is a slightly modified form of the one on page 2 of the link:http://research.google.com/archive/bigtable.html[BigTable] paper.
+There is a table called `webtable` that contains two rows (`com.cnn.www` and `com.example.www`) and three column families named `contents`, `anchor`, and `people`.
+In this example, for the first row (`com.cnn.www`), `anchor` contains two columns (`anchor:cssnsi.com`, `anchor:my.look.ca`) and `contents` contains one column (`contents:html`). This example contains 5 versions of the row with the row key `com.cnn.www`, and one version of the row with the row key `com.example.www`.
+The `contents:html` column qualifier contains the entire HTML of a given website.
+Qualifiers of the `anchor` column family each contain the external site which links to the site represented by the row, along with the text it used in the anchor of its link.
+The `people` column family represents people associated with the site.
+
+.Column Names
+[NOTE]
+====
+By convention, a column name is made of its column family prefix and a _qualifier_.
+For example, the column _contents:html_ is made up of the column family `contents` and the `html` qualifier.
+The colon character (`:`) delimits the column family from the column family _qualifier_.
+====
+
+.Table `webtable`
+[cols="1,1,1,1,1", frame="all", options="header"]
+|===
+|Row Key |Time Stamp |ColumnFamily `contents` |ColumnFamily `anchor`|ColumnFamily `people`
+|"com.cnn.www" |t9 | |anchor:cnnsi.com = "CNN" |
+|"com.cnn.www" |t8 | |anchor:my.look.ca = "CNN.com" |
+|"com.cnn.www" |t6 | contents:html = "<html>..." | |
+|"com.cnn.www" |t5 | contents:html = "<html>..." | |
+|"com.cnn.www" |t3 | contents:html = "<html>..." | |
+|"com.example.www"| t5 | contents:html = "<html>..." | people:author = "John Doe"
+|===
+
+Cells in this table that appear to be empty do not take space, or in fact exist, in HBase.
+This is what makes HBase "sparse." A tabular view is not the only possible way to look at data in HBase, or even the most accurate.
+The following represents the same information as a multi-dimensional map.
+This is only a mock-up for illustrative purposes and may not be strictly accurate.
+
+[source,json]
+----
+{
+ "com.cnn.www": {
+ contents: {
+ t6: contents:html: "<html>..."
+ t5: contents:html: "<html>..."
+ t3: contents:html: "<html>..."
+ }
+ anchor: {
+ t9: anchor:cnnsi.com = "CNN"
+ t8: anchor:my.look.ca = "CNN.com"
+ }
+ people: {}
+ }
+ "com.example.www": {
+ contents: {
+ t5: contents:html: "<html>..."
+ }
+ anchor: {}
+ people: {
+ t5: people:author: "John Doe"
+ }
+ }
+}
+----
+
+[[physical.view]]
+== Physical View
+
+Although at a conceptual level tables may be viewed as a sparse set of rows, they are physically stored by column family.
+A new column qualifier (column_family:column_qualifier) can be added to an existing column family at any time.
+
+.ColumnFamily `anchor`
+[cols="1,1,1", frame="all", options="header"]
+|===
+|Row Key | Time Stamp |Column Family `anchor`
+|"com.cnn.www" |t9 |`anchor:cnnsi.com = "CNN"`
+|"com.cnn.www" |t8 |`anchor:my.look.ca = "CNN.com"`
+|===
+
+
+.ColumnFamily `contents`
+[cols="1,1,1", frame="all", options="header"]
+|===
+|Row Key |Time Stamp |ColumnFamily `contents:`
+|"com.cnn.www" |t6 |contents:html = "<html>..."
+|"com.cnn.www" |t5 |contents:html = "<html>..."
+|"com.cnn.www" |t3 |contents:html = "<html>..."
+|===
+
+
+The empty cells shown in the conceptual view are not stored at all.
+Thus a request for the value of the `contents:html` column at time stamp `t8` would return no value.
+Similarly, a request for an `anchor:my.look.ca` value at time stamp `t9` would return no value.
+However, if no timestamp is supplied, the most recent value for a particular column would be returned.
+Given multiple versions, the most recent is also the first one found, since timestamps are stored in descending order.
+Thus a request for the values of all columns in the row `com.cnn.www` if no timestamp is specified would be: the value of `contents:html` from timestamp `t6`, the value of `anchor:cnnsi.com` from timestamp `t9`, the value of `anchor:my.look.ca` from timestamp `t8`.
+
+For more information about the internals of how Apache HBase stores data, see <<regions.arch,regions.arch>>.
+
+== Namespace
+
+A namespace is a logical grouping of tables analogous to a database in relation database systems.
+This abstraction lays the groundwork for upcoming multi-tenancy related features:
+
+* Quota Management (link:https://issues.apache.org/jira/browse/HBASE-8410[HBASE-8410]) - Restrict the amount of resources (ie regions, tables) a namespace can consume.
+* Namespace Security Administration (link:https://issues.apache.org/jira/browse/HBASE-9206[HBASE-9206]) - Provide another level of security administration for tenants.
+* Region server groups (link:https://issues.apache.org/jira/browse/HBASE-6721[HBASE-6721]) - A namespace/table can be pinned onto a subset of RegionServers thus guaranteeing a course level of isolation.
+
+[[namespace_creation]]
+=== Namespace management
+
+A namespace can be created, removed or altered.
+Namespace membership is determined during table creation by specifying a fully-qualified table name of the form:
+
+[source,xml]
+----
+<table namespace>:<table qualifier>
+----
+
+.Examples
+====
+[source,bourne]
+----
+
+#Create a namespace
+create_namespace 'my_ns'
+----
+
+[source,bourne]
+----
+
+#create my_table in my_ns namespace
+create 'my_ns:my_table', 'fam'
+----
+
+[source,bourne]
+----
+
+#drop namespace
+drop_namespace 'my_ns'
+----
+
+[source,bourne]
+----
+
+#alter namespace
+alter_namespace 'my_ns', {METHOD => 'set', 'PROPERTY_NAME' => 'PROPERTY_VALUE'}
+----
+====
+
+[[namespace_special]]
+=== Predefined namespaces
+
+There are two predefined special namespaces:
+
+* hbase - system namespace, used to contain HBase internal tables
+* default - tables with no explicit specified namespace will automatically fall into this namespace
+
+.Examples
+====
+[source,bourne]
+----
+
+#namespace=foo and table qualifier=bar
+create 'foo:bar', 'fam'
+
+#namespace=default and table qualifier=bar
+create 'bar', 'fam'
+----
+====
+
+== Table
+
+Tables are declared up front at schema definition time.
+
+== Row
+
+Row keys are uninterpreted bytes.
+Rows are lexicographically sorted with the lowest order appearing first in a table.
+The empty byte array is used to denote both the start and end of a tables' namespace.
+
+[[columnfamily]]
+== Column Family
+
+Columns in Apache HBase are grouped into _column families_.
+All column members of a column family have the same prefix.
+For example, the columns _courses:history_ and _courses:math_ are both members of the _courses_ column family.
+The colon character (`:`) delimits the column family from the column family qualifier.
+The column family prefix must be composed of _printable_ characters.
+The qualifying tail, the column family _qualifier_, can be made of any arbitrary bytes.
+Column families must be declared up front at schema definition time whereas columns do not need to be defined at schema time but can be conjured on the fly while the table is up an running.
+
+Physically, all column family members are stored together on the filesystem.
+Because tunings and storage specifications are done at the column family level, it is advised that all column family members have the same general access pattern and size characteristics.
+
+== Cells
+
+A _{row, column, version}_ tuple exactly specifies a `cell` in HBase.
+Cell content is uninterpreted bytes
+
+== Data Model Operations
+
+The four primary data model operations are Get, Put, Scan, and Delete.
+Operations are applied via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table] instances.
+
+=== Get
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] returns attributes for a specified row.
+Gets are executed via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#get(org.apache.hadoop.hbase.client.Get)[Table.get].
+
+=== Put
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Put.html[Put] either adds new rows to a table (if the key is new) or can update existing rows (if the key already exists). Puts are executed via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#put(org.apache.hadoop.hbase.client.Put)[Table.put] (writeBuffer) or link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#batch(java.util.List, java.lang.Object[])[Table.batch] (non-writeBuffer).
+
+[[scan]]
+=== Scans
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] allow iteration over multiple rows for specified attributes.
+
+The following is an example of a Scan on a Table instance.
+Assume that a table is populated with rows with keys "row1", "row2", "row3", and then another set of rows with the keys "abc1", "abc2", and "abc3". The following example shows how to set a Scan instance to return the rows beginning with "row".
+
+[source,java]
+----
+
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+
+Table table = ... // instantiate a Table instance
+
+Scan scan = new Scan();
+scan.addColumn(CF, ATTR);
+scan.setRowPrefixFilter(Bytes.toBytes("row"));
+ResultScanner rs = table.getScanner(scan);
+try {
+ for (Result r = rs.next(); r != null; r = rs.next()) {
+ // process result...
+ }
+} finally {
+ rs.close(); // always close the ResultScanner!
+}
+----
+
+Note that generally the easiest way to specify a specific stop point for a scan is by using the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/InclusiveStopFilter.html[InclusiveStopFilter] class.
+
+=== Delete
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Delete.html[Delete] removes a row from a table.
+Deletes are executed via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#delete(org.apache.hadoop.hbase.client.Delete)[Table.delete].
+
+HBase does not modify data in place, and so deletes are handled by creating new markers called _tombstones_.
+These tombstones, along with the dead values, are cleaned up on major compactions.
+
+See <<version.delete,version.delete>> for more information on deleting versions of columns, and see <<compaction,compaction>> for more information on compactions.
+
+[[versions]]
+== Versions
+
+A _{row, column, version}_ tuple exactly specifies a `cell` in HBase.
+It's possible to have an unbounded number of cells where the row and column are the same but the cell address differs only in its version dimension.
+
+While rows and column keys are expressed as bytes, the version is specified using a long integer.
+Typically this long contains time instances such as those returned by `java.util.Date.getTime()` or `System.currentTimeMillis()`, that is: [quote]_the difference, measured in milliseconds, between the current time and midnight, January 1, 1970 UTC_.
+
+The HBase version dimension is stored in decreasing order, so that when reading from a store file, the most recent values are found first.
+
+There is a lot of confusion over the semantics of `cell` versions, in HBase.
+In particular:
+
+* If multiple writes to a cell have the same version, only the last written is fetchable.
+* It is OK to write cells in a non-increasing version order.
+
+Below we describe how the version dimension in HBase currently works.
+See link:https://issues.apache.org/jira/browse/HBASE-2406[HBASE-2406] for discussion of HBase versions. link:http://outerthought.org/blog/417-ot.html[Bending time in HBase] makes for a good read on the version, or time, dimension in HBase.
+It has more detail on versioning than is provided here.
+As of this writing, the limitation _Overwriting values at existing timestamps_ mentioned in the article no longer holds in HBase.
+This section is basically a synopsis of this article by Bruno Dumon.
+
+[[specify.number.of.versions]]
+=== Specifying the Number of Versions to Store
+
+The maximum number of versions to store for a given column is part of the column schema and is specified at table creation, or via an `alter` command, via `HColumnDescriptor.DEFAULT_VERSIONS`.
+Prior to HBase 0.96, the default number of versions kept was `3`, but in 0.96 and newer has been changed to `1`.
+
+.Modify the Maximum Number of Versions for a Column Family
+====
+This example uses HBase Shell to keep a maximum of 5 versions of all columns in column family `f1`.
+You could also use link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor].
+
+----
+hbase> alter ‘t1′, NAME => ‘f1′, VERSIONS => 5
+----
+====
+
+.Modify the Minimum Number of Versions for a Column Family
+====
+You can also specify the minimum number of versions to store per column family.
+By default, this is set to 0, which means the feature is disabled.
+The following example sets the minimum number of versions on all columns in column family `f1` to `2`, via HBase Shell.
+You could also use link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor].
+
+----
+hbase> alter ‘t1′, NAME => ‘f1′, MIN_VERSIONS => 2
+----
+====
+
+Starting with HBase 0.98.2, you can specify a global default for the maximum number of versions kept for all newly-created columns, by setting `hbase.column.max.version` in _hbase-site.xml_.
+See <<hbase.column.max.version,hbase.column.max.version>>.
+
+[[versions.ops]]
+=== Versions and HBase Operations
+
+In this section we look at the behavior of the version dimension for each of the core HBase operations.
+
+==== Get/Scan
+
+Gets are implemented on top of Scans.
+The below discussion of link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] applies equally to link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scans].
+
+By default, i.e. if you specify no explicit version, when doing a `get`, the cell whose version has the largest value is returned (which may or may not be the latest one written, see later). The default behavior can be modified in the following ways:
+
+* to return more than one version, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html#setMaxVersions()[Get.setMaxVersions()]
+* to return versions other than the latest, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html#setTimeRange(long,%20long)[Get.setTimeRange()]
++
+To retrieve the latest version that is less than or equal to a given value, thus giving the 'latest' state of the record at a certain point in time, just use a range from 0 to the desired version and set the max versions to 1.
+
+
+==== Default Get Example
+
+The following Get will only retrieve the current version of the row
+
+[source,java]
+----
+
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+Get get = new Get(Bytes.toBytes("row1"));
+Result r = table.get(get);
+byte[] b = r.getValue(CF, ATTR); // returns current version of value
+----
+
+==== Versioned Get Example
+
+The following Get will return the last 3 versions of the row.
+
+[source,java]
+----
+
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+Get get = new Get(Bytes.toBytes("row1"));
+get.setMaxVersions(3); // will return last 3 versions of row
+Result r = table.get(get);
+byte[] b = r.getValue(CF, ATTR); // returns current version of value
+List<KeyValue> kv = r.getColumn(CF, ATTR); // returns all versions of this column
+----
+
+==== Put
+
+Doing a put always creates a new version of a `cell`, at a certain timestamp.
+By default the system uses the server's `currentTimeMillis`, but you can specify the version (= the long integer) yourself, on a per-column level.
+This means you could assign a time in the past or the future, or use the long value for non-time purposes.
+
+To overwrite an existing value, do a put at exactly the same row, column, and version as that of the cell you want to overwrite.
+
+===== Implicit Version Example
+
+The following Put will be implicitly versioned by HBase with the current time.
+
+[source,java]
+----
+
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+Put put = new Put(Bytes.toBytes(row));
+put.add(CF, ATTR, Bytes.toBytes( data));
+table.put(put);
+----
+
+===== Explicit Version Example
+
+The following Put has the version timestamp explicitly set.
+
+[source,java]
+----
+
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+Put put = new Put( Bytes.toBytes(row));
+long explicitTimeInMs = 555; // just an example
+put.add(CF, ATTR, explicitTimeInMs, Bytes.toBytes(data));
+table.put(put);
+----
+
+Caution: the version timestamp is used internally by HBase for things like time-to-live calculations.
+It's usually best to avoid setting this timestamp yourself.
+Prefer using a separate timestamp attribute of the row, or have the timestamp as a part of the row key, or both.
+
+[[version.delete]]
+==== Delete
+
+There are three different types of internal delete markers.
+See Lars Hofhansl's blog for discussion of his attempt adding another, link:http://hadoop-hbase.blogspot.com/2012/01/scanning-in-hbase.html[Scanning in HBase: Prefix Delete Marker].
+
+* Delete: for a specific version of a column.
+* Delete column: for all versions of a column.
+* Delete family: for all columns of a particular ColumnFamily
+
+When deleting an entire row, HBase will internally create a tombstone for each ColumnFamily (i.e., not each individual column).
+
+Deletes work by creating _tombstone_ markers.
+For example, let's suppose we want to delete a row.
+For this you can specify a version, or else by default the `currentTimeMillis` is used.
+What this means is _delete all cells where the version is less than or equal to this version_.
+HBase never modifies data in place, so for example a delete will not immediately delete (or mark as deleted) the entries in the storage file that correspond to the delete condition.
+Rather, a so-called _tombstone_ is written, which will mask the deleted values.
+When HBase does a major compaction, the tombstones are processed to actually remove the dead values, together with the tombstones themselves.
+If the version you specified when deleting a row is larger than the version of any value in the row, then you can consider the complete row to be deleted.
+
+For an informative discussion on how deletes and versioning interact, see the thread link:http://comments.gmane.org/gmane.comp.java.hadoop.hbase.user/28421[Put w/timestamp -> Deleteall -> Put w/ timestamp fails] up on the user mailing list.
+
+Also see <<keyvalue,keyvalue>> for more information on the internal KeyValue format.
+
+Delete markers are purged during the next major compaction of the store, unless the `KEEP_DELETED_CELLS` option is set in the column family (See <<cf.keep.deleted>>).
+To keep the deletes for a configurable amount of time, you can set the delete TTL via the +hbase.hstore.time.to.purge.deletes+ property in _hbase-site.xml_.
+If `hbase.hstore.time.to.purge.deletes` is not set, or set to 0, all delete markers, including those with timestamps in the future, are purged during the next major compaction.
+Otherwise, a delete marker with a timestamp in the future is kept until the major compaction which occurs after the time represented by the marker's timestamp plus the value of `hbase.hstore.time.to.purge.deletes`, in milliseconds.
+
+NOTE: This behavior represents a fix for an unexpected change that was introduced in HBase 0.94, and was fixed in link:https://issues.apache.org/jira/browse/HBASE-10118[HBASE-10118].
+The change has been backported to HBase 0.94 and newer branches.
+
+=== Current Limitations
+
+==== Deletes mask Puts
+
+Deletes mask puts, even puts that happened after the delete was entered.
+See link:https://issues.apache.org/jira/browse/HBASE-2256[HBASE-2256].
+Remember that a delete writes a tombstone, which only disappears after then next major compaction has run.
+Suppose you do a delete of everything <= T.
+After this you do a new put with a timestamp <= T.
+This put, even if it happened after the delete, will be masked by the delete tombstone.
+Performing the put will not fail, but when you do a get you will notice the put did have no effect.
+It will start working again after the major compaction has run.
+These issues should not be a problem if you use always-increasing versions for new puts to a row.
+But they can occur even if you do not care about time: just do delete and put immediately after each other, and there is some chance they happen within the same millisecond.
+
+[[major.compactions.change.query.results]]
+==== Major compactions change query results
+
+_...create three cell versions at t1, t2 and t3, with a maximum-versions
+ setting of 2. So when getting all versions, only the values at t2 and t3 will be
+ returned. But if you delete the version at t2 or t3, the one at t1 will appear again.
+ Obviously, once a major compaction has run, such behavior will not be the case
+ anymore..._ (See _Garbage Collection_ in link:http://outerthought.org/blog/417-ot.html[Bending time in HBase].)
+
+[[dm.sort]]
+== Sort Order
+
+All data model operations HBase return data in sorted order.
+First by row, then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted in reverse, so newest records are returned first).
+
+[[dm.column.metadata]]
+== Column Metadata
+
+There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily.
+Thus, while HBase can support not only a wide number of columns per row, but a heterogeneous set of columns between rows as well, it is your responsibility to keep track of the column names.
+
+The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows.
+For more information about how HBase stores data internally, see <<keyvalue,keyvalue>>.
+
+== Joins
+
+Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn't, at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated in this chapter, the read data model operations in HBase are Get and Scan.
+
+However, that doesn't mean that equivalent join functionality can't be supported in your application, but you have to do it yourself.
+The two primary strategies are either denormalizing the data upon writing to HBase, or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS' demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs.
+hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn't a single answer that works for every use case.
+
+== ACID
+
+See link:http://hbase.apache.org/acid-semantics.html[ACID Semantics].
+Lars Hofhansl has also written a note on link:http://hadoop-hbase.blogspot.com/2012/03/acid-in-hbase.html[ACID in HBase].
+
+ifdef::backend-docbook[]
+[index]
+== Index
+// Generated automatically by the DocBook toolchain.
+endif::backend-docbook[]
[02/18] hbase git commit: HBASE-13665 Fix docs and site building on
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+/* Remove the comments around the @import statement below when using this as a custom stylesheet */
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\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/book.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/book.adoc b/src/main/asciidoc/book.adoc
new file mode 100644
index 0000000..b2bd151
--- /dev/null
+++ b/src/main/asciidoc/book.adoc
@@ -0,0 +1,94 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+= Apache HBase (TM) Reference Guide
+:Author: Apache HBase Team
+:Email: <hb...@lists.apache.org>
+:doctype: book
+:Version: {docVersion}
+:revnumber: {docVersion}
+// Logo for PDF -- doesn't render in HTML
+:title-logo: hbase_logo_with_orca.png
+:numbered:
+:toc: left
+:toclevels: 1
+:toc-title: Contents
+:sectanchors:
+:icons: font
+:iconsdir: icons
+:linkcss:
+:experimental:
+:source-language: java
+:leveloffset: 0
+
+// Logo for HTML -- doesn't render in PDF
+++++
+<div>
+ <a href="http://hbase.apache.org"><img src="images/hbase_logo_with_orca.png" alt="Apache HBase Logo" /></a>
+</div>
+++++
+
+// The directory is called _chapters because asciidoctor skips direct
+// processing of files found in directories starting with an _. This
+// prevents each chapter being built as its own book.
+
+include::_chapters/preface.adoc[]
+
+include::_chapters/getting_started.adoc[]
+include::_chapters/configuration.adoc[]
+include::_chapters/upgrading.adoc[]
+include::_chapters/shell.adoc[]
+include::_chapters/datamodel.adoc[]
+include::_chapters/schema_design.adoc[]
+include::_chapters/mapreduce.adoc[]
+include::_chapters/security.adoc[]
+include::_chapters/architecture.adoc[]
+include::_chapters/hbase_apis.adoc[]
+include::_chapters/external_apis.adoc[]
+include::_chapters/thrift_filter_language.adoc[]
+include::_chapters/cp.adoc[]
+include::_chapters/performance.adoc[]
+include::_chapters/troubleshooting.adoc[]
+include::_chapters/case_studies.adoc[]
+include::_chapters/ops_mgt.adoc[]
+include::_chapters/developer.adoc[]
+include::_chapters/unit_testing.adoc[]
+include::_chapters/zookeeper.adoc[]
+include::_chapters/community.adoc[]
+
+= Appendix
+
+include::_chapters/appendix_contributing_to_documentation.adoc[]
+include::_chapters/faq.adoc[]
+include::_chapters/hbck_in_depth.adoc[]
+include::_chapters/appendix_acl_matrix.adoc[]
+include::_chapters/compression.adoc[]
+include::_chapters/sql.adoc[]
+include::_chapters/ycsb.adoc[]
+include::_chapters/appendix_hfile_format.adoc[]
+include::_chapters/other_info.adoc[]
+include::_chapters/hbase_history.adoc[]
+include::_chapters/asf.adoc[]
+include::_chapters/orca.adoc[]
+include::_chapters/tracing.adoc[]
+include::_chapters/rpc.adoc[]
+
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/hbase.css
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/hbase.css b/src/main/asciidoc/hbase.css
new file mode 100644
index 0000000..68d418b
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+a[href^="http:"]:not(.bare):after,a[href^="https:"]:not(.bare):after{content:"(" attr(href) ")";display:inline-block;font-size:.875em;padding-left:.25em}
+abbr[title]:after{content:" (" attr(title) ")"}
+pre,blockquote,tr,img{page-break-inside:avoid}
+thead{display:table-header-group}
+img{max-width:100%!important}
+p,blockquote,dt,td.content{font-size:1em;orphans:3;widows:3}
+h2,h3,#toctitle,.sidebarblock>.content>.title{page-break-after:avoid}
+#toc,.sidebarblock,.exampleblock>.content{background:none!important}
+#toc{border-bottom:1px solid #ddddd8!important;padding-bottom:0!important}
+.sect1{padding-bottom:0!important}
+.sect1+.sect1{border:0!important}
+#header>h1:first-child{margin-top:1.25rem}
+body.book #header{text-align:center}
+body.book #header>h1:first-child{border:0!important;margin:2.5em 0 1em 0}
+body.book #header .details{border:0!important;display:block;padding:0!important}
+body.book #header .details span:first-child{margin-left:0!important}
+body.book #header .details br{display:block}
+body.book #header .details br+span:before{content:none!important}
+body.book #toc{border:0!important;text-align:left!important;padding:0!important;margin:0!important}
+body.book #toc,body.book #preamble,body.book h1.sect0,body.book .sect1>h2{page-break-before:always}
+.listingblock code[data-lang]:before{display:block}
+#footer{background:none!important;padding:0 .9375em}
+#footer-text{color:rgba(0,0,0,.6)!important;font-size:.9em}
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+.show-for-print{display:inherit!important}}
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+../site/resources/images
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http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/acid-semantics.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Apache HBase (TM) ACID Properties
+
+== About this Document
+
+Apache HBase (TM) is not an ACID compliant database. However, it does guarantee certain specific properties.
+
+This specification enumerates the ACID properties of HBase.
+
+== Definitions
+
+For the sake of common vocabulary, we define the following terms:
+Atomicity::
+ An operation is atomic if it either completes entirely or not at all.
+
+Consistency::
+ All actions cause the table to transition from one valid state directly to another (eg a row will not disappear during an update, etc).
+
+Isolation::
+ an operation is isolated if it appears to complete independently of any other concurrent transaction.
+
+Durability::
+ Any update that reports "successful" to the client will not be lost.
+
+Visibility::
+ An update is considered visible if any subsequent read will see the update as having been committed.
+
+
+The terms _must_ and _may_ are used as specified by link:[RFC 2119].
+
+In short, the word "must" implies that, if some case exists where the statement is not true, it is a bug. The word _may_ implies that, even if the guarantee is provided in a current release, users should not rely on it.
+
+== APIs to Consider
+- Read APIs
+* get
+* scan
+- Write APIs
+* put
+* batch put
+* delete
+- Combination (read-modify-write) APIs
+* incrementColumnValue
+* checkAndPut
+
+== Guarantees Provided
+
+.Atomicity
+. All mutations are atomic within a row. Any put will either wholely succeed or wholely fail.footnoteref[Puts will either wholely succeed or wholely fail, provided that they are actually sent to the RegionServer. If the writebuffer is used, Puts will not be sent until the writebuffer is filled or it is explicitly flushed.]
+.. An operation that returns a _success_ code has completely succeeded.
+.. An operation that returns a _failure_ code has completely failed.
+.. An operation that times out may have succeeded and may have failed. However, it will not have partially succeeded or failed.
+. This is true even if the mutation crosses multiple column families within a row.
+. APIs that mutate several rows will _not_ be atomic across the multiple rows. For example, a multiput that operates on rows 'a','b', and 'c' may return having mutated some but not all of the rows. In such cases, these APIs will return a list of success codes, each of which may be succeeded, failed, or timed out as described above.
+. The checkAndPut API happens atomically like the typical _compareAndSet (CAS)_ operation found in many hardware architectures.
+. The order of mutations is seen to happen in a well-defined order for each row, with no interleaving. For example, if one writer issues the mutation `a=1,b=1,c=1` and another writer issues the mutation `a=2,b=2,c=`, the row must either be `a=1,b=1,c=1` or `a=2,b=2,c=2` and must *not* be something like `a=1,b=2,c=1`. +
+NOTE:This is not true _across rows_ for multirow batch mutations.
+
+== Consistency and Isolation
+. All rows returned via any access API will consist of a complete row that existed at some point in the table's history.
+. This is true across column families - i.e a get of a full row that occurs concurrent with some mutations 1,2,3,4,5 will return a complete row that existed at some point in time between mutation i and i+1 for some i between 1 and 5.
+. The state of a row will only move forward through the history of edits to it.
+
+== Consistency of Scans
+A scan is *not* a consistent view of a table. Scans do *not* exhibit _snapshot isolation_.
+
+Rather, scans have the following properties:
+. Any row returned by the scan will be a consistent view (i.e. that version of the complete row existed at some point in time)footnoteref[consistency,A consistent view is not guaranteed intra-row scanning -- i.e. fetching a portion of a row in one RPC then going back to fetch another portion of the row in a subsequent RPC. Intra-row scanning happens when you set a limit on how many values to return per Scan#next (See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setBatch(int)"[Scan#setBatch(int)]).]
+. A scan will always reflect a view of the data _at least as new as_ the beginning of the scan. This satisfies the visibility guarantees enumerated below.
+.. For example, if client A writes data X and then communicates via a side channel to client B, any scans started by client B will contain data at least as new as X.
+.. A scan _must_ reflect all mutations committed prior to the construction of the scanner, and _may_ reflect some mutations committed subsequent to the construction of the scanner.
+.. Scans must include _all_ data written prior to the scan (except in the case where data is subsequently mutated, in which case it _may_ reflect the mutation)
+
+Those familiar with relational databases will recognize this isolation level as "read committed".
+
+NOTE: The guarantees listed above regarding scanner consistency are referring to "transaction commit time", not the "timestamp" field of each cell. That is to say, a scanner started at time _t_ may see edits with a timestamp value greater than _t_, if those edits were committed with a "forward dated" timestamp before the scanner was constructed.
+
+== Visibility
+
+. When a client receives a "success" response for any mutation, that mutation is immediately visible to both that client and any client with whom it later communicates through side channels.footnoteref[consistency]
+. A row must never exhibit so-called "time-travel" properties. That is to say, if a series of mutations moves a row sequentially through a series of states, any sequence of concurrent reads will return a subsequence of those states. +
+For example, if a row's cells are mutated using the `incrementColumnValue` API, a client must never see the value of any cell decrease. +
+This is true regardless of which read API is used to read back the mutation.
+. Any version of a cell that has been returned to a read operation is guaranteed to be durably stored.
+
+== Durability
+. All visible data is also durable data. That is to say, a read will never return data that has not been made durable on disk.footnoteref[durability,In the context of Apache HBase, _durably on disk_; implies an `hflush()` call on the transaction log. This does not actually imply an `fsync()` to magnetic media, but rather just that the data has been written to the OS cache on all replicas of the log. In the case of a full datacenter power loss, it is possible that the edits are not truly durable.]
+. Any operation that returns a "success" code (eg does not throw an exception) will be made durable.footnoteref[durability]
+. Any operation that returns a "failure" code will not be made durable (subject to the Atomicity guarantees above).
+. All reasonable failure scenarios will not affect any of the guarantees of this document.
+
+== Tunability
+
+All of the above guarantees must be possible within Apache HBase. For users who would like to trade off some guarantees for performance, HBase may offer several tuning options. For example:
+
+* Visibility may be tuned on a per-read basis to allow stale reads or time travel.
+* Durability may be tuned to only flush data to disk on a periodic basis.
+
+== More Information
+
+For more information, see the link:book.html#client[client architecture] and link:book.html#datamodel[data model] sections in the Apache HBase Reference Guide.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/bulk-loads.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+= Bulk Loads in Apache HBase (TM)
+
+This page has been retired. The contents have been moved to the link:book.html#arch.bulk.load[Bulk Loading] section in the Reference Guide.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/site/asciidoc/cygwin.adoc
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+////
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+////
+
+
+== Installing Apache HBase (TM) on Windows using Cygwin
+
+== Introduction
+
+link:http://hbase.apache.org[Apache HBase (TM)] is a distributed, column-oriented store, modeled after Google's link:http://research.google.com/archive/bigtable.html[BigTable]. Apache HBase is built on top of link:http://hadoop.apache.org[Hadoop] for its link:http://hadoop.apache.org/mapreduce[MapReduce] link:http://hadoop.apache.org/hdfs[distributed file system] implementations. All these projects are open-source and part of the link:http://www.apache.org[Apache Software Foundation].
+
+== Purpose
+
+This document explains the *intricacies* of running Apache HBase on Windows using Cygwin* as an all-in-one single-node installation for testing and development. The HBase link:http://hbase.apache.org/apidocs/overview-summary.html#overview_description[Overview] and link:book.html#getting_started[QuickStart] guides on the other hand go a long way in explaning how to setup link:http://hadoop.apache.org/hbase[HBase] in more complex deployment scenarios.
+
+== Installation
+
+For running Apache HBase on Windows, 3 technologies are required:
+* Java
+* Cygwin
+* SSH
+
+The following paragraphs detail the installation of each of the aforementioned technologies.
+
+=== Java
+
+HBase depends on the link:http://java.sun.com/javase/6/[Java Platform, Standard Edition, 6 Release]. So the target system has to be provided with at least the Java Runtime Environment (JRE); however if the system will also be used for development, the Jave Development Kit (JDK) is preferred. You can download the latest versions for both from link:http://java.sun.com/javase/downloads/index.jsp[Sun's download page]. Installation is a simple GUI wizard that guides you through the process.
+
+=== Cygwin
+
+Cygwin is probably the oddest technology in this solution stack. It provides a dynamic link library that emulates most of a *nix environment on Windows. On top of that a whole bunch of the most common *nix tools are supplied. Combined, the DLL with the tools form a very *nix-alike environment on Windows.
+
+For installation, Cygwin provides the link:http://cygwin.com/setup.exe[`setup.exe` utility] that tracks the versions of all installed components on the target system and provides the mechanism for installing or updating everything from the mirror sites of Cygwin.
+
+To support installation, the `setup.exe` utility uses 2 directories on the target system. The *Root* directory for Cygwin (defaults to _C:\cygwin)_ which will become _/_ within the eventual Cygwin installation; and the *Local Package* directory (e.g. _C:\cygsetup_ that is the cache where `setup.exe`stores the packages before they are installed. The cache must not be the same folder as the Cygwin root.
+
+Perform following steps to install Cygwin, which are elaboratly detailed in the link:http://cygwin.com/cygwin-ug-net/setup-net.html[2nd chapter] of the link:http://cygwin.com/cygwin-ug-net/cygwin-ug-net.html[Cygwin User's Guide].
+
+. Make sure you have `Administrator` privileges on the target system.
+. Choose and create you Root and *Local Package* directories. A good suggestion is to use `C:\cygwin\root` and `C:\cygwin\setup` folders.
+. Download the `setup.exe` utility and save it to the *Local Package* directory. Run the `setup.exe` utility.
+.. Choose the `Install from Internet` option.
+.. Choose your *Root* and *Local Package* folders.
+.. Select an appropriate mirror.
+.. Don't select any additional packages yet, as we only want to install Cygwin for now.
+.. Wait for download and install.
+.. Finish the installation.
+. Optionally, you can now also add a shortcut to your Start menu pointing to the `setup.exe` utility in the *Local Package *folder.
+. Add `CYGWIN_HOME` system-wide environment variable that points to your *Root* directory.
+. Add `%CYGWIN_HOME%\bin` to the end of your `PATH` environment variable.
+. Reboot the sytem after making changes to the environment variables otherwise the OS will not be able to find the Cygwin utilities.
+. Test your installation by running your freshly created shortcuts or the `Cygwin.bat` command in the *Root* folder. You should end up in a terminal window that is running a link:http://www.gnu.org/software/bash/manual/bashref.html[Bash shell]. Test the shell by issuing following commands:
+.. `cd /` should take you to thr *Root* directory in Cygwin.
+.. The `LS` commands that should list all files and folders in the current directory.
+.. Use the `exit` command to end the terminal.
+. When needed, to *uninstall* Cygwin you can simply delete the *Root* and *Local Package* directory, and the *shortcuts* that were created during installation.
+
+=== SSH
+
+HBase (and Hadoop) rely on link:http://nl.wikipedia.org/wiki/Secure_Shell[*SSH*] for interprocess/-node *communication* and launching* remote commands*. SSH will be provisioned on the target system via Cygwin, which supports running Cygwin programs as *Windows services*!
+
+. Rerun the `*setup.exe*`* utility*.
+. Leave all parameters as is, skipping through the wizard using the `Next` button until the `Select Packages` panel is shown.
+. Maximize the window and click the `View` button to toggle to the list view, which is ordered alfabetically on `Package`, making it easier to find the packages we'll need.
+. Select the following packages by clicking the status word (normally `Skip`) so it's marked for installation. Use the `Next `button to download and install the packages.
+.. `OpenSSH`
+.. `tcp_wrappers`
+.. `diffutils`
+.. `zlib`
+. Wait for the install to complete and finish the installation.
+
+=== HBase
+
+Download the *latest release* of Apache HBase from link:http://www.apache.org/dyn/closer.cgi/hbase/. As the Apache HBase distributable is just a zipped archive, installation is as simple as unpacking the archive so it ends up in its final *installation* directory. Notice that HBase has to be installed in Cygwin and a good directory suggestion is to use `/usr/local/` (or [`*Root* directory]\usr\local` in Windows slang). You should end up with a `/usr/local/hbase-_versi` installation in Cygwin.
+
+This finishes installation. We go on with the configuration.
+
+== Configuration
+
+There are 3 parts left to configure: *Java, SSH and HBase* itself. Following paragraphs explain eacht topic in detail.
+
+=== Java
+
+One important thing to remember in shell scripting in general (i.e. *nix and Windows) is that managing, manipulating and assembling path names that contains spaces can be very hard, due to the need to escape and quote those characters and strings. So we try to stay away from spaces in path names. *nix environments can help us out here very easily by using *symbolic links*.
+
+. Create a link in `/usr/local` to the Java home directory by using the following command and substituting the name of your chosen Java environment: +
+----
+LN -s /cygdrive/c/Program\ Files/Java/*_jre name_*/usr/local/*_jre name_*
+----
+. Test your java installation by changing directories to your Java folder `CD /usr/local/_jre name_` and issueing the command `./bin/java -version`. This should output your version of the chosen JRE.
+
+=== SSH
+
+Configuring *SSH *is quite elaborate, but primarily a question of launching it by default as a* Windows service*.
+
+. On Windows Vista and above make sure you run the Cygwin shell with *elevated privileges*, by right-clicking on the shortcut an using `Run as Administrator`.
+. First of all, we have to make sure the *rights on some crucial files* are correct. Use the commands underneath. You can verify all rights by using the `LS -L` command on the different files. Also, notice the auto-completion feature in the shell using `TAB` is extremely handy in these situations.
+.. `chmod +r /etc/passwd` to make the passwords file readable for all
+.. `chmod u+w /etc/passwd` to make the passwords file writable for the owner
+.. `chmod +r /etc/group` to make the groups file readable for all
+.. `chmod u+w /etc/group` to make the groups file writable for the owner
+.. `chmod 755 /var` to make the var folder writable to owner and readable and executable to all
+. Edit the */etc/hosts.allow* file using your favorite editor (why not VI in the shell!) and make sure the following two lines are in there before the `PARANOID` line: +
+----
+ALL : localhost 127.0.0.1/32 : allow
+ALL : [::1]/128 : allow
+----
+. Next we have to *configure SSH* by using the script `ssh-host-config`.
+.. If this script asks to overwrite an existing `/etc/ssh_config`, answer `yes`.
+.. If this script asks to overwrite an existing `/etc/sshd_config`, answer `yes`.
+.. If this script asks to use privilege separation, answer `yes`.
+.. If this script asks to install `sshd` as a service, answer `yes`. Make sure you started your shell as Adminstrator!
+.. If this script asks for the CYGWIN value, just `enter` as the default is `ntsec`.
+.. If this script asks to create the `sshd` account, answer `yes`.
+.. If this script asks to use a different user name as service account, answer `no` as the default will suffice.
+.. If this script asks to create the `cyg_server` account, answer `yes`. Enter a password for the account.
+. *Start the SSH service* using `net start sshd` or `cygrunsrv --start sshd`. Notice that `cygrunsrv` is the utility that make the process run as a Windows service. Confirm that you see a message stating that `the CYGWIN sshd service was started succesfully.`
+. Harmonize Windows and Cygwin* user account* by using the commands: +
+----
+mkpasswd -cl > /etc/passwd
+mkgroup --local > /etc/group
+----
+. Test *the installation of SSH:
+.. Open a new Cygwin terminal.
+.. Use the command `whoami` to verify your userID.
+.. Issue an `ssh localhost` to connect to the system itself.
+.. Answer `yes` when presented with the server's fingerprint.
+.. Issue your password when prompted.
+.. Test a few commands in the remote session
+.. The `exit` command should take you back to your first shell in Cygwin.
+. `Exit` should terminate the Cygwin shell.
+
+=== HBase
+
+If all previous configurations are working properly, we just need some tinkering at the *HBase config* files to properly resolve on Windows/Cygwin. All files and paths referenced here start from the HBase `[*installation* directory]` as working directory.
+
+. HBase uses the `./conf/*hbase-env.sh*` to configure its dependencies on the runtime environment. Copy and uncomment following lines just underneath their original, change them to fit your environemnt. They should read something like: +
+----
+export JAVA_HOME=/usr/local/_jre name_
+export HBASE_IDENT_STRING=$HOSTNAME
+----
+. HBase uses the _./conf/`*hbase-default.xml*`_ file for configuration. Some properties do not resolve to existing directories because the JVM runs on Windows. This is the major issue to keep in mind when working with Cygwin: within the shell all paths are *nix-alike, hence relative to the root `/`. However, every parameter that is to be consumed within the windows processes themself, need to be Windows settings, hence `C:\`-alike. Change following propeties in the configuration file, adjusting paths where necessary to conform with your own installation:
+.. `hbase.rootdir` must read e.g. `file:///C:/cygwin/root/tmp/hbase/data`
+.. `hbase.tmp.dir` must read `C:/cygwin/root/tmp/hbase/tmp`
+.. `hbase.zookeeper.quorum` must read `127.0.0.1` because for some reason `localhost` doesn't seem to resolve properly on Cygwin.
+. Make sure the configured `hbase.rootdir` and `hbase.tmp.dir` *directories exist* and have the proper* rights* set up e.g. by issuing a `chmod 777` on them.
+
+== Testing
+
+This should conclude the installation and configuration of Apache HBase on Windows using Cygwin. So it's time *to test it*.
+
+. Start a Cygwin* terminal*, if you haven't already.
+. Change directory to HBase *installation* using `CD /usr/local/hbase-_version_`, preferably using auto-completion.
+. *Start HBase* using the command `./bin/start-hbase.sh`
+.. When prompted to accept the SSH fingerprint, answer `yes`.
+.. When prompted, provide your password. Maybe multiple times.
+.. When the command completes, the HBase server should have started.
+.. However, to be absolutely certain, check the logs in the `./logs` directory for any exceptions.
+. Next we *start the HBase shell* using the command `./bin/hbase shell`
+. We run some simple *test commands*
+.. Create a simple table using command `create 'test', 'data'`
+.. Verify the table exists using the command `list`
+.. Insert data into the table using e.g. +
+----
+put 'test', 'row1', 'data:1', 'value1'
+put 'test', 'row2', 'data:2', 'value2'
+put 'test', 'row3', 'data:3', 'value3'
+----
+.. List all rows in the table using the command `scan 'test'` that should list all the rows previously inserted. Notice how 3 new columns where added without changing the schema!
+.. Finally we get rid of the table by issuing `disable 'test'` followed by `drop 'test'` and verified by `list` which should give an empty listing.
+. *Leave the shell* by `exit`
+. To *stop the HBase server* issue the `./bin/stop-hbase.sh` command. And wait for it to complete!!! Killing the process might corrupt your data on disk.
+. In case of *problems*,
+.. Verify the HBase logs in the `./logs` directory.
+.. Try to fix the problem
+.. Get help on the forums or IRC (`#hbase@freenode.net`). People are very active and keen to help out!
+.. Stop and retest the server.
+
+== Conclusion
+
+Now your *HBase *server is running, *start coding* and build that next killer app on this particular, but scalable datastore!
+
[06/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/security.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[security]]
+= Securing Apache HBase
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[IMPORTANT]
+.Reporting Security Bugs
+====
+NOTE: To protect existing HBase installations from exploitation, please *do not* use JIRA to report security-related bugs. Instead, send your report to the mailing list private@apache.org, which allows anyone to send messages, but restricts who can read them. Someone on that list will contact you to follow up on your report.
+
+HBase adheres to the Apache Software Foundation's policy on reported vulnerabilities, available at http://apache.org/security/.
+
+If you wish to send an encrypted report, you can use the GPG details provided for the general ASF security list. This will likely increase the response time to your report.
+====
+
+HBase provides mechanisms to secure various components and aspects of HBase and how it relates to the rest of the Hadoop infrastructure, as well as clients and resources outside Hadoop.
+
+== Using Secure HTTP (HTTPS) for the Web UI
+
+A default HBase install uses insecure HTTP connections for Web UIs for the master and region servers.
+To enable secure HTTP (HTTPS) connections instead, set `hadoop.ssl.enabled` to `true` in _hbase-site.xml_.
+This does not change the port used by the Web UI.
+To change the port for the web UI for a given HBase component, configure that port's setting in hbase-site.xml.
+These settings are:
+
+* `hbase.master.info.port`
+* `hbase.regionserver.info.port`
+
+.If you enable HTTPS, clients should avoid using the non-secure HTTP connection.
+[NOTE]
+====
+If you enable secure HTTP, clients should connect to HBase using the `https://` URL.
+Clients using the `http://` URL will receive an HTTP response of `200`, but will not receive any data.
+The following exception is logged:
+
+----
+javax.net.ssl.SSLException: Unrecognized SSL message, plaintext connection?
+----
+
+This is because the same port is used for HTTP and HTTPS.
+
+HBase uses Jetty for the Web UI.
+Without modifying Jetty itself, it does not seem possible to configure Jetty to redirect one port to another on the same host.
+See Nick Dimiduk's contribution on this link:http://stackoverflow.com/questions/20611815/redirect-from-http-to-https-in-jetty[Stack Overflow] thread for more information.
+If you know how to fix this without opening a second port for HTTPS, patches are appreciated.
+====
+
+[[hbase.secure.configuration]]
+== Secure Client Access to Apache HBase
+
+Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients.
+See also Matteo Bertozzi's article on link:http://www.cloudera.com/blog/2012/09/understanding-user-authentication-and-authorization-in-apache-hbase/[Understanding User Authentication and Authorization in Apache HBase].
+
+This describes how to set up Apache HBase and clients for connection to secure HBase resources.
+
+[[security.prerequisites]]
+=== Prerequisites
+
+Hadoop Authentication Configuration::
+ To run HBase RPC with strong authentication, you must set `hbase.security.authentication` to `true`.
+ In this case, you must also set `hadoop.security.authentication` to `true`.
+ Otherwise, you would be using strong authentication for HBase but not for the underlying HDFS, which would cancel out any benefit.
+
+Kerberos KDC::
+ You need to have a working Kerberos KDC.
+
+=== Server-side Configuration for Secure Operation
+
+First, refer to <<security.prerequisites,security.prerequisites>> and ensure that your underlying HDFS configuration is secure.
+
+Add the following to the `hbase-site.xml` file on every server machine in the cluster:
+
+[source,xml]
+----
+<property>
+ <name>hbase.security.authentication</name>
+ <value>kerberos</value>
+</property>
+<property>
+ <name>hbase.security.authorization</name>
+ <value>true</value>
+</property>
+<property>
+<name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.token.TokenProvider</value>
+</property>
+----
+
+A full shutdown and restart of HBase service is required when deploying these configuration changes.
+
+=== Client-side Configuration for Secure Operation
+
+First, refer to <<security.prerequisites>> and ensure that your underlying HDFS configuration is secure.
+
+Add the following to the `hbase-site.xml` file on every client:
+
+[source,xml]
+----
+<property>
+ <name>hbase.security.authentication</name>
+ <value>kerberos</value>
+</property>
+----
+
+The client environment must be logged in to Kerberos from KDC or keytab via the `kinit` command before communication with the HBase cluster will be possible.
+
+Be advised that if the `hbase.security.authentication` in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.
+
+Once HBase is configured for secure RPC it is possible to optionally configure encrypted communication.
+To do so, add the following to the `hbase-site.xml` file on every client:
+
+[source,xml]
+----
+<property>
+ <name>hbase.rpc.protection</name>
+ <value>privacy</value>
+</property>
+----
+
+This configuration property can also be set on a per-connection basis.
+Set it in the `Configuration` supplied to `Table`:
+
+[source,java]
+----
+Configuration conf = HBaseConfiguration.create();
+Connection connection = ConnectionFactory.createConnection(conf);
+conf.set("hbase.rpc.protection", "privacy");
+try (Connection connection = ConnectionFactory.createConnection(conf)) {
+ try (Table table = connection.getTable(TableName.valueOf(tablename)) {
+ .... do your stuff
+ }
+}
+----
+
+Expect a ~10% performance penalty for encrypted communication.
+
+[[security.client.thrift]]
+=== Client-side Configuration for Secure Operation - Thrift Gateway
+
+Add the following to the `hbase-site.xml` file for every Thrift gateway:
+[source,xml]
+----
+<property>
+ <name>hbase.thrift.keytab.file</name>
+ <value>/etc/hbase/conf/hbase.keytab</value>
+</property>
+<property>
+ <name>hbase.thrift.kerberos.principal</name>
+ <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
+ <!-- TODO: This may need to be HTTP/_HOST@<REALM> and _HOST may not work.
+ You may have to put the concrete full hostname.
+ -->
+</property>
+----
+
+Substitute the appropriate credential and keytab for _$USER_ and _$KEYTAB_ respectively.
+
+In order to use the Thrift API principal to interact with HBase, it is also necessary to add the `hbase.thrift.kerberos.principal` to the `_acl_` table.
+For example, to give the Thrift API principal, `thrift_server`, administrative access, a command such as this one will suffice:
+
+[source,sql]
+----
+grant 'thrift_server', 'RWCA'
+----
+
+For more information about ACLs, please see the <<hbase.accesscontrol.configuration>> section
+
+The Thrift gateway will authenticate with HBase using the supplied credential.
+No authentication will be performed by the Thrift gateway itself.
+All client access via the Thrift gateway will use the Thrift gateway's credential and have its privilege.
+
+[[security.gateway.thrift]]
+=== Configure the Thrift Gateway to Authenticate on Behalf of the Client
+
+<<security.client.thrift>> describes how to authenticate a Thrift client to HBase using a fixed user.
+As an alternative, you can configure the Thrift gateway to authenticate to HBase on the client's behalf, and to access HBase using a proxy user.
+This was implemented in link:https://issues.apache.org/jira/browse/HBASE-11349[HBASE-11349] for Thrift 1, and link:https://issues.apache.org/jira/browse/HBASE-11474[HBASE-11474] for Thrift 2.
+
+.Limitations with Thrift Framed Transport
+[NOTE]
+====
+If you use framed transport, you cannot yet take advantage of this feature, because SASL does not work with Thrift framed transport at this time.
+====
+
+To enable it, do the following.
+
+
+. Be sure Thrift is running in secure mode, by following the procedure described in <<security.client.thrift>>.
+. Be sure that HBase is configured to allow proxy users, as described in <<security.rest.gateway>>.
+. In _hbase-site.xml_ for each cluster node running a Thrift gateway, set the property `hbase.thrift.security.qop` to one of the following three values:
++
+* `auth-conf` - authentication, integrity, and confidentiality checking
+* `auth-int` - authentication and integrity checking
+* `auth` - authentication checking only
+
+. Restart the Thrift gateway processes for the changes to take effect.
+ If a node is running Thrift, the output of the `jps` command will list a `ThriftServer` process.
+ To stop Thrift on a node, run the command `bin/hbase-daemon.sh stop thrift`.
+ To start Thrift on a node, run the command `bin/hbase-daemon.sh start thrift`.
+
+[[security.gateway.thrift.doas]]
+=== Configure the Thrift Gateway to Use the `doAs` Feature
+
+<<security.gateway.thrift>> describes how to configure the Thrift gateway to authenticate to HBase on the client's behalf, and to access HBase using a proxy user. The limitation of this approach is that after the client is initialized with a particular set of credentials, it cannot change these credentials during the session. The `doAs` feature provides a flexible way to impersonate multiple principals using the same client. This feature was implemented in link:https://issues.apache.org/jira/browse/HBASE-12640[HBASE-12640] for Thrift 1, but is currently not available for Thrift 2.
+
+*To allow proxy users*, add the following to the _hbase-site.xml_ file for every HBase node:
+
+[source,xml]
+----
+<property>
+ <name>hadoop.security.authorization</name>
+ <value>true</value>
+</property>
+<property>
+ <name>hadoop.proxyuser.$USER.groups</name>
+ <value>$GROUPS</value>
+</property>
+<property>
+ <name>hadoop.proxyuser.$USER.hosts</name>
+ <value>$GROUPS</value>
+</property>
+----
+
+*To enable the `doAs` feature*, add the following to the _hbase-site.xml_ file for every Thrift gateway:
+
+[source,xml]
+----
+<property>
+ <name>hbase.regionserver.thrift.http</name>
+ <value>true</value>
+</property>
+<property>
+ <name>hbase.thrift.support.proxyuser</name>
+ <value>true/value>
+</property>
+----
+
+Take a look at the link:https://github.com/apache/hbase/blob/master/hbase-examples/src/main/java/org/apache/hadoop/hbase/thrift/HttpDoAsClient.java[demo client] to get an overall idea of how to use this feature in your client.
+
+=== Client-side Configuration for Secure Operation - REST Gateway
+
+Add the following to the `hbase-site.xml` file for every REST gateway:
+
+[source,xml]
+----
+<property>
+ <name>hbase.rest.keytab.file</name>
+ <value>$KEYTAB</value>
+</property>
+<property>
+ <name>hbase.rest.kerberos.principal</name>
+ <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
+</property>
+----
+
+Substitute the appropriate credential and keytab for _$USER_ and _$KEYTAB_ respectively.
+
+The REST gateway will authenticate with HBase using the supplied credential.
+
+In order to use the REST API principal to interact with HBase, it is also necessary to add the `hbase.rest.kerberos.principal` to the `_acl_` table.
+For example, to give the REST API principal, `rest_server`, administrative access, a command such as this one will suffice:
+
+[source,sql]
+----
+grant 'rest_server', 'RWCA'
+----
+
+For more information about ACLs, please see the <<hbase.accesscontrol.configuration>> section
+
+HBase REST gateway supports link:http://hadoop.apache.org/docs/stable/hadoop-auth/index.html[SPNEGO HTTP authentication] for client access to the gateway.
+To enable REST gateway Kerberos authentication for client access, add the following to the `hbase-site.xml` file for every REST gateway.
+
+[source,xml]
+----
+<property>
+ <name>hbase.rest.authentication.type</name>
+ <value>kerberos</value>
+</property>
+<property>
+ <name>hbase.rest.authentication.kerberos.principal</name>
+ <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
+</property>
+<property>
+ <name>hbase.rest.authentication.kerberos.keytab</name>
+ <value>$KEYTAB</value>
+</property>
+----
+
+Substitute the keytab for HTTP for _$KEYTAB_.
+
+HBase REST gateway supports different 'hbase.rest.authentication.type': simple, kerberos.
+You can also implement a custom authentication by implemening Hadoop AuthenticationHandler, then specify the full class name as 'hbase.rest.authentication.type' value.
+For more information, refer to link:http://hadoop.apache.org/docs/stable/hadoop-auth/index.html[SPNEGO HTTP authentication].
+
+[[security.rest.gateway]]
+=== REST Gateway Impersonation Configuration
+
+By default, the REST gateway doesn't support impersonation.
+It accesses the HBase on behalf of clients as the user configured as in the previous section.
+To the HBase server, all requests are from the REST gateway user.
+The actual users are unknown.
+You can turn on the impersonation support.
+With impersonation, the REST gateway user is a proxy user.
+The HBase server knows the acutal/real user of each request.
+So it can apply proper authorizations.
+
+To turn on REST gateway impersonation, we need to configure HBase servers (masters and region servers) to allow proxy users; configure REST gateway to enable impersonation.
+
+To allow proxy users, add the following to the `hbase-site.xml` file for every HBase server:
+
+[source,xml]
+----
+<property>
+ <name>hadoop.security.authorization</name>
+ <value>true</value>
+</property>
+<property>
+ <name>hadoop.proxyuser.$USER.groups</name>
+ <value>$GROUPS</value>
+</property>
+<property>
+ <name>hadoop.proxyuser.$USER.hosts</name>
+ <value>$GROUPS</value>
+</property>
+----
+
+Substitute the REST gateway proxy user for _$USER_, and the allowed group list for _$GROUPS_.
+
+To enable REST gateway impersonation, add the following to the `hbase-site.xml` file for every REST gateway.
+
+[source,xml]
+----
+<property>
+ <name>hbase.rest.authentication.type</name>
+ <value>kerberos</value>
+</property>
+<property>
+ <name>hbase.rest.authentication.kerberos.principal</name>
+ <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
+</property>
+<property>
+ <name>hbase.rest.authentication.kerberos.keytab</name>
+ <value>$KEYTAB</value>
+</property>
+----
+
+Substitute the keytab for HTTP for _$KEYTAB_.
+
+[[hbase.secure.simpleconfiguration]]
+== Simple User Access to Apache HBase
+
+Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients.
+See also Matteo Bertozzi's article on link:http://www.cloudera.com/blog/2012/09/understanding-user-authentication-and-authorization-in-apache-hbase/[Understanding User Authentication and Authorization in Apache HBase].
+
+This describes how to set up Apache HBase and clients for simple user access to HBase resources.
+
+=== Simple versus Secure Access
+
+The following section shows how to set up simple user access.
+Simple user access is not a secure method of operating HBase.
+This method is used to prevent users from making mistakes.
+It can be used to mimic the Access Control using on a development system without having to set up Kerberos.
+
+This method is not used to prevent malicious or hacking attempts.
+To make HBase secure against these types of attacks, you must configure HBase for secure operation.
+Refer to the section <<hbase.secure.configuration>> and complete all of the steps described there.
+
+=== Prerequisites
+
+None
+
+=== Server-side Configuration for Simple User Access Operation
+
+Add the following to the `hbase-site.xml` file on every server machine in the cluster:
+
+[source,xml]
+----
+<property>
+ <name>hbase.security.authentication</name>
+ <value>simple</value>
+</property>
+<property>
+ <name>hbase.security.authorization</name>
+ <value>true</value>
+</property>
+<property>
+ <name>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.regionserver.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+----
+
+For 0.94, add the following to the `hbase-site.xml` file on every server machine in the cluster:
+
+[source,xml]
+----
+<property>
+ <name>hbase.rpc.engine</name>
+ <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
+</property>
+<property>
+ <name>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+----
+
+A full shutdown and restart of HBase service is required when deploying these configuration changes.
+
+=== Client-side Configuration for Simple User Access Operation
+
+Add the following to the `hbase-site.xml` file on every client:
+
+[source,xml]
+----
+<property>
+ <name>hbase.security.authentication</name>
+ <value>simple</value>
+</property>
+----
+
+For 0.94, add the following to the `hbase-site.xml` file on every server machine in the cluster:
+
+[source,xml]
+----
+<property>
+ <name>hbase.rpc.engine</name>
+ <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
+</property>
+----
+
+Be advised that if the `hbase.security.authentication` in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.
+
+==== Client-side Configuration for Simple User Access Operation - Thrift Gateway
+
+The Thrift gateway user will need access.
+For example, to give the Thrift API user, `thrift_server`, administrative access, a command such as this one will suffice:
+
+[source,sql]
+----
+grant 'thrift_server', 'RWCA'
+----
+
+For more information about ACLs, please see the <<hbase.accesscontrol.configuration>> section
+
+The Thrift gateway will authenticate with HBase using the supplied credential.
+No authentication will be performed by the Thrift gateway itself.
+All client access via the Thrift gateway will use the Thrift gateway's credential and have its privilege.
+
+==== Client-side Configuration for Simple User Access Operation - REST Gateway
+
+The REST gateway will authenticate with HBase using the supplied credential.
+No authentication will be performed by the REST gateway itself.
+All client access via the REST gateway will use the REST gateway's credential and have its privilege.
+
+The REST gateway user will need access.
+For example, to give the REST API user, `rest_server`, administrative access, a command such as this one will suffice:
+
+[source,sql]
+----
+grant 'rest_server', 'RWCA'
+----
+
+For more information about ACLs, please see the <<hbase.accesscontrol.configuration>> section
+
+It should be possible for clients to authenticate with the HBase cluster through the REST gateway in a pass-through manner via SPNEGO HTTP authentication.
+This is future work.
+
+== Securing Access To Your Data
+
+After you have configured secure authentication between HBase client and server processes and gateways, you need to consider the security of your data itself.
+HBase provides several strategies for securing your data:
+
+* Role-based Access Control (RBAC) controls which users or groups can read and write to a given HBase resource or execute a coprocessor endpoint, using the familiar paradigm of roles.
+* Visibility Labels which allow you to label cells and control access to labelled cells, to further restrict who can read or write to certain subsets of your data.
+ Visibility labels are stored as tags.
+ See <<hbase.tags,hbase.tags>> for more information.
+* Transparent encryption of data at rest on the underlying filesystem, both in HFiles and in the WAL.
+ This protects your data at rest from an attacker who has access to the underlying filesystem, without the need to change the implementation of the client.
+ It can also protect against data leakage from improperly disposed disks, which can be important for legal and regulatory compliance.
+
+Server-side configuration, administration, and implementation details of each of these features are discussed below, along with any performance trade-offs.
+An example security configuration is given at the end, to show these features all used together, as they might be in a real-world scenario.
+
+CAUTION: All aspects of security in HBase are in active development and evolving rapidly.
+Any strategy you employ for security of your data should be thoroughly tested.
+In addition, some of these features are still in the experimental stage of development.
+To take advantage of many of these features, you must be running HBase 0.98+ and using the HFile v3 file format.
+
+.Protecting Sensitive Files
+[WARNING]
+====
+Several procedures in this section require you to copy files between cluster nodes.
+When copying keys, configuration files, or other files containing sensitive strings, use a secure method, such as `ssh`, to avoid leaking sensitive data.
+====
+
+.Procedure: Basic Server-Side Configuration
+. Enable HFile v3, by setting `hfile.format.version` to 3 in _hbase-site.xml_.
+ This is the default for HBase 1.0 and newer.
++
+[source,xml]
+----
+<property>
+ <name>hfile.format.version</name>
+ <value>3</value>
+</property>
+----
+
+. Enable SASL and Kerberos authentication for RPC and ZooKeeper, as described in <<security.prerequisites,security.prerequisites>> and <<zk.sasl.auth>>.
+
+[[hbase.tags]]
+=== Tags
+
+[firstterm]_Tags_ are a feature of HFile v3.
+A tag is a piece of metadata which is part of a cell, separate from the key, value, and version.
+Tags are an implementation detail which provides a foundation for other security-related features such as cell-level ACLs and visibility labels.
+Tags are stored in the HFiles themselves.
+It is possible that in the future, tags will be used to implement other HBase features.
+You don't need to know a lot about tags in order to use the security features they enable.
+
+==== Implementation Details
+
+Every cell can have zero or more tags.
+Every tag has a type and the actual tag byte array.
+
+Just as row keys, column families, qualifiers and values can be encoded (see <<data.block.encoding.types,data.block.encoding.types>>), tags can also be encoded as well.
+You can enable or disable tag encoding at the level of the column family, and it is enabled by default.
+Use the `HColumnDescriptor#setCompressionTags(boolean compressTags)` method to manage encoding settings on a column family.
+You also need to enable the DataBlockEncoder for the column family, for encoding of tags to take effect.
+
+You can enable compression of each tag in the WAL, if WAL compression is also enabled, by setting the value of `hbase.regionserver.wal.tags.enablecompression` to `true` in _hbase-site.xml_.
+Tag compression uses dictionary encoding.
+
+Tag compression is not supported when using WAL encryption.
+
+[[hbase.accesscontrol.configuration]]
+=== Access Control Labels (ACLs)
+
+==== How It Works
+
+ACLs in HBase are based upon a user's membership in or exclusion from groups, and a given group's permissions to access a given resource.
+ACLs are implemented as a coprocessor called AccessController.
+
+HBase does not maintain a private group mapping, but relies on a [firstterm]_Hadoop group mapper_, which maps between entities in a directory such as LDAP or Active Directory, and HBase users.
+Any supported Hadoop group mapper will work.
+Users are then granted specific permissions (Read, Write, Execute, Create, Admin) against resources (global, namespaces, tables, cells, or endpoints).
+
+NOTE: With Kerberos and Access Control enabled, client access to HBase is authenticated and user data is private unless access has been explicitly granted.
+
+HBase has a simpler security model than relational databases, especially in terms of client operations.
+No distinction is made between an insert (new record) and update (of existing record), for example, as both collapse down into a Put.
+
+===== Understanding Access Levels
+
+HBase access levels are granted independently of each other and allow for different types of operations at a given scope.
+
+* _Read \(R)_ - can read data at the given scope
+* _Write (W)_ - can write data at the given scope
+* _Execute (X)_ - can execute coprocessor endpoints at the given scope
+* _Create \(C)_ - can create tables or drop tables (even those they did not create) at the given scope
+* _Admin (A)_ - can perform cluster operations such as balancing the cluster or assigning regions at the given scope
+
+The possible scopes are:
+
+* _Superuser_ - superusers can perform any operation available in HBase, to any resource.
+ The user who runs HBase on your cluster is a superuser, as are any principals assigned to the configuration property `hbase.superuser` in _hbase-site.xml_ on the HMaster.
+* _Global_ - permissions granted at _global_ scope allow the admin to operate on all tables of the cluster.
+* _Namespace_ - permissions granted at _namespace_ scope apply to all tables within a given namespace.
+* _Table_ - permissions granted at _table_ scope apply to data or metadata within a given table.
+* _ColumnFamily_ - permissions granted at _ColumnFamily_ scope apply to cells within that ColumnFamily.
+* _Cell_ - permissions granted at _cell_ scope apply to that exact cell coordinate (key, value, timestamp). This allows for policy evolution along with data.
++
+To change an ACL on a specific cell, write an updated cell with new ACL to the precise coordinates of the original.
++
+If you have a multi-versioned schema and want to update ACLs on all visible versions, you need to write new cells for all visible versions.
+The application has complete control over policy evolution.
++
+The exception to the above rule is `append` and `increment` processing.
+Appends and increments can carry an ACL in the operation.
+If one is included in the operation, then it will be applied to the result of the `append` or `increment`.
+Otherwise, the ACL of the existing cell you are appending to or incrementing is preserved.
+
+
+The combination of access levels and scopes creates a matrix of possible access levels that can be granted to a user.
+In a production environment, it is useful to think of access levels in terms of what is needed to do a specific job.
+The following list describes appropriate access levels for some common types of HBase users.
+It is important not to grant more access than is required for a given user to perform their required tasks.
+
+* _Superusers_ - In a production system, only the HBase user should have superuser access.
+ In a development environment, an administrator may need superuser access in order to quickly control and manage the cluster.
+ However, this type of administrator should usually be a Global Admin rather than a superuser.
+* _Global Admins_ - A global admin can perform tasks and access every table in HBase.
+ In a typical production environment, an admin should not have Read or Write permissions to data within tables.
+* A global admin with Admin permissions can perform cluster-wide operations on the cluster, such as balancing, assigning or unassigning regions, or calling an explicit major compaction.
+ This is an operations role.
+* A global admin with Create permissions can create or drop any table within HBase.
+ This is more of a DBA-type role.
++
+In a production environment, it is likely that different users will have only one of Admin and Create permissions.
++
+[WARNING]
+====
+In the current implementation, a Global Admin with `Admin` permission can grant himself `Read` and `Write` permissions on a table and gain access to that table's data.
+For this reason, only grant `Global Admin` permissions to trusted user who actually need them.
+
+Also be aware that a `Global Admin` with `Create` permission can perform a `Put` operation on the ACL table, simulating a `grant` or `revoke` and circumventing the authorization check for `Global Admin` permissions.
+
+Due to these issues, be cautious with granting `Global Admin` privileges.
+====
+
+* _Namespace Admins_ - a namespace admin with `Create` permissions can create or drop tables within that namespace, and take and restore snapshots.
+ A namespace admin with `Admin` permissions can perform operations such as splits or major compactions on tables within that namespace.
+* _Table Admins_ - A table admin can perform administrative operations only on that table.
+ A table admin with `Create` permissions can create snapshots from that table or restore that table from a snapshot.
+ A table admin with `Admin` permissions can perform operations such as splits or major compactions on that table.
+* _Users_ - Users can read or write data, or both.
+ Users can also execute coprocessor endpoints, if given `Executable` permissions.
+
+.Real-World Example of Access Levels
+[cols="1,1,1,1", options="header"]
+|===
+| Job Title
+| Scope
+| Permissions
+| Description
+
+| Senior Administrator
+| Global
+| Access, Create
+| Manages the cluster and gives access to Junior Administrators.
+
+| Junior Administrator
+| Global
+| Create
+| Creates tables and gives access to Table Administrators.
+
+| Table Administrator
+| Table
+| Access
+| Maintains a table from an operations point of view.
+
+| Data Analyst
+| Table
+| Read
+| Creates reports from HBase data.
+
+| Web Application
+| Table
+| Read, Write
+| Puts data into HBase and uses HBase data to perform operations.
+|===
+
+.ACL Matrix
+For more details on how ACLs map to specific HBase operations and tasks, see <<appendix_acl_matrix,appendix acl matrix>>.
+
+===== Implementation Details
+
+Cell-level ACLs are implemented using tags (see <<hbase.tags>>). In order to use cell-level ACLs, you must be using HFile v3 and HBase 0.98 or newer.
+
+. Files created by HBase are owned by the operating system user running the HBase process.
+ To interact with HBase files, you should use the API or bulk load facility.
+. HBase does not model "roles" internally in HBase.
+ Instead, group names can be granted permissions.
+ This allows external modeling of roles via group membership.
+ Groups are created and manipulated externally to HBase, via the Hadoop group mapping service.
+
+===== Server-Side Configuration
+
+. As a prerequisite, perform the steps in <<security.data.basic.server.side>>.
+. Install and configure the AccessController coprocessor, by setting the following properties in _hbase-site.xml_.
+ These properties take a list of classes.
++
+NOTE: If you use the AccessController along with the VisibilityController, the AccessController must come first in the list, because with both components active, the VisibilityController will delegate access control on its system tables to the AccessController.
+For an example of using both together, see <<security.example.config>>.
++
+[source,xml]
+----
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.token.TokenProvider</value>
+</property>
+<property>
+ <name>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.regionserver.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController</value>
+</property>
+<property>
+ <name>hbase.security.exec.permission.checks</name>
+ <value>true</value>
+</property>
+----
++
+Optionally, you can enable transport security, by setting `hbase.rpc.protection` to `auth-conf`.
+This requires HBase 0.98.4 or newer.
+
+. Set up the Hadoop group mapper in the Hadoop namenode's _core-site.xml_.
+ This is a Hadoop file, not an HBase file.
+ Customize it to your site's needs.
+ Following is an example.
++
+[source,xml]
+----
+<property>
+ <name>hadoop.security.group.mapping</name>
+ <value>org.apache.hadoop.security.LdapGroupsMapping</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.url</name>
+ <value>ldap://server</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.bind.user</name>
+ <value>Administrator@example-ad.local</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.bind.password</name>
+ <value>****</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.base</name>
+ <value>dc=example-ad,dc=local</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
+ <value>(&(objectClass=user)(sAMAccountName={0}))</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
+ <value>(objectClass=group)</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
+ <value>member</value>
+</property>
+
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
+ <value>cn</value>
+</property>
+----
+. Optionally, enable the early-out evaluation strategy.
+ Prior to HBase 0.98.0, if a user was not granted access to a column family, or at least a column qualifier, an AccessDeniedException would be thrown.
+ HBase 0.98.0 removed this exception in order to allow cell-level exceptional grants.
+ To restore the old behavior in HBase 0.98.0-0.98.6, set `hbase.security.access.early_out` to `true` in _hbase-site.xml_.
+ In HBase 0.98.6, the default has been returned to `true`.
+. Distribute your configuration and restart your cluster for changes to take effect.
+. To test your configuration, log into HBase Shell as a given user and use the `whoami` command to report the groups your user is part of.
+ In this example, the user is reported as being a member of the `services` group.
++
+----
+hbase> whoami
+service (auth:KERBEROS)
+ groups: services
+----
+
+
+===== Administration
+
+Administration tasks can be performed from HBase Shell or via an API.
+
+.API Examples
+[CAUTION]
+====
+Many of the API examples below are taken from source files _hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java_ and _hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/SecureTestUtil.java_.
+
+Neither the examples, nor the source files they are taken from, are part of the public HBase API, and are provided for illustration only.
+Refer to the official API for usage instructions.
+====
+
+
+. User and Group Administration
++
+Users and groups are maintained external to HBase, in your directory.
+
+. Granting Access To A Namespace, Table, Column Family, or Cell
++
+There are a few different types of syntax for grant statements.
+The first, and most familiar, is as follows, with the table and column family being optional:
++
+[source,sql]
+----
+grant 'user', 'RWXCA', 'TABLE', 'CF', 'CQ'
+----
++
+Groups and users are granted access in the same way, but groups are prefixed with an `@` symbol.
+In the same way, tables and namespaces are specified in the same way, but namespaces are prefixed with an `@` symbol.
++
+It is also possible to grant multiple permissions against the same resource in a single statement, as in this example.
+The first sub-clause maps users to ACLs and the second sub-clause specifies the resource.
++
+NOTE: HBase Shell support for granting and revoking access at the cell level is for testing and verification support, and should not be employed for production use because it won't apply the permissions to cells that don't exist yet.
+The correct way to apply cell level permissions is to do so in the application code when storing the values.
++
+.ACL Granularity and Evaluation Order
+ACLs are evaluated from least granular to most granular, and when an ACL is reached that grants permission, evaluation stops.
+This means that cell ACLs do not override ACLs at less granularity.
++
+.HBase Shell
+====
+* Global:
++
+----
+hbase> grant '@admins', 'RWXCA'
+----
+
+* Namespace:
++
+----
+hbase> grant 'service', 'RWXCA', '@test-NS'
+----
+
+* Table:
++
+----
+hbase> grant 'service', 'RWXCA', 'user'
+----
+
+* Column Family:
++
+----
+hbase> grant '@developers', 'RW', 'user', 'i'
+----
+
+* Column Qualifier:
++
+----
+hbase> grant 'service, 'RW', 'user', 'i', 'foo'
+----
+
+* Cell:
++
+The syntax for granting cell ACLs uses the following syntax:
++
+----
+grant <table>, \
+ { '<user-or-group>' => \
+ '<permissions>', ... }, \
+ { <scanner-specification> }
+----
++
+* _<user-or-group>_ is the user or group name, prefixed with `@` in the case of a group.
+* _<permissions>_ is a string containing any or all of "RWXCA", though only R and W are meaningful at cell scope.
+* _<scanner-specification>_ is the scanner specification syntax and conventions used by the 'scan' shell command.
+ For some examples of scanner specifications, issue the following HBase Shell command.
++
+----
+hbase> help "scan"
+----
+
++
+This example grants read access to the 'testuser' user and read/write access to the 'developers' group, on cells in the 'pii' column which match the filter.
++
+----
+hbase> grant 'user', \
+ { '@developers' => 'RW', 'testuser' => 'R' }, \
+ { COLUMNS => 'pii', FILTER => "(PrefixFilter ('test'))" }
+----
++
+The shell will run a scanner with the given criteria, rewrite the found cells with new ACLs, and store them back to their exact coordinates.
+
+====
++
+.API
+====
+The following example shows how to grant access at the table level.
+
+[source,java]
+----
+public static void grantOnTable(final HBaseTestingUtility util, final String user,
+ final TableName table, final byte[] family, final byte[] qualifier,
+ final Permission.Action... actions) throws Exception {
+ SecureTestUtil.updateACLs(util, new Callable<Void>() {
+ @Override
+ public Void call() throws Exception {
+ Configuration conf = HBaseConfiguration.create();
+ Connection connection = ConnectionFactory.createConnection(conf);
+ try (Connection connection = ConnectionFactory.createConnection(conf)) {
+ try (Table table = connection.getTable(TableName.valueOf(tablename)) {
+ AccessControlLists.ACL_TABLE_NAME);
+ try {
+ BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
+ AccessControlService.BlockingInterface protocol =
+ AccessControlService.newBlockingStub(service);
+ ProtobufUtil.grant(protocol, user, table, family, qualifier, actions);
+ } finally {
+ acl.close();
+ }
+ return null;
+ }
+ }
+ }
+ }
+}
+----
+
+To grant permissions at the cell level, you can use the `Mutation.setACL` method:
+
+[source,java]
+----
+Mutation.setACL(String user, Permission perms)
+Mutation.setACL(Map<String, Permission> perms)
+----
+
+Specifically, this example provides read permission to a user called `user1` on any cells contained in a particular Put operation:
+
+[source,java]
+----
+put.setACL(“user1”, new Permission(Permission.Action.READ))
+----
+====
+
+. Revoking Access Control From a Namespace, Table, Column Family, or Cell
++
+The `revoke` command and API are twins of the grant command and API, and the syntax is exactly the same.
+The only exception is that you cannot revoke permissions at the cell level.
+You can only revoke access that has previously been granted, and a `revoke` statement is not the same thing as explicit denial to a resource.
++
+NOTE: HBase Shell support for granting and revoking access is for testing and verification support, and should not be employed for production use because it won't apply the permissions to cells that don't exist yet.
+The correct way to apply cell-level permissions is to do so in the application code when storing the values.
++
+.Revoking Access To a Table
+====
+[source,java]
+----
+public static void revokeFromTable(final HBaseTestingUtility util, final String user,
+ final TableName table, final byte[] family, final byte[] qualifier,
+ final Permission.Action... actions) throws Exception {
+ SecureTestUtil.updateACLs(util, new Callable<Void>() {
+ @Override
+ public Void call() throws Exception {
+ Configuration conf = HBaseConfiguration.create();
+ Connection connection = ConnectionFactory.createConnection(conf);
+ Table acl = connection.getTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME);
+ try {
+ BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
+ AccessControlService.BlockingInterface protocol =
+ AccessControlService.newBlockingStub(service);
+ ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions);
+ } finally {
+ acl.close();
+ }
+ return null;
+ }
+ });
+}
+----
+====
+
+. Showing a User's Effective Permissions
++
+.HBase Shell
+====
+----
+hbase> user_permission 'user'
+
+hbase> user_permission '.*'
+
+hbase> user_permission JAVA_REGEX
+----
+====
+
+.API
+====
+[source,java]
+----
+public static void verifyAllowed(User user, AccessTestAction action, int count) throws Exception {
+ try {
+ Object obj = user.runAs(action);
+ if (obj != null && obj instanceof List<?>) {
+ List<?> results = (List<?>) obj;
+ if (results != null && results.isEmpty()) {
+ fail("Empty non null results from action for user '" ` user.getShortName() ` "'");
+ }
+ assertEquals(count, results.size());
+ }
+ } catch (AccessDeniedException ade) {
+ fail("Expected action to pass for user '" ` user.getShortName() ` "' but was denied");
+ }
+}
+----
+====
+
+
+=== Visibility Labels
+
+Visibility labels control can be used to only permit users or principals associated with a given label to read or access cells with that label.
+For instance, you might label a cell `top-secret`, and only grant access to that label to the `managers` group.
+Visibility labels are implemented using Tags, which are a feature of HFile v3, and allow you to store metadata on a per-cell basis.
+A label is a string, and labels can be combined into expressions by using logical operators (&, |, or !), and using parentheses for grouping.
+HBase does not do any kind of validation of expressions beyond basic well-formedness.
+Visibility labels have no meaning on their own, and may be used to denote sensitivity level, privilege level, or any other arbitrary semantic meaning.
+
+If a user's labels do not match a cell's label or expression, the user is denied access to the cell.
+
+In HBase 0.98.6 and newer, UTF-8 encoding is supported for visibility labels and expressions.
+When creating labels using the `addLabels(conf, labels)` method provided by the `org.apache.hadoop.hbase.security.visibility.VisibilityClient` class and passing labels in Authorizations via Scan or Get, labels can contain UTF-8 characters, as well as the logical operators normally used in visibility labels, with normal Java notations, without needing any escaping method.
+However, when you pass a CellVisibility expression via a Mutation, you must enclose the expression with the `CellVisibility.quote()` method if you use UTF-8 characters or logical operators.
+See `TestExpressionParser` and the source file _hbase-client/src/test/java/org/apache/hadoop/hbase/client/TestScan.java_.
+
+A user adds visibility expressions to a cell during a Put operation.
+In the default configuration, the user does not need to have access to a label in order to label cells with it.
+This behavior is controlled by the configuration option `hbase.security.visibility.mutations.checkauths`.
+If you set this option to `true`, the labels the user is modifying as part of the mutation must be associated with the user, or the mutation will fail.
+Whether a user is authorized to read a labelled cell is determined during a Get or Scan, and results which the user is not allowed to read are filtered out.
+This incurs the same I/O penalty as if the results were returned, but reduces load on the network.
+
+Visibility labels can also be specified during Delete operations.
+For details about visibility labels and Deletes, see link:https://issues.apache.org/jira/browse/HBASE-10885[HBASE-10885].
+
+The user's effective label set is built in the RPC context when a request is first received by the RegionServer.
+The way that users are associated with labels is pluggable.
+The default plugin passes through labels specified in Authorizations added to the Get or Scan and checks those against the calling user's authenticated labels list.
+When the client passes labels for which the user is not authenticated, the default plugin drops them.
+You can pass a subset of user authenticated labels via the `Get#setAuthorizations(Authorizations(String,...))` and `Scan#setAuthorizations(Authorizations(String,...));` methods.
+
+Groups can be granted visibility labels the same way as users. Groups are prefixed with an @ symbol. When checking visibility labels of a user, the server will include the visibility labels of the groups of which the user is a member, together with the user's own labels.
+When the visibility labels are retrieved using API `VisibilityClient#getAuths` or Shell command `get_auths` for a user, we will return labels added specifically for that user alone, not the group level labels.
+
+Visibility label access checking is performed by the VisibilityController coprocessor.
+You can use interface `VisibilityLabelService` to provide a custom implementation and/or control the way that visibility labels are stored with cells.
+See the source file _hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabelsWithCustomVisLabService.java_ for one example.
+
+Visibility labels can be used in conjunction with ACLs.
+
+NOTE: The labels have to be explicitly defined before they can be used in visibility labels. See below for an example of how this can be done.
+
+NOTE: There is currently no way to determine which labels have been applied to a cell. See link:https://issues.apache.org/jira/browse/HBASE-12470[HBASE-12470] for details.
+
+NOTE: Visibility labels are not currently applied for superusers.
+
+.Examples of Visibility Expressions
+[cols="l,1", options="header"]
+|===
+| Expression
+| Interpretation
+
+| fulltime
+| Allow accesss to users associated with the fulltime label.
+
+| !public
+| Allow access to users not associated with the public label.
+
+| ( secret \| topsecret ) & !probationary
+| Allow access to users associated with either the secret or topsecret label and not associated with the probationary label.
+|===
+
+==== Server-Side Configuration
+
+
+. As a prerequisite, perform the steps in <<security.data.basic.server.side>>.
+. Install and configure the VisibilityController coprocessor by setting the following properties in _hbase-site.xml_.
+ These properties take a list of class names.
++
+[source,xml]
+----
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
+</property>
+----
++
+NOTE: If you use the AccessController and VisibilityController coprocessors together, the AccessController must come first in the list, because with both components active, the VisibilityController will delegate access control on its system tables to the AccessController.
+
+. Adjust Configuration
++
+By default, users can label cells with any label, including labels they are not associated with, which means that a user can Put data that he cannot read.
+For example, a user could label a cell with the (hypothetical) 'topsecret' label even if the user is not associated with that label.
+If you only want users to be able to label cells with labels they are associated with, set `hbase.security.visibility.mutations.checkauths` to `true`.
+In that case, the mutation will fail if it makes use of labels the user is not associated with.
+
+. Distribute your configuration and restart your cluster for changes to take effect.
+
+==== Administration
+
+Administration tasks can be performed using the HBase Shell or the Java API.
+For defining the list of visibility labels and associating labels with users, the HBase Shell is probably simpler.
+
+.API Examples
+[CAUTION]
+====
+Many of the Java API examples in this section are taken from the source file _hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabels.java_.
+Refer to that file or the API documentation for more context.
+
+Neither these examples, nor the source file they were taken from, are part of the public HBase API, and are provided for illustration only.
+Refer to the official API for usage instructions.
+====
+
+
+. Define the List of Visibility Labels
++
+.HBase Shell
+====
+----
+hbase> add_labels [ 'admin', 'service', 'developer', 'test' ]
+----
+====
++
+.Java API
+====
+[source,java]
+----
+public static void addLabels() throws Exception {
+ PrivilegedExceptionAction<VisibilityLabelsResponse> action = new PrivilegedExceptionAction<VisibilityLabelsResponse>() {
+ public VisibilityLabelsResponse run() throws Exception {
+ String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE, COPYRIGHT, ACCENT,
+ UNICODE_VIS_TAG, UC1, UC2 };
+ try {
+ VisibilityClient.addLabels(conf, labels);
+ } catch (Throwable t) {
+ throw new IOException(t);
+ }
+ return null;
+ }
+ };
+ SUPERUSER.runAs(action);
+}
+----
+====
+
+. Associate Labels with Users
++
+.HBase Shell
+====
+----
+hbase> set_auths 'service', [ 'service' ]
+----
+
+----
+hbase> set_auths 'testuser', [ 'test' ]
+----
+
+----
+hbase> set_auths 'qa', [ 'test', 'developer' ]
+----
+
+----
+hbase> set_auths '@qagroup', [ 'test' ]
+----
+====
++
+.Java API
+====
+[source,java]
+----
+public void testSetAndGetUserAuths() throws Throwable {
+ final String user = "user1";
+ PrivilegedExceptionAction<Void> action = new PrivilegedExceptionAction<Void>() {
+ public Void run() throws Exception {
+ String[] auths = { SECRET, CONFIDENTIAL };
+ try {
+ VisibilityClient.setAuths(conf, auths, user);
+ } catch (Throwable e) {
+ }
+ return null;
+ }
+ ...
+----
+====
+
+. Clear Labels From Users
++
+.HBase Shell
+====
+----
+hbase> clear_auths 'service', [ 'service' ]
+----
+
+----
+hbase> clear_auths 'testuser', [ 'test' ]
+----
+
+----
+hbase> clear_auths 'qa', [ 'test', 'developer' ]
+----
+
+----
+hbase> clear_auths '@qagroup', [ 'test', 'developer' ]
+----
+====
++
+.Java API
+====
+[source,java]
+----
+...
+auths = new String[] { SECRET, PUBLIC, CONFIDENTIAL };
+VisibilityLabelsResponse response = null;
+try {
+ response = VisibilityClient.clearAuths(conf, auths, user);
+} catch (Throwable e) {
+ fail("Should not have failed");
+ ...
+}
+----
+====
+
+. Apply a Label or Expression to a Cell
++
+The label is only applied when data is written.
+The label is associated with a given version of the cell.
++
+.HBase Shell
+====
+----
+hbase> set_visibility 'user', 'admin|service|developer', { COLUMNS => 'i' }
+----
+
+----
+hbase> set_visibility 'user', 'admin|service', { COLUMNS => 'pii' }
+----
+
+----
+hbase> set_visibility 'user', 'test', { COLUMNS => [ 'i', 'pii' ], FILTER => "(PrefixFilter ('test'))" }
+----
+====
++
+NOTE: HBase Shell support for applying labels or permissions to cells is for testing and verification support, and should not be employed for production use because it won't apply the labels to cells that don't exist yet.
+The correct way to apply cell level labels is to do so in the application code when storing the values.
++
+.Java API
+====
+[source,java]
+----
+static Table createTableAndWriteDataWithLabels(TableName tableName, String... labelExps)
+ throws Exception {
+ Configuration conf = HBaseConfiguration.create();
+ Connection connection = ConnectionFactory.createConnection(conf);
+ Table table = NULL;
+ try {
+ table = TEST_UTIL.createTable(tableName, fam);
+ int i = 1;
+ List<Put> puts = new ArrayList<Put>();
+ for (String labelExp : labelExps) {
+ Put put = new Put(Bytes.toBytes("row" + i));
+ put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value);
+ put.setCellVisibility(new CellVisibility(labelExp));
+ puts.add(put);
+ i++;
+ }
+ table.put(puts);
+ } finally {
+ if (table != null) {
+ table.flushCommits();
+ }
+ }
+----
+====
+
+<<reading_cells_with_labels>>
+==== Reading Cells with Labels
+When you issue a Scan or Get, HBase uses your default set of authorizations to filter out cells that you do not have access to. A superuser can set the default set of authorizations for a given user by using the `set_auths` HBase Shell command or the link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/security/visibility/VisibilityClient.html#setAuths(org.apache.hadoop.conf.Configuration,%20java.lang.String\[\],%20java.lang.String)[VisibilityClient.setAuths()] method.
+
+You can specify a different authorization during the Scan or Get, by passing the AUTHORIZATIONS option in HBase Shell, or the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setAuthorizations%28org.apache.hadoop.hbase.security.visibility.Authorizations%29[setAuthorizations()] method if you use the API. This authorization will be combined with your default set as an additional filter. It will further filter your results, rather than giving you additional authorization.
+
+.HBase Shell
+====
+----
+hbase> get_auths 'myUser'
+hbase> scan 'table1', AUTHORIZATIONS => ['private']
+----
+====
+
+.Java API
+====
+[source,java]
+----
+...
+public Void run() throws Exception {
+ String[] auths1 = { SECRET, CONFIDENTIAL };
+ GetAuthsResponse authsResponse = null;
+ try {
+ VisibilityClient.setAuths(conf, auths1, user);
+ try {
+ authsResponse = VisibilityClient.getAuths(conf, user);
+ } catch (Throwable e) {
+ fail("Should not have failed");
+ }
+ } catch (Throwable e) {
+ }
+ List<String> authsList = new ArrayList<String>();
+ for (ByteString authBS : authsResponse.getAuthList()) {
+ authsList.add(Bytes.toString(authBS.toByteArray()));
+ }
+ assertEquals(2, authsList.size());
+ assertTrue(authsList.contains(SECRET));
+ assertTrue(authsList.contains(CONFIDENTIAL));
+ return null;
+}
+...
+----
+====
+
+
+
+==== Implementing Your Own Visibility Label Algorithm
+
+Interpreting the labels authenticated for a given get/scan request is a pluggable algorithm.
+
+You can specify a custom plugin or plugins by using the property `hbase.regionserver.scan.visibility.label.generator.class`. The output for the first `ScanLabelGenerator` will be the input for the next one, until the end of the list.
+
+The default implementation, which was implemented in link:https://issues.apache.org/jira/browse/HBASE-12466[HBASE-12466], loads two plugins, `FeedUserAuthScanLabelGenerator` and `DefinedSetFilterScanLabelGenerator`. See <<reading_cells_with_labels>>.
+
+==== Replicating Visibility Tags as Strings
+
+As mentioned in the above sections, the interface `VisibilityLabelService` could be used to implement a different way of storing the visibility expressions in the cells. Clusters with replication enabled also must replicate the visibility expressions to the peer cluster. If `DefaultVisibilityLabelServiceImpl` is used as the implementation for `VisibilityLabelService`, all the visibility expression are converted to the corresponding expression based on the ordinals for each visibility label stored in the labels table. During replication, visible cells are also replicated with the ordinal-based expression intact. The peer cluster may not have the same `labels` table with the same ordinal mapping for the visibility labels. In that case, replicating the ordinals makes no sense. It would be better if the replication occurred with the visibility expressions transmitted as strings. To replicate the visibility expression as strings to the peer cluster, create a `RegionServerObserver` config
uration which works based on the implementation of the `VisibilityLabelService` interface. The configuration below enables replication of visibility expressions to peer clusters as strings. See link:https://issues.apache.org/jira/browse/HBASE-11639[HBASE-11639] for more details.
+
+[source,xml]
+----
+<property>
+ <name>hbase.coprocessor.regionserver.classes</name>
+ <value>org.apache.hadoop.hbase.security.visibility.VisibilityController$VisibilityReplication</value>
+</property>
+----
+
+[[hbase.encryption.server]]
+=== Transparent Encryption of Data At Rest
+
+HBase provides a mechanism for protecting your data at rest, in HFiles and the WAL, which reside within HDFS or another distributed filesystem.
+A two-tier architecture is used for flexible and non-intrusive key rotation.
+"Transparent" means that no implementation changes are needed on the client side.
+When data is written, it is encrypted.
+When it is read, it is decrypted on demand.
+
+==== How It Works
+
+The administrator provisions a master key for the cluster, which is stored in a key provider accessible to every trusted HBase process, including the HMaster, RegionServers, and clients (such as HBase Shell) on administrative workstations.
+The default key provider is integrated with the Java KeyStore API and any key management systems with support for it.
+Other custom key provider implementations are possible.
+The key retrieval mechanism is configured in the _hbase-site.xml_ configuration file.
+The master key may be stored on the cluster servers, protected by a secure KeyStore file, or on an external keyserver, or in a hardware security module.
+This master key is resolved as needed by HBase processes through the configured key provider.
+
+Next, encryption use can be specified in the schema, per column family, by creating or modifying a column descriptor to include two additional attributes: the name of the encryption algorithm to use (currently only "AES" is supported), and optionally, a data key wrapped (encrypted) with the cluster master key.
+If a data key is not explicitly configured for a ColumnFamily, HBase will create a random data key per HFile.
+This provides an incremental improvement in security over the alternative.
+Unless you need to supply an explicit data key, such as in a case where you are generating encrypted HFiles for bulk import with a given data key, only specify the encryption algorithm in the ColumnFamily schema metadata and let HBase create data keys on demand.
+Per Column Family keys facilitate low impact incremental key rotation and reduce the scope of any external leak of key material.
+The wrapped data key is stored in the ColumnFamily schema metadata, and in each HFile for the Column Family, encrypted with the cluster master key.
+After the Column Family is configured for encryption, any new HFiles will be written encrypted.
+To ensure encryption of all HFiles, trigger a major compaction after enabling this feature.
+
+When the HFile is opened, the data key is extracted from the HFile, decrypted with the cluster master key, and used for decryption of the remainder of the HFile.
+The HFile will be unreadable if the master key is not available.
+If a remote user somehow acquires access to the HFile data because of some lapse in HDFS permissions, or from inappropriately discarded media, it will not be possible to decrypt either the data key or the file data.
+
+It is also possible to encrypt the WAL.
+Even though WALs are transient, it is necessary to encrypt the WALEdits to avoid circumventing HFile protections for encrypted column families, in the event that the underlying filesystem is compromised.
+When WAL encryption is enabled, all WALs are encrypted, regardless of whether the relevant HFiles are encrypted.
+
+==== Server-Side Configuration
+
+This procedure assumes you are using the default Java keystore implementation.
+If you are using a custom implementation, check its documentation and adjust accordingly.
+
+
+. Create a secret key of appropriate length for AES encryption, using the
+ `keytool` utility.
++
+[source,bash]
+----
+$ keytool -keystore /path/to/hbase/conf/hbase.jks \
+ -storetype jceks -storepass **** \
+ -genseckey -keyalg AES -keysize 128 \
+ -alias <alias>
+----
++
+Replace [replaceable]_****_ with the password for the keystore file and <alias> with the username of the HBase service account, or an arbitrary string.
+If you use an arbitrary string, you will need to configure HBase to use it, and that is covered below.
+Specify a keysize that is appropriate.
+Do not specify a separate password for the key, but press kbd:[Return] when prompted.
+
+. Set appropriate permissions on the keyfile and distribute it to all the HBase
+ servers.
++
+The previous command created a file called _hbase.jks_ in the HBase _conf/_ directory.
+Set the permissions and ownership on this file such that only the HBase service account user can read the file, and securely distribute the key to all HBase servers.
+
+. Configure the HBase daemons.
++
+Set the following properties in _hbase-site.xml_ on the region servers, to configure HBase daemons to use a key provider backed by the KeyStore file or retrieving the cluster master key.
+In the example below, replace [replaceable]_****_ with the password.
++
+[source,xml]
+----
+<property>
+ <name>hbase.crypto.keyprovider</name>
+ <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
+</property>
+<property>
+ <name>hbase.crypto.keyprovider.parameters</name>
+ <value>jceks:///path/to/hbase/conf/hbase.jks?password=****</value>
+</property>
+----
++
+By default, the HBase service account name will be used to resolve the cluster master key.
+However, you can store it with an arbitrary alias (in the `keytool` command). In that case, set the following property to the alias you used.
++
+[source,xml]
+----
+<property>
+ <name>hbase.crypto.master.key.name</name>
+ <value>my-alias</value>
+</property>
+----
++
+You also need to be sure your HFiles use HFile v3, in order to use transparent encryption.
+This is the default configuration for HBase 1.0 onward.
+For previous versions, set the following property in your _hbase-site.xml_ file.
++
+[source,xml]
+----
+<property>
+ <name>hfile.format.version</name>
+ <value>3</value>
+</property>
+----
++
+Optionally, you can use a different cipher provider, either a Java Cryptography Encryption (JCE) algorithm provider or a custom HBase cipher implementation.
++
+* JCE:
+** Install a signed JCE provider (supporting `AES/CTR/NoPadding` mode with 128 bit keys)
+** Add it with highest preference to the JCE site configuration file _$JAVA_HOME/lib/security/java.security_.
+** Update `hbase.crypto.algorithm.aes.provider` and `hbase.crypto.algorithm.rng.provider` options in [path]_hbase-site.xml_.
+
+* Custom HBase Cipher:
+** Implement `org.apache.hadoop.hbase.io.crypto.CipherProvider`.
+** Add the implementation to the server classpath.
+** Update `hbase.crypto.cipherprovider` in _hbase-site.xml_.
+
+
+. Configure WAL encryption.
++
+Configure WAL encryption in every RegionServer's _hbase-site.xml_, by setting the following properties.
+You can include these in the HMaster's _hbase-site.xml_ as well, but the HMaster does not have a WAL and will not use them.
++
+[source,xml]
+----
+<property>
+ <name>hbase.regionserver.hlog.reader.impl</name>
+ <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
+</property>
+<property>
+ <name>hbase.regionserver.hlog.writer.impl</name>
+ <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
+</property>
+<property>
+ <name>hbase.regionserver.wal.encryption</name>
+ <value>true</value>
+</property>
+----
+
+. Configure permissions on the _hbase-site.xml_ file.
++
+Because the keystore password is stored in the hbase-site.xml, you need to ensure that only the HBase user can read the _hbase-site.xml_ file, using file ownership and permissions.
+
+. Restart your cluster.
++
+Distribute the new configuration file to all nodes and restart your cluster.
+
+
+==== Administration
+
+Administrative tasks can be performed in HBase Shell or the Java API.
+
+.Java API
+[CAUTION]
+====
+Java API examples in this section are taken from the source file _hbase-server/src/test/java/org/apache/hadoop/hbase/util/TestHBaseFsckEncryption.java_.
+.
+
+Neither these examples, nor the source files they are taken from, are part of the public HBase API, and are provided for illustration only.
+Refer to the official API for usage instructions.
+====
+
+Enable Encryption on a Column Family::
+ To enable encryption on a column family, you can either use HBase Shell or the Java API.
+ After enabling encryption, trigger a major compaction.
+ When the major compaction completes, the HFiles will be encrypted.
+
+Rotate the Data Key::
+ To rotate the data key, first change the ColumnFamily key in the column descriptor, then trigger a major compaction.
+ When compaction is complete, all HFiles will be re-encrypted using the new data key.
+ Until the compaction completes, the old HFiles will still be readable using the old key.
+
+Switching Between Using a Random Data Key and Specifying A Key::
+ If you configured a column family to use a specific key and you want to return to the default behavior of using a randomly-generated key for that column family, use the Java API to alter the `HColumnDescriptor` so that no value is sent with the key `ENCRYPTION_KEY`.
+
+Rotate the Master Key::
+ To rotate the master key, first generate and distribute the new key.
+ Then update the KeyStore to contain a new master key, and keep the old master key in the KeyStore using a different alias.
+ Next, configure fallback to the old master key in the _hbase-site.xml_ file.
+
+::
+
+[[hbase.secure.bulkload]]
+=== Secure Bulk Load
+
+Bulk loading in secure mode is a bit more involved than normal setup, since the client has to transfer the ownership of the files generated from the MapReduce job to HBase.
+Secure bulk loading is implemented by a coprocessor, named link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/security/access/SecureBulkLoadEndpoint.html[SecureBulkLoadEndpoint], which uses a staging directory configured by the configuration property `hbase.bulkload.staging.dir`, which defaults to _/tmp/hbase-staging/_.
+
+.Secure Bulk Load Algorithm
+
+* One time only, create a staging directory which is world-traversable and owned by the user which runs HBase (mode 711, or `rwx--x--x`). A listing of this directory will look similar to the following:
++
+[source,bash]
+----
+$ ls -ld /tmp/hbase-staging
+drwx--x--x 2 hbase hbase 68 3 Sep 14:54 /tmp/hbase-staging
+----
+
+* A user writes out data to a secure output directory owned by that user.
+ For example, _/user/foo/data_.
+* Internally, HBase creates a secret staging directory which is globally readable/writable (`-rwxrwxrwx, 777`). For example, _/tmp/hbase-staging/averylongandrandomdirectoryname_.
+ The name and location of this directory is not exposed to the user.
+ HBase manages creation and deletion of this directory.
+* The user makes the data world-readable and world-writable, moves it into the random staging directory, then calls the `SecureBulkLoadClient#bulkLoadHFiles` method.
+
+The strength of the security lies in the length and randomness of the secret directory.
+
+To enable secure bulk load, add the following properties to _hbase-site.xml_.
+
+[source,xml]
+----
+<property>
+ <name>hbase.bulkload.staging.dir</name>
+ <value>/tmp/hbase-staging</value>
+</property>
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.token.TokenProvider,
+ org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
+</property>
+----
+
+[[security.example.config]]
+== Security Configuration Example
+
+This configuration example includes support for HFile v3, ACLs, Visibility Labels, and transparent encryption of data at rest and the WAL.
+All options have been discussed separately in the sections above.
+
+.Example Security Settings in _hbase-site.xml_
+====
+[source,xml]
+----
+<!-- HFile v3 Support -->
+<property>
+ <name>hfile.format.version</name>
+ <value>3</value>
+</property>
+<!-- HBase Superuser -->
+<property>
+ <name>hbase.superuser</name>
+ <value>hbase, admin</value>
+</property>
+<!-- Coprocessors for ACLs and Visibility Tags -->
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController,
+ org.apache.hadoop.hbase.security.visibility.VisibilityController,
+ org.apache.hadoop.hbase.security.token.TokenProvider</value>
+</property>
+<property>
+ <name>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.security.access.AccessController,
+ org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
+</property>
+<property>
+ <name>hbase.coprocessor.regionserver.classes</name>
+ <value>org.apache.hadoop/hbase.security.access.AccessController,
+ org.apache.hadoop.hbase.security.access.VisibilityController</value>
+</property>
+<!-- Executable ACL for Coprocessor Endpoints -->
+<property>
+ <name>hbase.security.exec.permission.checks</name>
+ <value>true</value>
+</property>
+<!-- Whether a user needs authorization for a visibility tag to set it on a cell -->
+<property>
+ <name>hbase.security.visibility.mutations.checkauth</name>
+ <value>false</value>
+</property>
+<!-- Secure RPC Transport -->
+<property>
+ <name>hbase.rpc.protection</name>
+ <value>auth-conf</value>
+ </property>
+ <!-- Transparent Encryption -->
+<property>
+ <name>hbase.crypto.keyprovider</name>
+ <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
+</property>
+<property>
+ <name>hbase.crypto.keyprovider.parameters</name>
+ <value>jceks:///path/to/hbase/conf/hbase.jks?password=***</value>
+</property>
+<property>
+ <name>hbase.crypto.master.key.name</name>
+ <value>hbase</value>
+</property>
+<!-- WAL Encryption -->
+<property>
+ <name>hbase.regionserver.hlog.reader.impl</name>
+ <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
+</property>
+<property>
+ <name>hbase.regionserver.hlog.writer.impl</name>
+ <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
+</property>
+<property>
+ <name>hbase.regionserver.wal.encryption</name>
+ <value>true</value>
+</property>
+<!-- For key rotation -->
+<property>
+ <name>hbase.crypto.master.alternate.key.name</name>
+ <value>hbase.old</value>
+</property>
+<!-- Secure Bulk Load -->
+<property>
+ <name>hbase.bulkload.staging.dir</name>
+ <value>/tmp/hbase-staging</value>
+</property>
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.security.token.TokenProvider,
+ org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
+</property>
+----
+====
+
+.Example Group Mapper in Hadoop _core-site.xml_
+====
+Adjust these settings to suit your environment.
+
+[source,xml]
+----
+<property>
+ <name>hadoop.security.group.mapping</name>
+ <value>org.apache.hadoop.security.LdapGroupsMapping</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.url</name>
+ <value>ldap://server</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.bind.user</name>
+ <value>Administrator@example-ad.local</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.bind.password</name>
+ <value>****</value> <!-- Replace with the actual password -->
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.base</name>
+ <value>dc=example-ad,dc=local</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
+ <value>(&(objectClass=user)(sAMAccountName={0}))</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
+ <value>(objectClass=group)</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
+ <value>member</value>
+</property>
+<property>
+ <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
+ <value>cn</value>
+</property>
+----
+====
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/shell.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/shell.adoc b/src/main/asciidoc/_chapters/shell.adoc
new file mode 100644
index 0000000..237089e
--- /dev/null
+++ b/src/main/asciidoc/_chapters/shell.adoc
@@ -0,0 +1,390 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[shell]]
+= The Apache HBase Shell
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+
+The Apache HBase Shell is link:http://jruby.org[(J)Ruby]'s IRB with some HBase particular commands added.
+Anything you can do in IRB, you should be able to do in the HBase Shell.
+
+To run the HBase shell, do as follows:
+
+[source,bash]
+----
+$ ./bin/hbase shell
+----
+
+Type `help` and then `<RETURN>` to see a listing of shell commands and options.
+Browse at least the paragraphs at the end of the help output for the gist of how variables and command arguments are entered into the HBase shell; in particular note how table names, rows, and columns, etc., must be quoted.
+
+See <<shell_exercises,shell exercises>> for example basic shell operation.
+
+Here is a nicely formatted listing of link:http://learnhbase.wordpress.com/2013/03/02/hbase-shell-commands/[all shell
+ commands] by Rajeshbabu Chintaguntla.
+
+[[scripting]]
+== Scripting with Ruby
+
+For examples scripting Apache HBase, look in the HBase _bin_ directory.
+Look at the files that end in _*.rb_.
+To run one of these files, do as follows:
+
+[source,bash]
+----
+$ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT
+----
+
+== Running the Shell in Non-Interactive Mode
+
+A new non-interactive mode has been added to the HBase Shell (link:https://issues.apache.org/jira/browse/HBASE-11658[HBASE-11658)].
+Non-interactive mode captures the exit status (success or failure) of HBase Shell commands and passes that status back to the command interpreter.
+If you use the normal interactive mode, the HBase Shell will only ever return its own exit status, which will nearly always be `0` for success.
+
+To invoke non-interactive mode, pass the `-n` or `--non-interactive` option to HBase Shell.
+
+[[hbase.shell.noninteractive]]
+== HBase Shell in OS Scripts
+
+You can use the HBase shell from within operating system script interpreters like the Bash shell which is the default command interpreter for most Linux and UNIX distributions.
+The following guidelines use Bash syntax, but could be adjusted to work with C-style shells such as csh or tcsh, and could probably be modified to work with the Microsoft Windows script interpreter as well. Submissions are welcome.
+
+NOTE: Spawning HBase Shell commands in this way is slow, so keep that in mind when you are deciding when combining HBase operations with the operating system command line is appropriate.
+
+.Passing Commands to the HBase Shell
+====
+You can pass commands to the HBase Shell in non-interactive mode (see <<hbasee.shell.noninteractive,hbasee.shell.noninteractive>>) using the `echo` command and the `|` (pipe) operator.
+Be sure to escape characters in the HBase commands which would otherwise be interpreted by the shell.
+Some debug-level output has been truncated from the example below.
+
+[source,bash]
+----
+$ echo "describe 'test1'" | ./hbase shell -n
+
+Version 0.98.3-hadoop2, rd5e65a9144e315bb0a964e7730871af32f5018d5, Sat May 31 19:56:09 PDT 2014
+
+describe 'test1'
+
+DESCRIPTION ENABLED
+ 'test1', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NON true
+ E', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
+ VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIO
+ NS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =>
+ 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false'
+ , BLOCKCACHE => 'true'}
+1 row(s) in 3.2410 seconds
+----
+
+To suppress all output, echo it to _/dev/null:_
+
+[source,bash]
+----
+$ echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
+----
+====
+
+.Checking the Result of a Scripted Command
+====
+Since scripts are not designed to be run interactively, you need a way to check whether your command failed or succeeded.
+The HBase shell uses the standard convention of returning a value of `0` for successful commands, and some non-zero value for failed commands.
+Bash stores a command's return value in a special environment variable called `$?`.
+Because that variable is overwritten each time the shell runs any command, you should store the result in a different, script-defined variable.
+
+This is a naive script that shows one way to store the return value and make a decision based upon it.
+
+[source,bash]
+----
+#!/bin/bash
+
+echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
+status=$?
+echo "The status was " $status
+if ($status == 0); then
+ echo "The command succeeded"
+else
+ echo "The command may have failed."
+fi
+return $status
+----
+====
+
+=== Checking for Success or Failure In Scripts
+
+Getting an exit code of `0` means that the command you scripted definitely succeeded.
+However, getting a non-zero exit code does not necessarily mean the command failed.
+The command could have succeeded, but the client lost connectivity, or some other event obscured its success.
+This is because RPC commands are stateless.
+The only way to be sure of the status of an operation is to check.
+For instance, if your script creates a table, but returns a non-zero exit value, you should check whether the table was actually created before trying again to create it.
+
+== Read HBase Shell Commands from a Command File
+
+You can enter HBase Shell commands into a text file, one command per line, and pass that file to the HBase Shell.
+
+.Example Command File
+====
+----
+create 'test', 'cf'
+list 'test'
+put 'test', 'row1', 'cf:a', 'value1'
+put 'test', 'row2', 'cf:b', 'value2'
+put 'test', 'row3', 'cf:c', 'value3'
+put 'test', 'row4', 'cf:d', 'value4'
+scan 'test'
+get 'test', 'row1'
+disable 'test'
+enable 'test'
+----
+====
+
+.Directing HBase Shell to Execute the Commands
+====
+Pass the path to the command file as the only argument to the `hbase shell` command.
+Each command is executed and its output is shown.
+If you do not include the `exit` command in your script, you are returned to the HBase shell prompt.
+There is no way to programmatically check each individual command for success or failure.
+Also, though you see the output for each command, the commands themselves are not echoed to the screen so it can be difficult to line up the command with its output.
+
+[source,bash]
+----
+$ ./hbase shell ./sample_commands.txt
+0 row(s) in 3.4170 seconds
+
+TABLE
+test
+1 row(s) in 0.0590 seconds
+
+0 row(s) in 0.1540 seconds
+
+0 row(s) in 0.0080 seconds
+
+0 row(s) in 0.0060 seconds
+
+0 row(s) in 0.0060 seconds
+
+ROW COLUMN+CELL
+ row1 column=cf:a, timestamp=1407130286968, value=value1
+ row2 column=cf:b, timestamp=1407130286997, value=value2
+ row3 column=cf:c, timestamp=1407130287007, value=value3
+ row4 column=cf:d, timestamp=1407130287015, value=value4
+4 row(s) in 0.0420 seconds
+
+COLUMN CELL
+ cf:a timestamp=1407130286968, value=value1
+1 row(s) in 0.0110 seconds
+
+0 row(s) in 1.5630 seconds
+
+0 row(s) in 0.4360 seconds
+----
+====
+
+== Passing VM Options to the Shell
+
+You can pass VM options to the HBase Shell using the `HBASE_SHELL_OPTS` environment variable.
+You can set this in your environment, for instance by editing _~/.bashrc_, or set it as part of the command to launch HBase Shell.
+The following example sets several garbage-collection-related variables, just for the lifetime of the VM running the HBase Shell.
+The command should be run all on a single line, but is broken by the `\` character, for readability.
+
+[source,bash]
+----
+$ HBASE_SHELL_OPTS="-verbose:gc -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCDateStamps \
+ -XX:+PrintGCDetails -Xloggc:$HBASE_HOME/logs/gc-hbase.log" ./bin/hbase shell
+----
+
+== Shell Tricks
+
+=== Table variables
+
+HBase 0.95 adds shell commands that provides jruby-style object-oriented references for tables.
+Previously all of the shell commands that act upon a table have a procedural style that always took the name of the table as an argument.
+HBase 0.95 introduces the ability to assign a table to a jruby variable.
+The table reference can be used to perform data read write operations such as puts, scans, and gets well as admin functionality such as disabling, dropping, describing tables.
+
+For example, previously you would always specify a table name:
+
+----
+hbase(main):000:0> create ‘t’, ‘f’
+0 row(s) in 1.0970 seconds
+hbase(main):001:0> put 't', 'rold', 'f', 'v'
+0 row(s) in 0.0080 seconds
+
+hbase(main):002:0> scan 't'
+ROW COLUMN+CELL
+ rold column=f:, timestamp=1378473207660, value=v
+1 row(s) in 0.0130 seconds
+
+hbase(main):003:0> describe 't'
+DESCRIPTION ENABLED
+ 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true
+ SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
+ 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
+ ', BLOCKCACHE => 'true'}
+1 row(s) in 1.4430 seconds
+
+hbase(main):004:0> disable 't'
+0 row(s) in 14.8700 seconds
+
+hbase(main):005:0> drop 't'
+0 row(s) in 23.1670 seconds
+
+hbase(main):006:0>
+----
+
+Now you can assign the table to a variable and use the results in jruby shell code.
+
+----
+hbase(main):007 > t = create 't', 'f'
+0 row(s) in 1.0970 seconds
+
+=> Hbase::Table - t
+hbase(main):008 > t.put 'r', 'f', 'v'
+0 row(s) in 0.0640 seconds
+hbase(main):009 > t.scan
+ROW COLUMN+CELL
+ r column=f:, timestamp=1331865816290, value=v
+1 row(s) in 0.0110 seconds
+hbase(main):010:0> t.describe
+DESCRIPTION ENABLED
+ 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true
+ SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
+ 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
+ ', BLOCKCACHE => 'true'}
+1 row(s) in 0.0210 seconds
+hbase(main):038:0> t.disable
+0 row(s) in 6.2350 seconds
+hbase(main):039:0> t.drop
+0 row(s) in 0.2340 seconds
+----
+
+If the table has already been created, you can assign a Table to a variable by using the get_table method:
+
+----
+hbase(main):011 > create 't','f'
+0 row(s) in 1.2500 seconds
+
+=> Hbase::Table - t
+hbase(main):012:0> tab = get_table 't'
+0 row(s) in 0.0010 seconds
+
+=> Hbase::Table - t
+hbase(main):013:0> tab.put ‘r1’ ,’f’, ‘v’
+0 row(s) in 0.0100 seconds
+hbase(main):014:0> tab.scan
+ROW COLUMN+CELL
+ r1 column=f:, timestamp=1378473876949, value=v
+1 row(s) in 0.0240 seconds
+hbase(main):015:0>
+----
+
+The list functionality has also been extended so that it returns a list of table names as strings.
+You can then use jruby to script table operations based on these names.
+The list_snapshots command also acts similarly.
+
+----
+hbase(main):016 > tables = list(‘t.*’)
+TABLE
+t
+1 row(s) in 0.1040 seconds
+
+=> #<#<Class:0x7677ce29>:0x21d377a4>
+hbase(main):017:0> tables.map { |t| disable t ; drop t}
+0 row(s) in 2.2510 seconds
+
+=> [nil]
+hbase(main):018:0>
+----
+
+=== _irbrc_
+
+Create an _.irbrc_ file for yourself in your home directory.
+Add customizations.
+A useful one is command history so commands are save across Shell invocations:
+[source,bash]
+----
+$ more .irbrc
+require 'irb/ext/save-history'
+IRB.conf[:SAVE_HISTORY] = 100
+IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"
+----
+
+See the `ruby` documentation of _.irbrc_ to learn about other possible configurations.
+
+=== LOG data to timestamp
+
+To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, do:
+
+----
+hbase(main):021:0> import java.text.SimpleDateFormat
+hbase(main):022:0> import java.text.ParsePosition
+hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000
+----
+
+To go the other direction:
+
+----
+hbase(main):021:0> import java.util.Date
+hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008"
+----
+
+To output in a format that is exactly like that of the HBase log format will take a little messing with link:http://download.oracle.com/javase/6/docs/api/java/text/SimpleDateFormat.html[SimpleDateFormat].
+
+=== Debug
+
+==== Shell debug switch
+
+You can set a debug switch in the shell to see more output -- e.g.
+more of the stack trace on exception -- when you run a command:
+
+[source]
+----
+hbase> debug <RETURN>
+----
+
+==== DEBUG log level
+
+To enable DEBUG level logging in the shell, launch it with the `-d` option.
+
+[source,bash]
+----
+$ ./bin/hbase shell -d
+----
+
+=== Commands
+
+==== count
+
+Count command returns the number of rows in a table.
+It's quite fast when configured with the right CACHE
+
+[source]
+----
+hbase> count '<tablename>', CACHE => 1000
+----
+
+The above count fetches 1000 rows at a time.
+Set CACHE lower if your rows are big.
+Default is to fetch one row at a time.
[09/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/ops_mgt.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/ops_mgt.adoc b/src/main/asciidoc/_chapters/ops_mgt.adoc
new file mode 100644
index 0000000..b8018b6
--- /dev/null
+++ b/src/main/asciidoc/_chapters/ops_mgt.adoc
@@ -0,0 +1,2008 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[ops_mgt]]
+= Apache HBase Operational Management
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter will cover operational tools and practices required of a running Apache HBase cluster.
+The subject of operations is related to the topics of <<trouble>>, <<performance>>, and <<configuration>> but is a distinct topic in itself.
+
+[[tools]]
+== HBase Tools and Utilities
+
+HBase provides several tools for administration, analysis, and debugging of your cluster.
+The entry-point to most of these tools is the _bin/hbase_ command, though some tools are available in the _dev-support/_ directory.
+
+To see usage instructions for _bin/hbase_ command, run it with no arguments, or with the `-h` argument.
+These are the usage instructions for HBase 0.98.x.
+Some commands, such as `version`, `pe`, `ltt`, `clean`, are not available in previous versions.
+
+----
+$ bin/hbase
+Usage: hbase [<options>] <command> [<args>]
+Options:
+ --config DIR Configuration direction to use. Default: ./conf
+ --hosts HOSTS Override the list in 'regionservers' file
+
+Commands:
+Some commands take arguments. Pass no args or -h for usage.
+ shell Run the HBase shell
+ hbck Run the hbase 'fsck' tool
+ wal Write-ahead-log analyzer
+ hfile Store file analyzer
+ zkcli Run the ZooKeeper shell
+ upgrade Upgrade hbase
+ master Run an HBase HMaster node
+ regionserver Run an HBase HRegionServer node
+ zookeeper Run a Zookeeper server
+ rest Run an HBase REST server
+ thrift Run the HBase Thrift server
+ thrift2 Run the HBase Thrift2 server
+ clean Run the HBase clean up script
+ classpath Dump hbase CLASSPATH
+ mapredcp Dump CLASSPATH entries required by mapreduce
+ pe Run PerformanceEvaluation
+ ltt Run LoadTestTool
+ version Print the version
+ CLASSNAME Run the class named CLASSNAME
+----
+
+Some of the tools and utilities below are Java classes which are passed directly to the _bin/hbase_ command, as referred to in the last line of the usage instructions.
+Others, such as `hbase shell` (<<shell>>), `hbase upgrade` (<<upgrading>>), and `hbase thrift` (<<thrift>>), are documented elsewhere in this guide.
+
+=== Canary
+
+There is a Canary class can help users to canary-test the HBase cluster status, with every column-family for every regions or RegionServer's granularity.
+To see the usage, use the `--help` parameter.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -help
+
+Usage: bin/hbase org.apache.hadoop.hbase.tool.Canary [opts] [table1 [table2]...] | [regionserver1 [regionserver2]..]
+ where [opts] are:
+ -help Show this help and exit.
+ -regionserver replace the table argument to regionserver,
+ which means to enable regionserver mode
+ -daemon Continuous check at defined intervals.
+ -interval <N> Interval between checks (sec)
+ -e Use region/regionserver as regular expression
+ which means the region/regionserver is regular expression pattern
+ -f <B> stop whole program if first error occurs, default is true
+ -t <N> timeout for a check, default is 600000 (milliseconds)
+----
+
+This tool will return non zero error codes to user for collaborating with other monitoring tools, such as Nagios.
+The error code definitions are:
+
+[source,java]
+----
+private static final int USAGE_EXIT_CODE = 1;
+private static final int INIT_ERROR_EXIT_CODE = 2;
+private static final int TIMEOUT_ERROR_EXIT_CODE = 3;
+private static final int ERROR_EXIT_CODE = 4;
+----
+
+Here are some examples based on the following given case.
+There are two Table objects called test-01 and test-02, they have two column family cf1 and cf2 respectively, and deployed on the 3 RegionServers.
+see following table.
+
+[cols="1,1,1", options="header"]
+|===
+| RegionServer
+| test-01
+| test-02
+| rs1 | r1 | r2
+| rs2 | r2 |
+| rs3 | r2 | r1
+|===
+
+Following are some examples based on the previous given case.
+
+==== Canary test for every column family (store) of every region of every table
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary
+
+3/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf1 in 2ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf2 in 2ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf1 in 4ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf2 in 1ms
+...
+13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf1 in 5ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf2 in 3ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf1 in 31ms
+13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf2 in 8ms
+----
+
+So you can see, table test-01 has two regions and two column families, so the Canary tool will pick 4 small piece of data from 4 (2 region * 2 store) different stores.
+This is a default behavior of the this tool does.
+
+==== Canary test for every column family (store) of every region of specific table(s)
+
+You can also test one or more specific tables.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary test-01 test-02
+----
+
+==== Canary test with RegionServer granularity
+
+This will pick one small piece of data from each RegionServer, and can also put your RegionServer name as input options for canary-test specific RegionServer.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -regionserver
+
+13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs2 in 72ms
+13/12/09 06:05:17 INFO tool.Canary: Read from table:test-02 on region server:rs3 in 34ms
+13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs1 in 56ms
+----
+
+==== Canary test with regular expression pattern
+
+This will test both table test-01 and test-02.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -e test-0[1-2]
+----
+
+==== Run canary test as daemon mode
+
+Run repeatedly with interval defined in option `-interval` whose default value is 6 seconds.
+This daemon will stop itself and return non-zero error code if any error occurs, due to the default value of option -f is true.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -daemon
+----
+
+Run repeatedly with internal 5 seconds and will not stop itself even if errors occur in the test.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -daemon -interval 50000 -f false
+----
+
+==== Force timeout if canary test stuck
+
+In some cases the request is stuck and no response is sent back to the client. This can happen with dead RegionServers which the master has not yet noticed.
+Because of this we provide a timeout option to kill the canary test and return a non-zero error code.
+This run sets the timeout value to 60 seconds, the default value is 600 seconds.
+
+----
+$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -t 600000
+----
+
+==== Running Canary in a Kerberos-enabled Cluster
+
+To run Canary in a Kerberos-enabled cluster, configure the following two properties in _hbase-site.xml_:
+
+* `hbase.client.keytab.file`
+* `hbase.client.kerberos.principal`
+
+Kerberos credentials are refreshed every 30 seconds when Canary runs in daemon mode.
+
+To configure the DNS interface for the client, configure the following optional properties in _hbase-site.xml_.
+
+* `hbase.client.dns.interface`
+* `hbase.client.dns.nameserver`
+
+.Canary in a Kerberos-Enabled Cluster
+====
+This example shows each of the properties with valid values.
+
+[source,xml]
+----
+<property>
+ <name>hbase.client.kerberos.principal</name>
+ <value>hbase/_HOST@YOUR-REALM.COM</value>
+</property>
+<property>
+ <name>hbase.client.keytab.file</name>
+ <value>/etc/hbase/conf/keytab.krb5</value>
+</property>
+<!-- optional params -->
+property>
+ <name>hbase.client.dns.interface</name>
+ <value>default</value>
+</property>
+<property>
+ <name>hbase.client.dns.nameserver</name>
+ <value>default</value>
+</property>
+----
+====
+
+[[health.check]]
+=== Health Checker
+
+You can configure HBase to run a script periodically and if it fails N times (configurable), have the server exit.
+See _HBASE-7351 Periodic health check script_ for configurations and detail.
+
+=== Driver
+
+Several frequently-accessed utilities are provided as `Driver` classes, and executed by the _bin/hbase_ command.
+These utilities represent MapReduce jobs which run on your cluster.
+They are run in the following way, replacing _UtilityName_ with the utility you want to run.
+This command assumes you have set the environment variable `HBASE_HOME` to the directory where HBase is unpacked on your server.
+
+----
+
+${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.mapreduce.UtilityName
+----
+
+The following utilities are available:
+
+`LoadIncrementalHFiles`::
+ Complete a bulk data load.
+
+`CopyTable`::
+ Export a table from the local cluster to a peer cluster.
+
+`Export`::
+ Write table data to HDFS.
+
+`Import`::
+ Import data written by a previous `Export` operation.
+
+`ImportTsv`::
+ Import data in TSV format.
+
+`RowCounter`::
+ Count rows in an HBase table.
+
+`replication.VerifyReplication`::
+ Compare the data from tables in two different clusters.
+ WARNING: It doesn't work for incrementColumnValues'd cells since the timestamp is changed.
+ Note that this command is in a different package than the others.
+
+Each command except `RowCounter` accepts a single `--help` argument to print usage instructions.
+
+[[hbck]]
+=== HBase `hbck`
+
+To run `hbck` against your HBase cluster run `$./bin/hbase hbck`. At the end of the command's output it prints `OK` or `INCONSISTENCY`.
+If your cluster reports inconsistencies, pass `-details` to see more detail emitted.
+If inconsistencies, run `hbck` a few times because the inconsistency may be transient (e.g. cluster is starting up or a region is splitting).
+ Passing `-fix` may correct the inconsistency (This is an experimental feature).
+
+For more information, see <<hbck.in.depth>>.
+
+[[hfile_tool2]]
+=== HFile Tool
+
+See <<hfile_tool>>.
+
+=== WAL Tools
+
+[[hlog_tool]]
+==== `FSHLog` tool
+
+The main method on `FSHLog` offers manual split and dump facilities.
+Pass it WALs or the product of a split, the content of the _recovered.edits_.
+directory.
+
+You can get a textual dump of a WAL file content by doing the following:
+
+----
+ $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012
+----
+
+The return code will be non-zero if there are any issues with the file so you can test wholesomeness of file by redirecting `STDOUT` to `/dev/null` and testing the program return.
+
+Similarly you can force a split of a log file directory by doing:
+
+----
+ $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/
+----
+
+[[hlog_tool.prettyprint]]
+===== WAL Pretty Printer
+
+The WAL Pretty Printer is a tool with configurable options to print the contents of a WAL.
+You can invoke it via the HBase cli with the 'wal' command.
+
+----
+ $ ./bin/hbase wal hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012
+----
+
+.WAL Printing in older versions of HBase
+[NOTE]
+====
+Prior to version 2.0, the WAL Pretty Printer was called the `HLogPrettyPrinter`, after an internal name for HBase's write ahead log.
+In those versions, you can pring the contents of a WAL using the same configuration as above, but with the 'hlog' command.
+
+----
+ $ ./bin/hbase hlog hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012
+----
+====
+
+[[compression.tool]]
+=== Compression Tool
+
+See <<compression.test,compression.test>>.
+
+=== CopyTable
+
+CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster.
+The target table must first exist.
+The usage is as follows:
+
+----
+
+$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
+/bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
+Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename>
+
+Options:
+ rs.class hbase.regionserver.class of the peer cluster,
+ specify if different from current cluster
+ rs.impl hbase.regionserver.impl of the peer cluster,
+ startrow the start row
+ stoprow the stop row
+ starttime beginning of the time range (unixtime in millis)
+ without endtime means from starttime to forever
+ endtime end of the time range. Ignored if no starttime specified.
+ versions number of cell versions to copy
+ new.name new table's name
+ peer.adr Address of the peer cluster given in the format
+ hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
+ families comma-separated list of families to copy
+ To copy from cf1 to cf2, give sourceCfName:destCfName.
+ To keep the same name, just give "cfName"
+ all.cells also copy delete markers and deleted cells
+
+Args:
+ tablename Name of the table to copy
+
+Examples:
+ To copy 'TestTable' to a cluster that uses replication for a 1 hour window:
+ $ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289 --endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase --families=myOldCf:myNewCf,cf2,cf3 TestTable
+
+For performance consider the following general options:
+ It is recommended that you set the following to >=100. A higher value uses more memory but
+ decreases the round trip time to the server and may increase performance.
+ -Dhbase.client.scanner.caching=100
+ The following should always be set to false, to prevent writing data twice, which may produce
+ inaccurate results.
+ -Dmapred.map.tasks.speculative.execution=false
+----
+
+.Scanner Caching
+[NOTE]
+====
+Caching for the input Scan is configured via `hbase.client.scanner.caching` in the job configuration.
+====
+
+.Versions
+[NOTE]
+====
+By default, CopyTable utility only copies the latest version of row cells unless `--versions=n` is explicitly specified in the command.
+====
+
+See Jonathan Hsieh's link:http://www.cloudera.com/blog/2012/06/online-hbase-backups-with-copytable-2/[Online
+ HBase Backups with CopyTable] blog post for more on `CopyTable`.
+
+=== Export
+
+Export is a utility that will dump the contents of table to HDFS in a sequence file.
+Invoke via:
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]]
+----
+
+By default, the `Export` tool only exports the newest version of a given cell, regardless of the number of versions stored. To export more than one version, replace *_<versions>_* with the desired number of versions.
+
+Note: caching for the input Scan is configured via `hbase.client.scanner.caching` in the job configuration.
+
+=== Import
+
+Import is a utility that will load data that has been exported back into HBase.
+Invoke via:
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
+----
+
+To import 0.94 exported files in a 0.96 cluster or onwards, you need to set system property "hbase.import.version" when running the import command as below:
+
+----
+$ bin/hbase -Dhbase.import.version=0.94 org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
+----
+
+=== ImportTsv
+
+ImportTsv is a utility that will load data in TSV format into HBase.
+It has two distinct usages: loading data from TSV format in HDFS into HBase via Puts, and preparing StoreFiles to be loaded via the `completebulkload`.
+
+To load data via Puts (i.e., non-bulk loading):
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir>
+----
+
+To generate StoreFiles for bulk-loading:
+
+[source,bourne]
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>
+----
+
+These generated StoreFiles can be loaded into HBase via <<completebulkload,completebulkload>>.
+
+[[importtsv.options]]
+==== ImportTsv Options
+
+Running `ImportTsv` with no arguments prints brief usage information:
+
+----
+
+Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>
+
+Imports the given input directory of TSV data into the specified table.
+
+The column names of the TSV data must be specified using the -Dimporttsv.columns
+option. This option takes the form of comma-separated column names, where each
+column name is either a simple column family, or a columnfamily:qualifier. The special
+column name HBASE_ROW_KEY is used to designate that this column should be used
+as the row key for each imported record. You must specify exactly one column
+to be the row key, and you must specify a column name for every column that exists in the
+input data.
+
+By default importtsv will load data directly into HBase. To instead generate
+HFiles of data to prepare for a bulk data load, pass the option:
+ -Dimporttsv.bulk.output=/path/for/output
+ Note: the target table will be created with default column family descriptors if it does not already exist.
+
+Other options that may be specified with -D include:
+ -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
+ '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
+ -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
+ -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper
+----
+
+[[importtsv.example]]
+==== ImportTsv Example
+
+For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily called 'd' with two columns "c1" and "c2".
+
+Assume that an input file exists as follows:
+----
+
+row1 c1 c2
+row2 c1 c2
+row3 c1 c2
+row4 c1 c2
+row5 c1 c2
+row6 c1 c2
+row7 c1 c2
+row8 c1 c2
+row9 c1 c2
+row10 c1 c2
+----
+
+For ImportTsv to use this imput file, the command line needs to look like this:
+
+----
+
+ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile
+----
+
+\... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used.
+The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.
+
+[[importtsv.warning]]
+==== ImportTsv Warning
+
+If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately.
+
+[[importtsv.also]]
+==== See Also
+
+For more information about bulk-loading HFiles into HBase, see <<arch.bulk.load,arch.bulk.load>>
+
+=== CompleteBulkLoad
+
+The `completebulkload` utility will move generated StoreFiles into an HBase table.
+This utility is often used in conjunction with output from <<importtsv,importtsv>>.
+
+There are two ways to invoke this utility, with explicit classname and via the driver:
+
+.Explicit Classname
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles <hdfs://storefileoutput> <tablename>
+----
+
+.Driver
+----
+HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename>
+----
+
+[[completebulkload.warning]]
+==== CompleteBulkLoad Warning
+
+Data generated via MapReduce is often created with file permissions that are not compatible with the running HBase process.
+Assuming you're running HDFS with permissions enabled, those permissions will need to be updated before you run CompleteBulkLoad.
+
+For more information about bulk-loading HFiles into HBase, see <<arch.bulk.load,arch.bulk.load>>.
+
+=== WALPlayer
+
+WALPlayer is a utility to replay WAL files into HBase.
+
+The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in milliseconds). The WAL is filtered to this set of tables.
+The output can optionally be mapped to another set of tables.
+
+WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no mapping can be specified.
+
+Invoke via:
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]>
+----
+
+For example:
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2
+----
+
+WALPlayer, by default, runs as a mapreduce job.
+To NOT run WALPlayer as a mapreduce job on your cluster, force it to run all in the local process by adding the flags `-Dmapreduce.jobtracker.address=local` on the command line.
+
+[[rowcounter]]
+=== RowCounter and CellCounter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html[RowCounter] is a mapreduce job to count all the rows of a table.
+This is a good utility to use as a sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of metadata inconsistency.
+It will run the mapreduce all in a single process but it will run faster if you have a MapReduce cluster in place for it to exploit.
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...]
+----
+
+RowCounter only counts one version per cell.
+
+Note: caching for the input Scan is configured via `hbase.client.scanner.caching` in the job configuration.
+
+HBase ships another diagnostic mapreduce job called link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/CellCounter.html[CellCounter].
+Like RowCounter, it gathers more fine-grained statistics about your table.
+The statistics gathered by RowCounter are more fine-grained and include:
+
+* Total number of rows in the table.
+* Total number of CFs across all rows.
+* Total qualifiers across all rows.
+* Total occurrence of each CF.
+* Total occurrence of each qualifier.
+* Total number of versions of each qualifier.
+
+The program allows you to limit the scope of the run.
+Provide a row regex or prefix to limit the rows to analyze.
+Use `hbase.mapreduce.scan.column.family` to specify scanning a single column family.
+
+----
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.CellCounter <tablename> <outputDir> [regex or prefix]
+----
+
+Note: just like RowCounter, caching for the input Scan is configured via `hbase.client.scanner.caching` in the job configuration.
+
+=== mlockall
+
+It is possible to optionally pin your servers in physical memory making them less likely to be swapped out in oversubscribed environments by having the servers call link:http://linux.die.net/man/2/mlockall[mlockall] on startup.
+See link:https://issues.apache.org/jira/browse/HBASE-4391[HBASE-4391 Add ability to
+ start RS as root and call mlockall] for how to build the optional library and have it run on startup.
+
+[[compaction.tool]]
+=== Offline Compaction Tool
+
+See the usage for the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/regionserver/CompactionTool.html[Compaction
+ Tool].
+Run it like this +./bin/hbase
+ org.apache.hadoop.hbase.regionserver.CompactionTool+
+
+=== `hbase clean`
+
+The `hbase clean` command cleans HBase data from ZooKeeper, HDFS, or both.
+It is appropriate to use for testing.
+Run it with no options for usage instructions.
+The `hbase clean` command was introduced in HBase 0.98.
+
+----
+
+$ bin/hbase clean
+Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll)
+Options:
+ --cleanZk cleans hbase related data from zookeeper.
+ --cleanHdfs cleans hbase related data from hdfs.
+ --cleanAll cleans hbase related data from both zookeeper and hdfs.
+----
+
+=== `hbase pe`
+
+The `hbase pe` command is a shortcut provided to run the `org.apache.hadoop.hbase.PerformanceEvaluation` tool, which is used for testing.
+The `hbase pe` command was introduced in HBase 0.98.4.
+
+The PerformanceEvaluation tool accepts many different options and commands.
+For usage instructions, run the command with no options.
+
+To run PerformanceEvaluation prior to HBase 0.98.4, issue the command `hbase org.apache.hadoop.hbase.PerformanceEvaluation`.
+
+The PerformanceEvaluation tool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLs and visibility labels, multiget support for RPC calls, increased sampling sizes, an option to randomly sleep during testing, and ability to "warm up" the cluster before testing starts.
+
+=== `hbase ltt`
+
+The `hbase ltt` command is a shortcut provided to run the `org.apache.hadoop.hbase.util.LoadTestTool` utility, which is used for testing.
+The `hbase ltt` command was introduced in HBase 0.98.4.
+
+You must specify either `-write` or `-update-read` as the first option.
+For general usage instructions, pass the `-h` option.
+
+To run LoadTestTool prior to HBase 0.98.4, issue the command +hbase
+ org.apache.hadoop.hbase.util.LoadTestTool+.
+
+The LoadTestTool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLS and visibility labels, testing security-related features, ability to specify the number of regions per server, tests for multi-get RPC calls, and tests relating to replication.
+
+[[ops.regionmgt]]
+== Region Management
+
+[[ops.regionmgt.majorcompact]]
+=== Major Compaction
+
+Major compactions can be requested via the HBase shell or link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html#majorCompact%28java.lang.String%29[Admin.majorCompact].
+
+Note: major compactions do NOT do region merges.
+See <<compaction,compaction>> for more information about compactions.
+
+[[ops.regionmgt.merge]]
+=== Merge
+
+Merge is a utility that can merge adjoining regions in the same table (see org.apache.hadoop.hbase.util.Merge).
+
+[source,bourne]
+----
+$ bin/hbase org.apache.hadoop.hbase.util.Merge <tablename> <region1> <region2>
+----
+
+If you feel you have too many regions and want to consolidate them, Merge is the utility you need.
+Merge must run be done when the cluster is down.
+See the link:http://ofps.oreilly.com/titles/9781449396107/performance.html[O'Reilly HBase
+ Book] for an example of usage.
+
+You will need to pass 3 parameters to this application.
+The first one is the table name.
+The second one is the fully qualified name of the first region to merge, like "table_name,\x0A,1342956111995.7cef47f192318ba7ccc75b1bbf27a82b.". The third one is the fully qualified name for the second region to merge.
+
+Additionally, there is a Ruby script attached to link:https://issues.apache.org/jira/browse/HBASE-1621[HBASE-1621] for region merging.
+
+[[node.management]]
+== Node Management
+
+[[decommission]]
+=== Node Decommission
+
+You can stop an individual RegionServer by running the following script in the HBase directory on the particular node:
+
+----
+$ ./bin/hbase-daemon.sh stop regionserver
+----
+
+The RegionServer will first close all regions and then shut itself down.
+On shutdown, the RegionServer's ephemeral node in ZooKeeper will expire.
+The master will notice the RegionServer gone and will treat it as a 'crashed' server; it will reassign the nodes the RegionServer was carrying.
+
+.Disable the Load Balancer before Decommissioning a node
+[NOTE]
+====
+If the load balancer runs while a node is shutting down, then there could be contention between the Load Balancer and the Master's recovery of the just decommissioned RegionServer.
+Avoid any problems by disabling the balancer first.
+See <<lb,lb>> below.
+====
+
+.Kill Node Tool
+[NOTE]
+====
+In hbase-2.0, in the bin directory, we added a script named _considerAsDead.sh_ that can be used to kill a regionserver.
+Hardware issues could be detected by specialized monitoring tools before the zookeeper timeout has expired. _considerAsDead.sh_ is a simple function to mark a RegionServer as dead.
+It deletes all the znodes of the server, starting the recovery process.
+Plug in the script into your monitoring/fault detection tools to initiate faster failover.
+Be careful how you use this disruptive tool.
+Copy the script if you need to make use of it in a version of hbase previous to hbase-2.0.
+====
+
+A downside to the above stop of a RegionServer is that regions could be offline for a good period of time.
+Regions are closed in order.
+If many regions on the server, the first region to close may not be back online until all regions close and after the master notices the RegionServer's znode gone.
+In Apache HBase 0.90.2, we added facility for having a node gradually shed its load and then shutdown itself down.
+Apache HBase 0.90.2 added the _graceful_stop.sh_ script.
+Here is its usage:
+
+----
+$ ./bin/graceful_stop.sh
+Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--rest] &hostname>
+ thrift If we should stop/start thrift before/after the hbase stop/start
+ rest If we should stop/start rest before/after the hbase stop/start
+ restart If we should restart after graceful stop
+ reload Move offloaded regions back on to the stopped server
+ debug Move offloaded regions back on to the stopped server
+ hostname Hostname of server we are to stop
+----
+
+To decommission a loaded RegionServer, run the following: +$
+ ./bin/graceful_stop.sh HOSTNAME+ where `HOSTNAME` is the host carrying the RegionServer you would decommission.
+
+.On `HOSTNAME`
+[NOTE]
+====
+The `HOSTNAME` passed to _graceful_stop.sh_ must match the hostname that hbase is using to identify RegionServers.
+Check the list of RegionServers in the master UI for how HBase is referring to servers.
+Its usually hostname but can also be FQDN.
+Whatever HBase is using, this is what you should pass the _graceful_stop.sh_ decommission script.
+If you pass IPs, the script is not yet smart enough to make a hostname (or FQDN) of it and so it will fail when it checks if server is currently running; the graceful unloading of regions will not run.
+====
+
+The _graceful_stop.sh_ script will move the regions off the decommissioned RegionServer one at a time to minimize region churn.
+It will verify the region deployed in the new location before it will moves the next region and so on until the decommissioned server is carrying zero regions.
+At this point, the _graceful_stop.sh_ tells the RegionServer `stop`.
+The master will at this point notice the RegionServer gone but all regions will have already been redeployed and because the RegionServer went down cleanly, there will be no WAL logs to split.
+
+.Load Balancer
+[NOTE]
+====
+It is assumed that the Region Load Balancer is disabled while the `graceful_stop` script runs (otherwise the balancer and the decommission script will end up fighting over region deployments). Use the shell to disable the balancer:
+
+[source]
+----
+hbase(main):001:0> balance_switch false
+true
+0 row(s) in 0.3590 seconds
+----
+
+This turns the balancer OFF.
+To reenable, do:
+
+[source]
+----
+hbase(main):001:0> balance_switch true
+false
+0 row(s) in 0.3590 seconds
+----
+
+The `graceful_stop` will check the balancer and if enabled, will turn it off before it goes to work.
+If it exits prematurely because of error, it will not have reset the balancer.
+Hence, it is better to manage the balancer apart from `graceful_stop` reenabling it after you are done w/ graceful_stop.
+====
+
+[[draining.servers]]
+==== Decommissioning several Regions Servers concurrently
+
+If you have a large cluster, you may want to decommission more than one machine at a time by gracefully stopping mutiple RegionServers concurrently.
+To gracefully drain multiple regionservers at the same time, RegionServers can be put into a "draining" state.
+This is done by marking a RegionServer as a draining node by creating an entry in ZooKeeper under the _hbase_root/draining_ znode.
+This znode has format `name,port,startcode` just like the regionserver entries under _hbase_root/rs_ znode.
+
+Without this facility, decommissioning mulitple nodes may be non-optimal because regions that are being drained from one region server may be moved to other regionservers that are also draining.
+Marking RegionServers to be in the draining state prevents this from happening.
+See this link:http://inchoate-clatter.blogspot.com/2012/03/hbase-ops-automation.html[blog
+ post] for more details.
+
+[[bad.disk]]
+==== Bad or Failing Disk
+
+It is good having <<dfs.datanode.failed.volumes.tolerated,dfs.datanode.failed.volumes.tolerated>> set if you have a decent number of disks per machine for the case where a disk plain dies.
+But usually disks do the "John Wayne" -- i.e.
+take a while to go down spewing errors in _dmesg_ -- or for some reason, run much slower than their companions.
+In this case you want to decommission the disk.
+You have two options.
+You can link:http://wiki.apache.org/hadoop/FAQ#I_want_to_make_a_large_cluster_smaller_by_taking_out_a_bunch_of_nodes_simultaneously._How_can_this_be_done.3F[decommission
+ the datanode] or, less disruptive in that only the bad disks data will be rereplicated, can stop the datanode, unmount the bad volume (You can't umount a volume while the datanode is using it), and then restart the datanode (presuming you have set dfs.datanode.failed.volumes.tolerated > 0). The regionserver will throw some errors in its logs as it recalibrates where to get its data from -- it will likely roll its WAL log too -- but in general but for some latency spikes, it should keep on chugging.
+
+.Short Circuit Reads
+[NOTE]
+====
+If you are doing short-circuit reads, you will have to move the regions off the regionserver before you stop the datanode; when short-circuiting reading, though chmod'd so regionserver cannot have access, because it already has the files open, it will be able to keep reading the file blocks from the bad disk even though the datanode is down.
+Move the regions back after you restart the datanode.
+====
+
+[[rolling]]
+=== Rolling Restart
+
+Some cluster configuration changes require either the entire cluster, or the RegionServers, to be restarted in order to pick up the changes.
+In addition, rolling restarts are supported for upgrading to a minor or maintenance release, and to a major release if at all possible.
+See the release notes for release you want to upgrade to, to find out about limitations to the ability to perform a rolling upgrade.
+
+There are multiple ways to restart your cluster nodes, depending on your situation.
+These methods are detailed below.
+
+==== Using the `rolling-restart.sh` Script
+
+HBase ships with a script, _bin/rolling-restart.sh_, that allows you to perform rolling restarts on the entire cluster, the master only, or the RegionServers only.
+The script is provided as a template for your own script, and is not explicitly tested.
+It requires password-less SSH login to be configured and assumes that you have deployed using a tarball.
+The script requires you to set some environment variables before running it.
+Examine the script and modify it to suit your needs.
+
+._rolling-restart.sh_ General Usage
+====
+----
+
+$ ./bin/rolling-restart.sh --help
+Usage: rolling-restart.sh [--config <hbase-confdir>] [--rs-only] [--master-only] [--graceful] [--maxthreads xx]
+----
+====
+
+Rolling Restart on RegionServers Only::
+ To perform a rolling restart on the RegionServers only, use the `--rs-only` option.
+ This might be necessary if you need to reboot the individual RegionServer or if you make a configuration change that only affects RegionServers and not the other HBase processes.
+
+Rolling Restart on Masters Only::
+ To perform a rolling restart on the active and backup Masters, use the `--master-only` option.
+ You might use this if you know that your configuration change only affects the Master and not the RegionServers, or if you need to restart the server where the active Master is running.
+
+Graceful Restart::
+ If you specify the `--graceful` option, RegionServers are restarted using the _bin/graceful_stop.sh_ script, which moves regions off a RegionServer before restarting it.
+ This is safer, but can delay the restart.
+
+Limiting the Number of Threads::
+ To limit the rolling restart to using only a specific number of threads, use the `--maxthreads` option.
+
+[[rolling.restart.manual]]
+==== Manual Rolling Restart
+
+To retain more control over the process, you may wish to manually do a rolling restart across your cluster.
+This uses the `graceful-stop.sh` command <<decommission,decommission>>.
+In this method, you can restart each RegionServer individually and then move its old regions back into place, retaining locality.
+If you also need to restart the Master, you need to do it separately, and restart the Master before restarting the RegionServers using this method.
+The following is an example of such a command.
+You may need to tailor it to your environment.
+This script does a rolling restart of RegionServers only.
+It disables the load balancer before moving the regions.
+
+----
+
+$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &;
+----
+
+Monitor the output of the _/tmp/log.txt_ file to follow the progress of the script.
+
+==== Logic for Crafting Your Own Rolling Restart Script
+
+Use the following guidelines if you want to create your own rolling restart script.
+
+. Extract the new release, verify its configuration, and synchronize it to all nodes of your cluster using `rsync`, `scp`, or another secure synchronization mechanism.
+. Use the hbck utility to ensure that the cluster is consistent.
++
+----
+
+$ ./bin/hbck
+----
++
+Perform repairs if required.
+See <<hbck,hbck>> for details.
+
+. Restart the master first.
+ You may need to modify these commands if your new HBase directory is different from the old one, such as for an upgrade.
++
+----
+
+$ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master
+----
+
+. Gracefully restart each RegionServer, using a script such as the following, from the Master.
++
+----
+
+$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &
+----
++
+If you are running Thrift or REST servers, pass the --thrift or --rest options.
+For other available options, run the `bin/graceful-stop.sh --help` command.
++
+It is important to drain HBase regions slowly when restarting multiple RegionServers.
+Otherwise, multiple regions go offline simultaneously and must be reassigned to other nodes, which may also go offline soon.
+This can negatively affect performance.
+You can inject delays into the script above, for instance, by adding a Shell command such as `sleep`.
+To wait for 5 minutes between each RegionServer restart, modify the above script to the following:
++
+----
+
+$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i & sleep 5m; done &> /tmp/log.txt &
+----
+
+. Restart the Master again, to clear out the dead servers list and re-enable the load balancer.
+. Run the `hbck` utility again, to be sure the cluster is consistent.
+
+[[adding.new.node]]
+=== Adding a New Node
+
+Adding a new regionserver in HBase is essentially free, you simply start it like this: `$ ./bin/hbase-daemon.sh start regionserver` and it will register itself with the master.
+Ideally you also started a DataNode on the same machine so that the RS can eventually start to have local files.
+If you rely on ssh to start your daemons, don't forget to add the new hostname in _conf/regionservers_ on the master.
+
+At this point the region server isn't serving data because no regions have moved to it yet.
+If the balancer is enabled, it will start moving regions to the new RS.
+On a small/medium cluster this can have a very adverse effect on latency as a lot of regions will be offline at the same time.
+It is thus recommended to disable the balancer the same way it's done when decommissioning a node and move the regions manually (or even better, using a script that moves them one by one).
+
+The moved regions will all have 0% locality and won't have any blocks in cache so the region server will have to use the network to serve requests.
+Apart from resulting in higher latency, it may also be able to use all of your network card's capacity.
+For practical purposes, consider that a standard 1GigE NIC won't be able to read much more than _100MB/s_.
+In this case, or if you are in a OLAP environment and require having locality, then it is recommended to major compact the moved regions.
+
+== HBase Metrics
+
+HBase emits metrics which adhere to the link:http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/metrics/package-summary.html[Hadoop metrics] API.
+Starting with HBase 0.95footnote:[The Metrics system was redone in
+ HBase 0.96. See Migration
+ to the New Metrics Hotness – Metrics2 by Elliot Clark for detail], HBase is configured to emit a default set of metrics with a default sampling period of every 10 seconds.
+You can use HBase metrics in conjunction with Ganglia.
+You can also filter which metrics are emitted and extend the metrics framework to capture custom metrics appropriate for your environment.
+
+=== Metric Setup
+
+For HBase 0.95 and newer, HBase ships with a default metrics configuration, or [firstterm]_sink_.
+This includes a wide variety of individual metrics, and emits them every 10 seconds by default.
+To configure metrics for a given region server, edit the _conf/hadoop-metrics2-hbase.properties_ file.
+Restart the region server for the changes to take effect.
+
+To change the sampling rate for the default sink, edit the line beginning with `*.period`.
+To filter which metrics are emitted or to extend the metrics framework, see link:http://hadoop.apache.org/docs/current/api/org/apache/hadoop/metrics2/package-summary.html
+
+.HBase Metrics and Ganglia
+[NOTE]
+====
+By default, HBase emits a large number of metrics per region server.
+Ganglia may have difficulty processing all these metrics.
+Consider increasing the capacity of the Ganglia server or reducing the number of metrics emitted by HBase.
+See link:http://hadoop.apache.org/docs/current/api/org/apache/hadoop/metrics2/package-summary.html#filtering[Metrics Filtering].
+====
+
+=== Disabling Metrics
+
+To disable metrics for a region server, edit the _conf/hadoop-metrics2-hbase.properties_ file and comment out any uncommented lines.
+Restart the region server for the changes to take effect.
+
+[[discovering.available.metrics]]
+=== Discovering Available Metrics
+
+Rather than listing each metric which HBase emits by default, you can browse through the available metrics, either as a JSON output or via JMX.
+Different metrics are exposed for the Master process and each region server process.
+
+.Procedure: Access a JSON Output of Available Metrics
+. After starting HBase, access the region server's web UI, at `http://REGIONSERVER_HOSTNAME:60030` by default (or port 16030 in HBase 1.0+).
+. Click the [label]#Metrics Dump# link near the top.
+ The metrics for the region server are presented as a dump of the JMX bean in JSON format.
+ This will dump out all metrics names and their values.
+ To include metrics descriptions in the listing -- this can be useful when you are exploring what is available -- add a query string of `?description=true` so your URL becomes `http://REGIONSERVER_HOSTNAME:60030/jmx?description=true`.
+ Not all beans and attributes have descriptions.
+. To view metrics for the Master, connect to the Master's web UI instead (defaults to `http://localhost:60010` or port 16010 in HBase 1.0+) and click its [label]#Metrics
+ Dump# link.
+ To include metrics descriptions in the listing -- this can be useful when you are exploring what is available -- add a query string of `?description=true` so your URL becomes `http://REGIONSERVER_HOSTNAME:60010/jmx?description=true`.
+ Not all beans and attributes have descriptions.
+
+
+You can use many different tools to view JMX content by browsing MBeans.
+This procedure uses `jvisualvm`, which is an application usually available in the JDK.
+
+.Procedure: Browse the JMX Output of Available Metrics
+. Start HBase, if it is not already running.
+. Run the command `jvisualvm` command on a host with a GUI display.
+ You can launch it from the command line or another method appropriate for your operating system.
+. Be sure the [label]#VisualVM-MBeans# plugin is installed. Browse to *Tools -> Plugins*. Click [label]#Installed# and check whether the plugin is listed.
+ If not, click [label]#Available Plugins#, select it, and click btn:[Install].
+ When finished, click btn:[Close].
+. To view details for a given HBase process, double-click the process in the [label]#Local# sub-tree in the left-hand panel.
+ A detailed view opens in the right-hand panel.
+ Click the [label]#MBeans# tab which appears as a tab in the top of the right-hand panel.
+. To access the HBase metrics, navigate to the appropriate sub-bean:
+.* Master:
+.* RegionServer:
+
+. The name of each metric and its current value is displayed in the [label]#Attributes# tab.
+ For a view which includes more details, including the description of each attribute, click the [label]#Metadata# tab.
+
+=== Units of Measure for Metrics
+
+Different metrics are expressed in different units, as appropriate.
+Often, the unit of measure is in the name (as in the metric `shippedKBs`). Otherwise, use the following guidelines.
+When in doubt, you may need to examine the source for a given metric.
+
+* Metrics that refer to a point in time are usually expressed as a timestamp.
+* Metrics that refer to an age (such as `ageOfLastShippedOp`) are usually expressed in milliseconds.
+* Metrics that refer to memory sizes are in bytes.
+* Sizes of queues (such as `sizeOfLogQueue`) are expressed as the number of items in the queue.
+ Determine the size by multiplying by the block size (default is 64 MB in HDFS).
+* Metrics that refer to things like the number of a given type of operations (such as `logEditsRead`) are expressed as an integer.
+
+[[master_metrics]]
+=== Most Important Master Metrics
+
+Note: Counts are usually over the last metrics reporting interval.
+
+hbase.master.numRegionServers::
+ Number of live regionservers
+
+hbase.master.numDeadRegionServers::
+ Number of dead regionservers
+
+hbase.master.ritCount ::
+ The number of regions in transition
+
+hbase.master.ritCountOverThreshold::
+ The number of regions that have been in transition longer than a threshold time (default: 60 seconds)
+
+hbase.master.ritOldestAge::
+ The age of the longest region in transition, in milliseconds
+
+[[rs_metrics]]
+=== Most Important RegionServer Metrics
+
+Note: Counts are usually over the last metrics reporting interval.
+
+hbase.regionserver.regionCount::
+ The number of regions hosted by the regionserver
+
+hbase.regionserver.storeFileCount::
+ The number of store files on disk currently managed by the regionserver
+
+hbase.regionserver.storeFileSize::
+ Aggregate size of the store files on disk
+
+hbase.regionserver.hlogFileCount::
+ The number of write ahead logs not yet archived
+
+hbase.regionserver.totalRequestCount::
+ The total number of requests received
+
+hbase.regionserver.readRequestCount::
+ The number of read requests received
+
+hbase.regionserver.writeRequestCount::
+ The number of write requests received
+
+hbase.regionserver.numOpenConnections::
+ The number of open connections at the RPC layer
+
+hbase.regionserver.numActiveHandler::
+ The number of RPC handlers actively servicing requests
+
+hbase.regionserver.numCallsInGeneralQueue::
+ The number of currently enqueued user requests
+
+hbase.regionserver.numCallsInReplicationQueue::
+ The number of currently enqueued operations received from replication
+
+hbase.regionserver.numCallsInPriorityQueue::
+ The number of currently enqueued priority (internal housekeeping) requests
+
+hbase.regionserver.flushQueueLength::
+ Current depth of the memstore flush queue.
+ If increasing, we are falling behind with clearing memstores out to HDFS.
+
+hbase.regionserver.updatesBlockedTime::
+ Number of milliseconds updates have been blocked so the memstore can be flushed
+
+hbase.regionserver.compactionQueueLength::
+ Current depth of the compaction request queue.
+ If increasing, we are falling behind with storefile compaction.
+
+hbase.regionserver.blockCacheHitCount::
+ The number of block cache hits
+
+hbase.regionserver.blockCacheMissCount::
+ The number of block cache misses
+
+hbase.regionserver.blockCacheExpressHitPercent ::
+ The percent of the time that requests with the cache turned on hit the cache
+
+hbase.regionserver.percentFilesLocal::
+ Percent of store file data that can be read from the local DataNode, 0-100
+
+hbase.regionserver.<op>_<measure>::
+ Operation latencies, where <op> is one of Append, Delete, Mutate, Get, Replay, Increment; and where <measure> is one of min, max, mean, median, 75th_percentile, 95th_percentile, 99th_percentile
+
+hbase.regionserver.slow<op>Count ::
+ The number of operations we thought were slow, where <op> is one of the list above
+
+hbase.regionserver.GcTimeMillis::
+ Time spent in garbage collection, in milliseconds
+
+hbase.regionserver.GcTimeMillisParNew::
+ Time spent in garbage collection of the young generation, in milliseconds
+
+hbase.regionserver.GcTimeMillisConcurrentMarkSweep::
+ Time spent in garbage collection of the old generation, in milliseconds
+
+hbase.regionserver.authenticationSuccesses::
+ Number of client connections where authentication succeeded
+
+hbase.regionserver.authenticationFailures::
+ Number of client connection authentication failures
+
+hbase.regionserver.mutationsWithoutWALCount ::
+ Count of writes submitted with a flag indicating they should bypass the write ahead log
+
+[[ops.monitoring]]
+== HBase Monitoring
+
+[[ops.monitoring.overview]]
+=== Overview
+
+The following metrics are arguably the most important to monitor for each RegionServer for "macro monitoring", preferably with a system like link:http://opentsdb.net/[OpenTSDB].
+If your cluster is having performance issues it's likely that you'll see something unusual with this group.
+
+HBase::
+ * See <<rs_metrics,rs metrics>>
+
+OS::
+ * IO Wait
+ * User CPU
+
+Java::
+ * GC
+
+For more information on HBase metrics, see <<hbase_metrics,hbase metrics>>.
+
+[[ops.slow.query]]
+=== Slow Query Log
+
+The HBase slow query log consists of parseable JSON structures describing the properties of those client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or produced too much output.
+The thresholds for "too long to run" and "too much output" are configurable, as described below.
+The output is produced inline in the main region server logs so that it is easy to discover further details from context with other logged events.
+It is also prepended with identifying tags `(responseTooSlow)`, `(responseTooLarge)`, `(operationTooSlow)`, and `(operationTooLarge)` in order to enable easy filtering with grep, in case the user desires to see only slow queries.
+
+==== Configuration
+
+There are two configuration knobs that can be used to adjust the thresholds for when queries are logged.
+
+* `hbase.ipc.warn.response.time` Maximum number of milliseconds that a query can be run without being logged.
+ Defaults to 10000, or 10 seconds.
+ Can be set to -1 to disable logging by time.
+* `hbase.ipc.warn.response.size` Maximum byte size of response that a query can return without being logged.
+ Defaults to 100 megabytes.
+ Can be set to -1 to disable logging by size.
+
+==== Metrics
+
+The slow query log exposes to metrics to JMX.
+
+* `hadoop.regionserver_rpc_slowResponse` a global metric reflecting the durations of all responses that triggered logging.
+* `hadoop.regionserver_rpc_methodName.aboveOneSec` A metric reflecting the durations of all responses that lasted for more than one second.
+
+==== Output
+
+The output is tagged with operation e.g. `(operationTooSlow)` if the call was a client operation, such as a Put, Get, or Delete, which we expose detailed fingerprint information for.
+If not, it is tagged `(responseTooSlow)` and still produces parseable JSON output, but with less verbose information solely regarding its duration and size in the RPC itself. `TooLarge` is substituted for `TooSlow` if the response size triggered the logging, with `TooLarge` appearing even in the case that both size and duration triggered logging.
+
+==== Example
+
+
+[source]
+----
+2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":1315501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1","vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timestamp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f98764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:33623","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","responsesize":0,"method":"multiPut"}
+----
+
+Note that everything inside the "tables" structure is output produced by MultiPut's fingerprint, while the rest of the information is RPC-specific, such as processing time and client IP/port.
+Other client operations follow the same pattern and the same general structure, with necessary differences due to the nature of the individual operations.
+In the case that the call is not a client operation, that detailed fingerprint information will be completely absent.
+
+This particular example, for example, would indicate that the likely cause of slowness is simply a very large (on the order of 100MB) multiput, as we can tell by the "vlen," or value length, fields of each put in the multiPut.
+
+=== Block Cache Monitoring
+
+Starting with HBase 0.98, the HBase Web UI includes the ability to monitor and report on the performance of the block cache.
+To view the block cache reports, click .
+Following are a few examples of the reporting capabilities.
+
+.Basic Info
+image::bc_basic.png[]
+
+.Config
+image::bc_config.png[]
+
+.Stats
+image::bc_stats.png[]
+
+.L1 and L2
+image::bc_l1.png[]
+
+This is not an exhaustive list of all the screens and reports available.
+Have a look in the Web UI.
+
+== Cluster Replication
+
+NOTE: This information was previously available at link:http://hbase.apache.org/replication.html[Cluster Replication].
+
+HBase provides a cluster replication mechanism which allows you to keep one cluster's state synchronized with that of another cluster, using the write-ahead log (WAL) of the source cluster to propagate the changes.
+Some use cases for cluster replication include:
+
+* Backup and disaster recovery
+* Data aggregation
+* Geographic data distribution
+* Online data ingestion combined with offline data analytics
+
+NOTE: Replication is enabled at the granularity of the column family.
+Before enabling replication for a column family, create the table and all column families to be replicated, on the destination cluster.
+
+=== Replication Overview
+
+Cluster replication uses a source-push methodology.
+An HBase cluster can be a source (also called master or active, meaning that it is the originator of new data), a destination (also called slave or passive, meaning that it receives data via replication), or can fulfill both roles at once.
+Replication is asynchronous, and the goal of replication is eventual consistency.
+When the source receives an edit to a column family with replication enabled, that edit is propagated to all destination clusters using the WAL for that for that column family on the RegionServer managing the relevant region.
+
+When data is replicated from one cluster to another, the original source of the data is tracked via a cluster ID which is part of the metadata.
+In HBase 0.96 and newer (link:https://issues.apache.org/jira/browse/HBASE-7709[HBASE-7709]), all clusters which have already consumed the data are also tracked.
+This prevents replication loops.
+
+The WALs for each region server must be kept in HDFS as long as they are needed to replicate data to any slave cluster.
+Each region server reads from the oldest log it needs to replicate and keeps track of its progress processing WALs inside ZooKeeper to simplify failure recovery.
+The position marker which indicates a slave cluster's progress, as well as the queue of WALs to process, may be different for every slave cluster.
+
+The clusters participating in replication can be of different sizes.
+The master cluster relies on randomization to attempt to balance the stream of replication on the slave clusters.
+It is expected that the slave cluster has storage capacity to hold the replicated data, as well as any data it is responsible for ingesting.
+If a slave cluster does run out of room, or is inaccessible for other reasons, it throws an error and the master retains the WAL and retries the replication at intervals.
+
+.Terminology Changes
+[NOTE]
+====
+Previously, terms such as [firstterm]_master-master_, [firstterm]_master-slave_, and [firstterm]_cyclical_ were used to describe replication relationships in HBase.
+These terms added confusion, and have been abandoned in favor of discussions about cluster topologies appropriate for different scenarios.
+====
+
+.Cluster Topologies
+* A central source cluster might propagate changes out to multiple destination clusters, for failover or due to geographic distribution.
+* A source cluster might push changes to a destination cluster, which might also push its own changes back to the original cluster.
+* Many different low-latency clusters might push changes to one centralized cluster for backup or resource-intensive data analytics jobs.
+ The processed data might then be replicated back to the low-latency clusters.
+
+Multiple levels of replication may be chained together to suit your organization's needs.
+The following diagram shows a hypothetical scenario.
+Use the arrows to follow the data paths.
+
+.Example of a Complex Cluster Replication Configuration
+image::hbase_replication_diagram.jpg[]
+
+HBase replication borrows many concepts from the [firstterm]_statement-based replication_ design used by MySQL.
+Instead of SQL statements, entire WALEdits (consisting of multiple cell inserts coming from Put and Delete operations on the clients) are replicated in order to maintain atomicity.
+
+=== Managing and Configuring Cluster Replication
+.Cluster Configuration Overview
+
+. Configure and start the source and destination clusters.
+ Create tables with the same names and column families on both the source and destination clusters, so that the destination cluster knows where to store data it will receive.
+. All hosts in the source and destination clusters should be reachable to each other.
+. If both clusters use the same ZooKeeper cluster, you must use a different `zookeeper.znode.parent`, because they cannot write in the same folder.
+. Check to be sure that replication has not been disabled. `hbase.replication` defaults to `true`.
+. On the source cluster, in HBase Shell, add the destination cluster as a peer, using the `add_peer` command.
+. On the source cluster, in HBase Shell, enable the table replication, using the `enable_table_replication` command.
+. Check the logs to see if replication is taking place. If so, you will see messages like the following, coming from the ReplicationSource.
+----
+LOG.info("Replicating "+clusterId + " -> " + peerClusterId);
+----
+
+.Cluster Management Commands
+add_peer <ID> <CLUSTER_KEY>::
+ Adds a replication relationship between two clusters. +
+ * ID -- a unique string, which must not contain a hyphen.
+ * CLUSTER_KEY: composed using the following template, with appropriate place-holders: `hbase.zookeeper.quorum:hbase.zookeeper.property.clientPort:zookeeper.znode.parent`
+list_peers:: list all replication relationships known by this cluster
+enable_peer <ID>::
+ Enable a previously-disabled replication relationship
+disable_peer <ID>::
+ Disable a replication relationship. HBase will no longer send edits to that peer cluster, but it still keeps track of all the new WALs that it will need to replicate if and when it is re-enabled.
+remove_peer <ID>::
+ Disable and remove a replication relationship. HBase will no longer send edits to that peer cluster or keep track of WALs.
+enable_table_replication <TABLE_NAME>::
+ Enable the table replication switch for all it's column families. If the table is not found in the destination cluster then it will create one with the same name and column families.
+disable_table_replication <TABLE_NAME>::
+ Disable the table replication switch for all it's column families.
+
+=== Verifying Replicated Data
+
+The `VerifyReplication` MapReduce job, which is included in HBase, performs a systematic comparison of replicated data between two different clusters. Run the VerifyReplication job on the master cluster, supplying it with the peer ID and table name to use for validation. You can limit the verification further by specifying a time range or specific families. The job's short name is `verifyrep`. To run the job, use a command like the following:
++
+[source,bash]
+----
+$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` "${HADOOP_HOME}/bin/hadoop" jar "${HBASE_HOME}/hbase-server-VERSION.jar" verifyrep --starttime=<timestamp> --stoptime=<timestamp> --families=<myFam> <ID> <tableName>
+----
++
+The `VerifyReplication` command prints out `GOODROWS` and `BADROWS` counters to indicate rows that did and did not replicate correctly.
+
+=== Detailed Information About Cluster Replication
+
+.Replication Architecture Overview
+image::replication_overview.png[]
+
+==== Life of a WAL Edit
+
+A single WAL edit goes through several steps in order to be replicated to a slave cluster.
+
+. An HBase client uses a Put or Delete operation to manipulate data in HBase.
+. The region server writes the request to the WAL in a way allows it to be replayed if it is not written successfully.
+. If the changed cell corresponds to a column family that is scoped for replication, the edit is added to the queue for replication.
+. In a separate thread, the edit is read from the log, as part of a batch process.
+ Only the KeyValues that are eligible for replication are kept.
+ Replicable KeyValues are part of a column family whose schema is scoped GLOBAL, are not part of a catalog such as `hbase:meta`, did not originate from the target slave cluster, and have not already been consumed by the target slave cluster.
+. The edit is tagged with the master's UUID and added to a buffer.
+ When the buffer is filled, or the reader reaches the end of the file, the buffer is sent to a random region server on the slave cluster.
+. The region server reads the edits sequentially and separates them into buffers, one buffer per table.
+ After all edits are read, each buffer is flushed using link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table], HBase's normal client.
+ The master's UUID and the UUIDs of slaves which have already consumed the data are preserved in the edits they are applied, in order to prevent replication loops.
+. In the master, the offset for the WAL that is currently being replicated is registered in ZooKeeper.
+
+. The first three steps, where the edit is inserted, are identical.
+. Again in a separate thread, the region server reads, filters, and edits the log edits in the same way as above.
+ The slave region server does not answer the RPC call.
+. The master sleeps and tries again a configurable number of times.
+. If the slave region server is still not available, the master selects a new subset of region server to replicate to, and tries again to send the buffer of edits.
+. Meanwhile, the WALs are rolled and stored in a queue in ZooKeeper.
+ Logs that are [firstterm]_archived_ by their region server, by moving them from the region server's log directory to a central log directory, will update their paths in the in-memory queue of the replicating thread.
+. When the slave cluster is finally available, the buffer is applied in the same way as during normal processing.
+ The master region server will then replicate the backlog of logs that accumulated during the outage.
+
+.Spreading Queue Failover Load
+When replication is active, a subset of region servers in the source cluster is responsible for shipping edits to the sink.
+This responsibility must be failed over like all other region server functions should a process or node crash.
+The following configuration settings are recommended for maintaining an even distribution of replication activity over the remaining live servers in the source cluster:
+
+* Set `replication.source.maxretriesmultiplier` to `300`.
+* Set `replication.source.sleepforretries` to `1` (1 second). This value, combined with the value of `replication.source.maxretriesmultiplier`, causes the retry cycle to last about 5 minutes.
+* Set `replication.sleep.before.failover` to `30000` (30 seconds) in the source cluster site configuration.
+
+.Preserving Tags During Replication
+By default, the codec used for replication between clusters strips tags, such as cell-level ACLs, from cells.
+To prevent the tags from being stripped, you can use a different codec which does not strip them.
+Configure `hbase.replication.rpc.codec` to use `org.apache.hadoop.hbase.codec.KeyValueCodecWithTags`, on both the source and sink RegionServers involved in the replication.
+This option was introduced in link:https://issues.apache.org/jira/browse/HBASE-10322[HBASE-10322].
+
+==== Replication Internals
+
+Replication State in ZooKeeper::
+ HBase replication maintains its state in ZooKeeper.
+ By default, the state is contained in the base node _/hbase/replication_.
+ This node contains two child nodes, the `Peers` znode and the `RS` znode.
+
+The `Peers` Znode::
+ The `peers` znode is stored in _/hbase/replication/peers_ by default.
+ It consists of a list of all peer replication clusters, along with the status of each of them.
+ The value of each peer is its cluster key, which is provided in the HBase Shell.
+ The cluster key contains a list of ZooKeeper nodes in the cluster's quorum, the client port for the ZooKeeper quorum, and the base znode for HBase in HDFS on that cluster.
+
+The `RS` Znode::
+ The `rs` znode contains a list of WAL logs which need to be replicated.
+ This list is divided into a set of queues organized by region server and the peer cluster the region server is shipping the logs to.
+ The rs znode has one child znode for each region server in the cluster.
+ The child znode name is the region server's hostname, client port, and start code.
+ This list includes both live and dead region servers.
+
+==== Choosing Region Servers to Replicate To
+
+When a master cluster region server initiates a replication source to a slave cluster, it first connects to the slave's ZooKeeper ensemble using the provided cluster key . It then scans the _rs/_ directory to discover all the available sinks (region servers that are accepting incoming streams of edits to replicate) and randomly chooses a subset of them using a configured ratio which has a default value of 10%. For example, if a slave cluster has 150 machines, 15 will be chosen as potential recipient for edits that this master cluster region server sends.
+Because this selection is performed by each master region server, the probability that all slave region servers are used is very high, and this method works for clusters of any size.
+For example, a master cluster of 10 machines replicating to a slave cluster of 5 machines with a ratio of 10% causes the master cluster region servers to choose one machine each at random.
+
+A ZooKeeper watcher is placed on the _${zookeeper.znode.parent}/rs_ node of the slave cluster by each of the master cluster's region servers.
+This watch is used to monitor changes in the composition of the slave cluster.
+When nodes are removed from the slave cluster, or if nodes go down or come back up, the master cluster's region servers will respond by selecting a new pool of slave region servers to replicate to.
+
+==== Keeping Track of Logs
+
+Each master cluster region server has its own znode in the replication znodes hierarchy.
+It contains one znode per peer cluster (if 5 slave clusters, 5 znodes are created), and each of these contain a queue of WALs to process.
+Each of these queues will track the WALs created by that region server, but they can differ in size.
+For example, if one slave cluster becomes unavailable for some time, the WALs should not be deleted, so they need to stay in the queue while the others are processed.
+See <<rs.failover.details,rs.failover.details>> for an example.
+
+When a source is instantiated, it contains the current WAL that the region server is writing to.
+During log rolling, the new file is added to the queue of each slave cluster's znode just before it is made available.
+This ensures that all the sources are aware that a new log exists before the region server is able to append edits into it, but this operations is now more expensive.
+The queue items are discarded when the replication thread cannot read more entries from a file (because it reached the end of the last block) and there are other files in the queue.
+This means that if a source is up to date and replicates from the log that the region server writes to, reading up to the "end" of the current file will not delete the item in the queue.
+
+A log can be archived if it is no longer used or if the number of logs exceeds `hbase.regionserver.maxlogs` because the insertion rate is faster than regions are flushed.
+When a log is archived, the source threads are notified that the path for that log changed.
+If a particular source has already finished with an archived log, it will just ignore the message.
+If the log is in the queue, the path will be updated in memory.
+If the log is currently being replicated, the change will be done atomically so that the reader doesn't attempt to open the file when has already been moved.
+Because moving a file is a NameNode operation , if the reader is currently reading the log, it won't generate any exception.
+
+==== Reading, Filtering and Sending Edits
+
+By default, a source attempts to read from a WAL and ship log entries to a sink as quickly as possible.
+Speed is limited by the filtering of log entries Only KeyValues that are scoped GLOBAL and that do not belong to catalog tables will be retained.
+Speed is also limited by total size of the list of edits to replicate per slave, which is limited to 64 MB by default.
+With this configuration, a master cluster region server with three slaves would use at most 192 MB to store data to replicate.
+This does not account for the data which was filtered but not garbage collected.
+
+Once the maximum size of edits has been buffered or the reader reaces the end of the WAL, the source thread stops reading and chooses at random a sink to replicate to (from the list that was generated by keeping only a subset of slave region servers). It directly issues a RPC to the chosen region server and waits for the method to return.
+If the RPC was successful, the source determines whether the current file has been emptied or it contains more data which needs to be read.
+If the file has been emptied, the source deletes the znode in the queue.
+Otherwise, it registers the new offset in the log's znode.
+If the RPC threw an exception, the source will retry 10 times before trying to find a different sink.
+
+==== Cleaning Logs
+
+If replication is not enabled, the master's log-cleaning thread deletes old logs using a configured TTL.
+This TTL-based method does not work well with replication, because archived logs which have exceeded their TTL may still be in a queue.
+The default behavior is augmented so that if a log is past its TTL, the cleaning thread looks up every queue until it finds the log, while caching queues it has found.
+If the log is not found in any queues, the log will be deleted.
+The next time the cleaning process needs to look for a log, it starts by using its cached list.
+
+[[rs.failover.details]]
+==== Region Server Failover
+
+When no region servers are failing, keeping track of the logs in ZooKeeper adds no value.
+Unfortunately, region servers do fail, and since ZooKeeper is highly available, it is useful for managing the transfer of the queues in the event of a failure.
+
+Each of the master cluster region servers keeps a watcher on every other region server, in order to be notified when one dies (just as the master does). When a failure happens, they all race to create a znode called `lock` inside the dead region server's znode that contains its queues.
+The region server that creates it successfully then transfers all the queues to its own znode, one at a time since ZooKeeper does not support renaming queues.
+After queues are all transferred, they are deleted from the old location.
+The znodes that were recovered are renamed with the ID of the slave cluster appended with the name of the dead server.
+
+Next, the master cluster region server creates one new source thread per copied queue, and each of the source threads follows the read/filter/ship pattern.
+The main difference is that those queues will never receive new data, since they do not belong to their new region server.
+When the reader hits the end of the last log, the queue's znode is deleted and the master cluster region server closes that replication source.
+
+Given a master cluster with 3 region servers replicating to a single slave with id `2`, the following hierarchy represents what the znodes layout could be at some point in time.
+The region servers' znodes all contain a `peers` znode which contains a single queue.
+The znode names in the queues represent the actual file names on HDFS in the form `address,port.timestamp`.
+
+----
+
+/hbase/replication/rs/
+ 1.1.1.1,60020,123456780/
+ 2/
+ 1.1.1.1,60020.1234 (Contains a position)
+ 1.1.1.1,60020.1265
+ 1.1.1.2,60020,123456790/
+ 2/
+ 1.1.1.2,60020.1214 (Contains a position)
+ 1.1.1.2,60020.1248
+ 1.1.1.2,60020.1312
+ 1.1.1.3,60020, 123456630/
+ 2/
+ 1.1.1.3,60020.1280 (Contains a position)
+----
+
+Assume that 1.1.1.2 loses its ZooKeeper session.
+The survivors will race to create a lock, and, arbitrarily, 1.1.1.3 wins.
+It will then start transferring all the queues to its local peers znode by appending the name of the dead server.
+Right before 1.1.1.3 is able to clean up the old znodes, the layout will look like the following:
+
+----
+
+/hbase/replication/rs/
+ 1.1.1.1,60020,123456780/
+ 2/
+ 1.1.1.1,60020.1234 (Contains a position)
+ 1.1.1.1,60020.1265
+ 1.1.1.2,60020,123456790/
+ lock
+ 2/
+ 1.1.1.2,60020.1214 (Contains a position)
+ 1.1.1.2,60020.1248
+ 1.1.1.2,60020.1312
+ 1.1.1.3,60020,123456630/
+ 2/
+ 1.1.1.3,60020.1280 (Contains a position)
+
+ 2-1.1.1.2,60020,123456790/
+ 1.1.1.2,60020.1214 (Contains a position)
+ 1.1.1.2,60020.1248
+ 1.1.1.2,60020.1312
+----
+
+Some time later, but before 1.1.1.3 is able to finish replicating the last WAL from 1.1.1.2, it dies too.
+Some new logs were also created in the normal queues.
+The last region server will then try to lock 1.1.1.3's znode and will begin transferring all the queues.
+The new layout will be:
+
+----
+
+/hbase/replication/rs/
+ 1.1.1.1,60020,123456780/
+ 2/
+ 1.1.1.1,60020.1378 (Contains a position)
+
+ 2-1.1.1.3,60020,123456630/
+ 1.1.1.3,60020.1325 (Contains a position)
+ 1.1.1.3,60020.1401
+
+ 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
+ 1.1.1.2,60020.1312 (Contains a position)
+ 1.1.1.3,60020,123456630/
+ lock
+ 2/
+ 1.1.1.3,60020.1325 (Contains a position)
+ 1.1.1.3,60020.1401
+
+ 2-1.1.1.2,60020,123456790/
+ 1.1.1.2,60020.1312 (Contains a position)
+----
+
+=== Replication Metrics
+
+The following metrics are exposed at the global region server level and (since HBase 0.95) at the peer level:
+
+`source.sizeOfLogQueue`::
+ number of WALs to process (excludes the one which is being processed) at the Replication source
+
+`source.shippedOps`::
+ number of mutations shipped
+
+`source.logEditsRead`::
+ number of mutations read from WALs at the replication source
+
+`source.ageOfLastShippedOp`::
+ age of last batch that was shipped by the replication source
+
+=== Replication Configuration Options
+
+[cols="1,1,1", options="header"]
+|===
+| Option
+| Description
+| Default
+
+| zookeeper.znode.parent
+| The name of the base ZooKeeper znode used for HBase
+| /hbase
+
+| zookeeper.znode.replication
+| The name of the base znode used for replication
+| replication
+
+| zookeeper.znode.replication.peers
+| The name of the peer znode
+| peers
+
+| zookeeper.znode.replication.peers.state
+| The name of peer-state znode
+| peer-state
+
+| zookeeper.znode.replication.rs
+| The name of the rs znode
+| rs
+
+| hbase.replication
+| Whether replication is enabled or disabled on a given
+ cluster
+| false
+
+| eplication.sleep.before.failover
+| How many milliseconds a worker should sleep before attempting to replicate
+ a dead region server's WAL queues.
+|
+
+| replication.executor.workers
+| The number of region servers a given region server should attempt to
+ failover simultaneously.
+| 1
+|===
+
+=== Monitoring Replication Status
+
+You can use the HBase Shell command `status 'replication'` to monitor the replication status on your cluster. The command has three variations:
+* `status 'replication'` -- prints the status of each source and its sinks, sorted by hostname.
+* `status 'replication', 'source'` -- prints the status for each replication source, sorted by hostname.
+* `status 'replication', 'sink'` -- prints the status for each replication sink, sorted by hostname.
+
+[[ops.backup]]
+== HBase Backup
+
+There are two broad strategies for performing HBase backups: backing up with a full cluster shutdown, and backing up on a live cluster.
+Each approach has pros and cons.
+
+For additional information, see link:http://blog.sematext.com/2011/03/11/hbase-backup-options/[HBase Backup
+ Options] over on the Sematext Blog.
+
+[[ops.backup.fullshutdown]]
+=== Full Shutdown Backup
+
+Some environments can tolerate a periodic full shutdown of their HBase cluster, for example if it is being used a back-end analytic capacity and not serving front-end web-pages.
+The benefits are that the NameNode/Master are RegionServers are down, so there is no chance of missing any in-flight changes to either StoreFiles or metadata.
+The obvious con is that the cluster is down.
+The steps include:
+
+[[ops.backup.fullshutdown.stop]]
+==== Stop HBase
+
+
+
+[[ops.backup.fullshutdown.distcp]]
+==== Distcp
+
+Distcp could be used to either copy the contents of the HBase directory in HDFS to either the same cluster in another directory, or to a different cluster.
+
+Note: Distcp works in this situation because the cluster is down and there are no in-flight edits to files.
+Distcp-ing of files in the HBase directory is not generally recommended on a live cluster.
+
+[[ops.backup.fullshutdown.restore]]
+==== Restore (if needed)
+
+The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory via distcp.
+The act of copying these files creates new HDFS metadata, which is why a restore of the NameNode edits from the time of the HBase backup isn't required for this kind of restore, because it's a restore (via distcp) of a specific HDFS directory (i.e., the HBase part) not the entire HDFS file-system.
+
+[[ops.backup.live.replication]]
+=== Live Cluster Backup - Replication
+
+This approach assumes that there is a second cluster.
+See the HBase page on link:http://hbase.apache.org/replication.html[replication] for more information.
+
+[[ops.backup.live.copytable]]
+=== Live Cluster Backup - CopyTable
+
+The <<copytable,copytable>> utility could either be used to copy data from one table to another on the same cluster, or to copy data to another table on another cluster.
+
+Since the cluster is up, there is a risk that edits could be missed in the copy process.
+
+[[ops.backup.live.export]]
+=== Live Cluster Backup - Export
+
+The <<export,export>> approach dumps the content of a table to HDFS on the same cluster.
+To restore the data, the <<import,import>> utility would be used.
+
+Since the cluster is up, there is a risk that edits could be missed in the export process.
+
+[[ops.snapshots]]
+== HBase Snapshots
+
+HBase Snapshots allow you to take a snapshot of a table without too much impact on Region Servers.
+Snapshot, Clone and restore operations don't involve data copying.
+Also, Exporting the snapshot to another cluster doesn't have impact on the Region Servers.
+
+Prior to version 0.94.6, the only way to backup or to clone a table is to use CopyTable/ExportTable, or to copy all the hfiles in HDFS after disabling the table.
+The disadvantages of these methods are that you can degrade region server performance (Copy/Export Table) or you need to disable the table, that means no reads or writes; and this is usually unacceptable.
+
+[[ops.snapshots.configuration]]
+=== Configuration
+
+To turn on the snapshot support just set the `hbase.snapshot.enabled` property to true.
+(Snapshots are enabled by default in 0.95+ and off by default in 0.94.6+)
+
+[source,java]
+----
+
+ <property>
+ <name>hbase.snapshot.enabled</name>
+ <value>true</value>
+ </property>
+----
+
+[[ops.snapshots.takeasnapshot]]
+=== Take a Snapshot
+
+You can take a snapshot of a table regardless of whether it is enabled or disabled.
+The snapshot operation doesn't involve any data copying.
+
+----
+
+$ ./bin/hbase shell
+hbase> snapshot 'myTable', 'myTableSnapshot-122112'
+----
+
+.Take a Snapshot Without Flushing
+The default behavior is to perform a flush of data in memory before the snapshot is taken.
+This means that data in memory is included in the snapshot.
+In most cases, this is the desired behavior.
+However, if your set-up can tolerate data in memory being excluded from the snapshot, you can use the `SKIP_FLUSH` option of the `snapshot` command to disable and flushing while taking the snapshot.
+
+----
+hbase> snapshot 'mytable', 'snapshot123', {SKIP_FLUSH => true}
+----
+
+WARNING: There is no way to determine or predict whether a very concurrent insert or update will be included in a given snapshot, whether flushing is enabled or disabled.
+A snapshot is only a representation of a table during a window of time.
+The amount of time the snapshot operation will take to reach each Region Server may vary from a few seconds to a minute, depending on the resource load and speed of the hardware or network, among other factors.
+There is also no way to know whether a given insert or update is in memory or has been flushed.
+
+[[ops.snapshots.list]]
+=== Listing Snapshots
+
+List all snapshots taken (by printing the names and relative information).
+
+----
+
+$ ./bin/hbase shell
+hbase> list_snapshots
+----
+
+[[ops.snapshots.delete]]
+=== Deleting Snapshots
+
+You can remove a snapshot, and the files retained for that snapshot will be removed if no longer needed.
+
+----
+
+$ ./bin/hbase shell
+hbase> delete_snapshot 'myTableSnapshot-122112'
+----
+
+[[ops.snapshots.clone]]
+=== Clone a table from snapshot
+
+From a snapshot you can create a new table (clone operation) with the same data that you had when the snapshot was taken.
+The clone operation, doesn't involve data copies, and a change to the cloned table doesn't impact the snapshot or the original table.
+
+----
+
+$ ./bin/hbase shell
+hbase> clone_snapshot 'myTableSnapshot-122112', 'myNewTestTable'
+----
+
+[[ops.snapshots.restore]]
+=== Restore a snapshot
+
+The restore operation requires the table to be disabled, and the table will be restored to the state at the time when the snapshot was taken, changing both data and schema if required.
+
+----
+
+$ ./bin/hbase shell
+hbase> disable 'myTable'
+hbase> restore_snapshot 'myTableSnapshot-122112'
+----
+
+NOTE: Since Replication works at log level and snapshots at file-system level, after a restore, the replicas will be in a different state from the master.
+If you want to use restore, you need to stop replication and redo the bootstrap.
+
+In case of partial data-loss due to misbehaving client, instead of a full restore that requires the table to be disabled, you can clone the table from the snapshot and use a Map-Reduce job to copy the data that you need, from the clone to the main one.
+
+[[ops.snapshots.acls]]
+=== Snapshots operations and ACLs
+
+If you are using security with the AccessController Coprocessor (See <<hbase.accesscontrol.configuration,hbase.accesscontrol.configuration>>), only a global administrator can take, clone, or restore a snapshot, and these actions do not capture the ACL rights.
+This means that restoring a table preserves the ACL rights of the existing table, while cloning a table creates a new table that has no ACL rights until the administrator adds them.
+
+[[ops.snapshots.export]]
+=== Export to another cluster
+
+The ExportSnapshot tool copies all the data related to a snapshot (hfiles, logs, snapshot metadata) to another cluster.
+The tool executes a Map-Reduce job, similar to distcp, to copy files between the two clusters, and since it works at file-system level the hbase cluster does not have to be online.
+
+To copy a snapshot called MySnapshot to an HBase cluster srv2 (hdfs:///srv2:8082/hbase) using 16 mappers:
+
+[source,bourne]
+----
+$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16
+----
+
+.Limiting Bandwidth Consumption
+You can limit the bandwidth consumption when exporting a snapshot, by specifying the `-bandwidth` parameter, which expects an integer representing megabytes per second.
+The following example limits the above example to 200 MB/sec.
+
+[source,bourne]
+----
+$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16 -bandwidth 200
+----
+
+[[ops.capacity]]
+== Capacity Planning and Region Sizing
+
+There are several considerations when planning the capacity for an HBase cluster and performing the initial configuration.
+Start with a solid understanding of how HBase handles data internally.
+
+[[ops.capacity.nod
<TRUNCATED>
[18/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
HBASE-13665 Fix docs and site building on branch-1
Project: http://git-wip-us.apache.org/repos/asf/hbase/repo
Commit: http://git-wip-us.apache.org/repos/asf/hbase/commit/33fe79cf
Tree: http://git-wip-us.apache.org/repos/asf/hbase/tree/33fe79cf
Diff: http://git-wip-us.apache.org/repos/asf/hbase/diff/33fe79cf
Branch: refs/heads/branch-1
Commit: 33fe79cf6f8e961777032194c3f42c57c5797ff6
Parents: 95f1fe5
Author: Nick Dimiduk <nd...@apache.org>
Authored: Mon May 11 14:43:58 2015 -0700
Committer: Nick Dimiduk <nd...@apache.org>
Committed: Mon May 11 15:26:41 2015 -0700
----------------------------------------------------------------------
pom.xml | 177 +-
.../asciidoc/_chapters/appendix_acl_matrix.adoc | 161 ++
.../appendix_contributing_to_documentation.adoc | 395 +++
.../_chapters/appendix_hfile_format.adoc | 355 +++
src/main/asciidoc/_chapters/architecture.adoc | 2512 ++++++++++++++++++
src/main/asciidoc/_chapters/asf.adoc | 47 +
src/main/asciidoc/_chapters/case_studies.adoc | 170 ++
src/main/asciidoc/_chapters/community.adoc | 111 +
src/main/asciidoc/_chapters/compression.adoc | 459 ++++
src/main/asciidoc/_chapters/configuration.adoc | 1097 ++++++++
src/main/asciidoc/_chapters/cp.adoc | 230 ++
src/main/asciidoc/_chapters/datamodel.adoc | 562 ++++
src/main/asciidoc/_chapters/developer.adoc | 1971 ++++++++++++++
src/main/asciidoc/_chapters/external_apis.adoc | 66 +
src/main/asciidoc/_chapters/faq.adoc | 141 +
.../asciidoc/_chapters/getting_started.adoc | 641 +++++
src/main/asciidoc/_chapters/hbase-default.adoc | 2254 ++++++++++++++++
src/main/asciidoc/_chapters/hbase_apis.adoc | 135 +
src/main/asciidoc/_chapters/hbase_history.adoc | 37 +
src/main/asciidoc/_chapters/hbck_in_depth.adoc | 212 ++
src/main/asciidoc/_chapters/images | 1 +
src/main/asciidoc/_chapters/mapreduce.adoc | 596 +++++
src/main/asciidoc/_chapters/ops_mgt.adoc | 2008 ++++++++++++++
src/main/asciidoc/_chapters/orca.adoc | 38 +
src/main/asciidoc/_chapters/other_info.adoc | 80 +
src/main/asciidoc/_chapters/performance.adoc | 890 +++++++
src/main/asciidoc/_chapters/preface.adoc | 64 +
src/main/asciidoc/_chapters/rpc.adoc | 222 ++
src/main/asciidoc/_chapters/schema_design.adoc | 1048 ++++++++
src/main/asciidoc/_chapters/security.adoc | 1722 ++++++++++++
src/main/asciidoc/_chapters/shell.adoc | 390 +++
src/main/asciidoc/_chapters/sql.adoc | 42 +
.../_chapters/thrift_filter_language.adoc | 279 ++
src/main/asciidoc/_chapters/tracing.adoc | 194 ++
.../asciidoc/_chapters/troubleshooting.adoc | 1471 ++++++++++
src/main/asciidoc/_chapters/unit_testing.adoc | 351 +++
src/main/asciidoc/_chapters/upgrading.adoc | 452 ++++
src/main/asciidoc/_chapters/ycsb.adoc | 42 +
src/main/asciidoc/_chapters/zookeeper.adoc | 451 ++++
src/main/asciidoc/asciidoctor.css | 399 +++
src/main/asciidoc/book.adoc | 94 +
src/main/asciidoc/hbase.css | 400 +++
src/main/asciidoc/images | 1 +
src/main/site/asciidoc/acid-semantics.adoc | 114 +
src/main/site/asciidoc/bulk-loads.adoc | 19 +
src/main/site/asciidoc/cygwin.adoc | 193 ++
src/main/site/asciidoc/export_control.adoc | 40 +
src/main/site/asciidoc/index.adoc | 71 +
src/main/site/asciidoc/metrics.adoc | 97 +
src/main/site/asciidoc/old_news.adoc | 117 +
src/main/site/asciidoc/pseudo-distributed.adoc | 19 +
src/main/site/asciidoc/replication.adoc | 18 +
src/main/site/asciidoc/resources.adoc | 22 +
src/main/site/asciidoc/sponsors.adoc | 30 +
src/main/site/resources/.htaccess | 8 +
src/main/site/resources/book/.empty | 1 +
src/main/site/resources/css/site.css | 3 +
.../resources/images/hbase_logo_with_orca.png | Bin 0 -> 11618 bytes
.../resources/images/hbase_logo_with_orca.xcf | Bin 0 -> 84265 bytes
.../images/jumping-orca_transparent_rotated.xcf | Bin 0 -> 135399 bytes
.../resources/images/region_split_process.png | Bin 0 -> 338255 bytes
src/main/site/site.xml | 68 +-
src/main/site/xdoc/index.xml | 20 +-
src/main/site/xdoc/replication.xml | 516 +---
.../xslt/configuration_to_asciidoc_chapter.xsl | 92 +
.../xslt/configuration_to_docbook_section.xsl | 66 -
66 files changed, 23806 insertions(+), 676 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/pom.xml
----------------------------------------------------------------------
diff --git a/pom.xml b/pom.xml
index 3e91394..72419eb 100644
--- a/pom.xml
+++ b/pom.xml
@@ -837,14 +837,21 @@
</executions>
<configuration>
<transformationSets>
+ <!-- For asciidoc -->
<transformationSet>
<!--Reaching up and over into common sub-module for hbase-default.xml-->
<dir>${basedir}/hbase-common/src/main/resources/</dir>
<includes>
<include>hbase-default.xml</include>
</includes>
- <stylesheet>${basedir}/src/main/xslt/configuration_to_docbook_section.xsl</stylesheet>
- <outputDir>${basedir}/target/docbkx</outputDir>
+ <stylesheet>${basedir}/src/main/xslt/configuration_to_asciidoc_chapter.xsl</stylesheet>
+ <fileMappers>
+ <fileMapper implementation="org.codehaus.plexus.components.io.filemappers.RegExpFileMapper">
+ <pattern>^(.*)\.xml$</pattern>
+ <replacement>$1.adoc</replacement>
+ </fileMapper>
+ </fileMappers>
+ <outputDir>${basedir}/target/asciidoc</outputDir>
</transformationSet>
</transformationSets>
</configuration>
@@ -881,62 +888,6 @@
<suppressionsLocation>hbase/checkstyle-suppressions.xml</suppressionsLocation>
</configuration>
</plugin>
- <!--Build the documentation. We build it twice. Once as a single page and then
- again as multipage.-->
- <plugin>
- <groupId>com.agilejava.docbkx</groupId>
- <artifactId>docbkx-maven-plugin</artifactId>
- <version>2.0.15</version>
- <inherited>false</inherited>
- <dependencies>
- <dependency>
- <groupId>org.docbook</groupId>
- <artifactId>docbook-xml</artifactId>
- <version>4.4</version>
- <scope>runtime</scope>
- </dependency>
- </dependencies>
- <configuration>
- <sourceDirectory>${basedir}/src/main/docbkx</sourceDirectory>
- <xincludeSupported>true</xincludeSupported>
- <useIdAsFilename>true</useIdAsFilename>
- <sectionAutolabelMaxDepth>100</sectionAutolabelMaxDepth>
- <sectionAutolabel>true</sectionAutolabel>
- <sectionLabelIncludesComponentLabel>true</sectionLabelIncludesComponentLabel>
- <htmlCustomization>${basedir}/src/main/docbkx/customization.xsl</htmlCustomization>
- <tocMaxDepth>2</tocMaxDepth>
- <insertXrefPageNumber>yes</insertXrefPageNumber>
- <chunkerOutputEncoding>UTF-8</chunkerOutputEncoding>
- </configuration>
- <executions>
- <execution>
- <id>multipage</id>
- <goals>
- <goal>generate-html</goal>
- </goals>
- <phase>pre-site</phase>
- <configuration>
- <navigShowtitles>true</navigShowtitles>
- <chunkedOutput>true</chunkedOutput>
- <imgSrcPath>../images/</imgSrcPath>
- <htmlStylesheet>../css/freebsd_docbook.css</htmlStylesheet>
- <targetDirectory>${basedir}/target/docbkx/book</targetDirectory>
- </configuration>
- </execution>
- <execution>
- <id>onepage</id>
- <goals>
- <goal>generate-html</goal>
- </goals>
- <phase>pre-site</phase>
- <configuration>
- <imgSrcPath>images/</imgSrcPath>
- <htmlStylesheet>css/freebsd_docbook.css</htmlStylesheet>
- <targetDirectory>${basedir}/target/docbkx/</targetDirectory>
- </configuration>
- </execution>
- </executions>
- </plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-resources-plugin</artifactId>
@@ -962,18 +913,37 @@
</configuration>
</execution>
<execution>
- <id>copy-docbkx</id>
+ <id>copy-htaccess</id>
<goals>
<goal>copy-resources</goal>
</goals>
- <phase>site</phase>
+ <phase>post-site</phase>
<configuration>
- <outputDirectory>target/site</outputDirectory>
+ <outputDirectory>${basedir}/target/site</outputDirectory>
<resources>
<resource>
- <directory>${basedir}/target/docbkx</directory>
+ <directory>${basedir}/src/main/site/resources/</directory>
<includes>
- <include>**/**</include>
+ <include>.htaccess</include>
+ </includes>
+ </resource>
+ </resources>
+ </configuration>
+ </execution>
+ <!-- needed to make the redirect above work -->
+ <execution>
+ <id>copy-empty-book-dir</id>
+ <goals>
+ <goal>copy-resources</goal>
+ </goals>
+ <phase>post-site</phase>
+ <configuration>
+ <outputDirectory>${basedir}/target/site</outputDirectory>
+ <resources>
+ <resource>
+ <directory>${basedir}/src/main/site/resources/</directory>
+ <includes>
+ <include>book/**</include>
</includes>
</resource>
</resources>
@@ -1008,6 +978,12 @@
<artifactId>velocity</artifactId>
<version>1.7</version>
</dependency>
+ <!-- For building docs from asciidoctor -->
+ <!--<dependency>
+ <groupId>org.asciidoctor</groupId>
+ <artifactId>asciidoctor-maven-plugin</artifactId>
+ <version>1.5.2</version>
+ </dependency>-->
</dependencies>
<configuration>
<siteDirectory>${basedir}/src/main/site</siteDirectory>
@@ -1015,6 +991,81 @@
<outputEncoding>UTF-8</outputEncoding>
</configuration>
</plugin>
+ <!-- For AsciiDoc docs building -->
+ <plugin>
+ <groupId>org.asciidoctor</groupId>
+ <artifactId>asciidoctor-maven-plugin</artifactId>
+ <version>1.5.2</version>
+ <inherited>false</inherited>
+ <dependencies>
+ <dependency>
+ <groupId>org.asciidoctor</groupId>
+ <artifactId>asciidoctorj-pdf</artifactId>
+ <version>1.5.0-alpha.6</version>
+ </dependency>
+ </dependencies>
+ <configuration>
+ <outputDirectory>target/site</outputDirectory>
+ <doctype>book</doctype>
+ <imagesDir>images</imagesDir>
+ <sourceHighlighter>coderay</sourceHighlighter>
+ <attributes>
+ <docVersion>${project.version}</docVersion>
+ </attributes>
+ </configuration>
+ <executions>
+ <execution>
+ <id>output-html</id>
+ <phase>site</phase>
+ <goals>
+ <goal>process-asciidoc</goal>
+ </goals>
+ <configuration>
+ <attributes>
+ <stylesheet>hbase.css</stylesheet>
+ </attributes>
+ <backend>html5</backend>
+ </configuration>
+ </execution>
+ <execution>
+ <id>output-pdf</id>
+ <phase>site</phase>
+ <goals>
+ <goal>process-asciidoc</goal>
+ </goals>
+ <configuration>
+ <backend>pdf</backend>
+ <attributes>
+ <pagenums/>
+ <toc/>
+ <idprefix/>
+ <idseparator>-</idseparator>
+ </attributes>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ <plugin>
+ <artifactId>maven-antrun-plugin</artifactId>
+ <version>${maven.antrun.version}</version>
+ <inherited>false</inherited>
+ <!-- Rename the book.pdf generated by asciidoctor -->
+ <executions>
+ <execution>
+ <id>rename-pdf</id>
+ <phase>post-site</phase>
+ <configuration>
+ <target name="rename file">
+ <move file="${project.basedir}/target/site/book.pdf" tofile="${project.basedir}/target/site/apache_hbase_reference_guide.pdf" />
+ <move file="${project.basedir}/target/site/book.pdfmarks" tofile="${project.basedir}/target/site/apache_hbase_reference_guide.pdfmarks" />
+ </target>
+ </configuration>
+ <goals>
+ <goal>run</goal>
+ </goals>
+ </execution>
+ </executions>
+ </plugin>
<plugin>
<groupId>org.jacoco</groupId>
<artifactId>jacoco-maven-plugin</artifactId>
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc b/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc
new file mode 100644
index 0000000..cb285f3
--- /dev/null
+++ b/src/main/asciidoc/_chapters/appendix_acl_matrix.adoc
@@ -0,0 +1,161 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[appendix_acl_matrix]]
+== Access Control Matrix
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+:toc: left
+:source-language: java
+
+The following matrix shows the permission set required to perform operations in HBase.
+Before using the table, read through the information about how to interpret it.
+
+.Interpreting the ACL Matrix Table
+The following conventions are used in the ACL Matrix table:
+
+=== Scopes
+Permissions are evaluated starting at the widest scope and working to the narrowest scope.
+
+A scope corresponds to a level of the data model. From broadest to narrowest, the scopes are as follows:
+
+.Scopes
+* Global
+* Namespace (NS)
+* Table
+* Column Family (CF)
+* Column Qualifier (CQ)
+* Cell
+
+For instance, a permission granted at table level dominates any grants done at the Column Family, Column Qualifier, or cell level. The user can do what that grant implies at any location in the table. A permission granted at global scope dominates all: the user is always allowed to take that action everywhere.
+
+=== Permissions
+Possible permissions include the following:
+
+.Permissions
+* Superuser - a special user that belongs to group "supergroup" and has unlimited access
+* Admin (A)
+* Create \(C)
+* Write (W)
+* Read \(R)
+* Execute (X)
+
+For the most part, permissions work in an expected way, with the following caveats:
+
+Having Write permission does not imply Read permission.::
+ It is possible and sometimes desirable for a user to be able to write data that same user cannot read. One such example is a log-writing process.
+The [systemitem]+hbase:meta+ table is readable by every user, regardless of the user's other grants or restrictions.::
+ This is a requirement for HBase to function correctly.
+`CheckAndPut` and `CheckAndDelete` operations will fail if the user does not have both Write and Read permission.::
+`Increment` and `Append` operations do not require Read access.::
+The `superuser`, as the name suggests has permissions to perform all possible operations.::
+And for the operations marked with *, the checks are done in post hook and only subset of results satisfying access checks are returned back to the user.::
+
+The following table is sorted by the interface that provides each operation.
+In case the table goes out of date, the unit tests which check for accuracy of permissions can be found in _hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java_, and the access controls themselves can be examined in _hbase-server/src/main/java/org/apache/hadoop/hbase/security/access/AccessController.java_.
+
+.ACL Matrix
+[cols="1,1,1", frame="all", options="header"]
+|===
+| Interface | Operation | Permissions
+| Master | createTable | superuser\|global\(C)\|NS\(C)
+| | modifyTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | deleteTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | truncateTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | addColumn | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | modifyColumn | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)\|column(A)\|column\(C)
+| | deleteColumn | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)\|column(A)\|column\(C)
+| | enableTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | disableTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | disableAclTable | Not allowed
+| | move | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | assign | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | unassign | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | regionOffline | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | balance | superuser\|global(A)
+| | balanceSwitch | superuser\|global(A)
+| | shutdown | superuser\|global(A)
+| | stopMaster | superuser\|global(A)
+| | snapshot | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | listSnapshot | superuser\|global(A)\|SnapshotOwner
+| | cloneSnapshot | superuser\|global(A)
+| | restoreSnapshot | superuser\|global(A)\|SnapshotOwner & (NS(A)\|TableOwner\|table(A))
+| | deleteSnapshot | superuser\|global(A)\|SnapshotOwner
+| | createNamespace | superuser\|global(A)
+| | deleteNamespace | superuser\|global(A)
+| | modifyNamespace | superuser\|global(A)
+| | getNamespaceDescriptor | superuser\|global(A)\|NS(A)
+| | listNamespaceDescriptors* | superuser\|global(A)\|NS(A)
+| | flushTable | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | getTableDescriptors* | superuser\|global(A)\|global\(C)\|NS(A)\|NS\(C)\|TableOwner\|table(A)\|table\(C)
+| | getTableNames* | superuser\|TableOwner\|Any global or table perm
+| | setUserQuota(global level) | superuser\|global(A)
+| | setUserQuota(namespace level) | superuser\|global(A)
+| | setUserQuota(Table level) | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | setTableQuota | superuser\|global(A)\|NS(A)\|TableOwner\|table(A)
+| | setNamespaceQuota | superuser\|global(A)
+| Region | openRegion | superuser\|global(A)
+| | closeRegion | superuser\|global(A)
+| | flush | superuser\|global(A)\|global\(C)\|TableOwner\|table(A)\|table\(C)
+| | split | superuser\|global(A)\|TableOwner\|TableOwner\|table(A)
+| | compact | superuser\|global(A)\|global\(C)\|TableOwner\|table(A)\|table\(C)
+| | getClosestRowBefore | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | getOp | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | exists | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | put | superuser\|global(W)\|NS(W)\|table(W)\|TableOwner\|CF(W)\|CQ(W)
+| | delete | superuser\|global(W)\|NS(W)\|table(W)\|TableOwner\|CF(W)\|CQ(W)
+| | batchMutate | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | checkAndPut | superuser\|global(RW)\|NS(RW)\|TableOwner\|table(RW)\|CF(RW)\|CQ(RW)
+| | checkAndPutAfterRowLock | superuser\|global\(R)\|NS\(R)\|TableOwner\|Table\(R)\|CF\(R)\|CQ\(R)
+| | checkAndDelete | superuser\|global(RW)\|NS(RW)\|TableOwner\|table(RW)\|CF(RW)\|CQ(RW)
+| | checkAndDeleteAfterRowLock | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | incrementColumnValue | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | append | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | appendAfterRowLock | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | increment | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | incrementAfterRowLock | superuser\|global(W)\|NS(W)\|TableOwner\|table(W)\|CF(W)\|CQ(W)
+| | scannerOpen | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | scannerNext | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | scannerClose | superuser\|global\(R)\|NS\(R)\|TableOwner\|table\(R)\|CF\(R)\|CQ\(R)
+| | bulkLoadHFile | superuser\|global\(C)\|TableOwner\|table\(C)\|CF\(C)
+| | prepareBulkLoad | superuser\|global\(C)\|TableOwner\|table\(C)\|CF\(C)
+| | cleanupBulkLoad | superuser\|global\(C)\|TableOwner\|table\(C)\|CF\(C)
+| Endpoint | invoke | superuser\|global(X)\|NS(X)\|TableOwner\|table(X)
+| AccessController | grant(global level) | global(A)
+| | grant(namespace level) | global(A)\|NS(A)
+| | grant(table level) | global(A)\|NS(A)\|TableOwner\|table(A)\|CF(A)\|CQ(A)
+| | revoke(global level) | global(A)
+| | revoke(namespace level) | global(A)\|NS(A)
+| | revoke(table level) | global(A)\|NS(A)\|TableOwner\|table(A)\|CF(A)\|CQ(A)
+| | getUserPermissions(global level) | global(A)
+| | getUserPermissions(namespace level) | global(A)\|NS(A)
+| | getUserPermissions(table level) | global(A)\|NS(A)\|TableOwner\|table(A)\|CF(A)\|CQ(A)
+| RegionServer | stopRegionServer | superuser\|global(A)
+| | mergeRegions | superuser\|global(A)
+| | rollWALWriterRequest | superuser\|global(A)
+| | replicateLogEntries | superuser\|global(W)
+|===
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc b/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc
new file mode 100644
index 0000000..6b31059
--- /dev/null
+++ b/src/main/asciidoc/_chapters/appendix_contributing_to_documentation.adoc
@@ -0,0 +1,395 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[appendix_contributing_to_documentation]]
+== Contributing to Documentation
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+:toc: left
+:source-language: java
+
+The Apache HBase project welcomes contributions to all aspects of the project, including the documentation.
+
+In HBase, documentation includes the following areas, and probably some others:
+
+* The link:http://hbase.apache.org/book.html[HBase Reference
+ Guide] (this book)
+* The link:http://hbase.apache.org/[HBase website]
+* The link:http://wiki.apache.org/hadoop/Hbase[HBase
+ Wiki]
+* API documentation
+* Command-line utility output and help text
+* Web UI strings, explicit help text, context-sensitive strings, and others
+* Log messages
+* Comments in source files, configuration files, and others
+* Localization of any of the above into target languages other than English
+
+No matter which area you want to help out with, the first step is almost always to download (typically by cloning the Git repository) and familiarize yourself with the HBase source code.
+The only exception in the list above is the HBase Wiki, which is edited online.
+For information on downloading and building the source, see <<developer,developer>>.
+
+=== Getting Access to the Wiki
+
+The HBase Wiki is not well-maintained and much of its content has been moved into the HBase Reference Guide (this guide). However, some pages on the Wiki are well maintained, and it would be great to have some volunteers willing to help out with the Wiki.
+To request access to the Wiki, register a new account at link:https://wiki.apache.org/hadoop/Hbase?action=newaccount[https://wiki.apache.org/hadoop/Hbase?action=newaccount].
+Contact one of the HBase committers, who can either give you access or refer you to someone who can.
+
+=== Contributing to Documentation or Other Strings
+
+If you spot an error in a string in a UI, utility, script, log message, or elsewhere, or you think something could be made more clear, or you think text needs to be added where it doesn't currently exist, the first step is to file a JIRA.
+Be sure to set the component to `Documentation` in addition any other involved components.
+Most components have one or more default owners, who monitor new issues which come into those queues.
+Regardless of whether you feel able to fix the bug, you should still file bugs where you see them.
+
+If you want to try your hand at fixing your newly-filed bug, assign it to yourself.
+You will need to clone the HBase Git repository to your local system and work on the issue there.
+When you have developed a potential fix, submit it for review.
+If it addresses the issue and is seen as an improvement, one of the HBase committers will commit it to one or more branches, as appropriate.
+
+.Procedure: Suggested Work flow for Submitting Patches
+This procedure goes into more detail than Git pros will need, but is included in this appendix so that people unfamiliar with Git can feel confident contributing to HBase while they learn.
+
+. If you have not already done so, clone the Git repository locally.
+ You only need to do this once.
+. Fairly often, pull remote changes into your local repository by using the `git pull` command, while your master branch is checked out.
+. For each issue you work on, create a new branch.
+ One convention that works well for naming the branches is to name a given branch the same as the JIRA it relates to:
++
+----
+$ git checkout -b HBASE-123456
+----
+
+. Make your suggested changes on your branch, committing your changes to your local repository often.
+ If you need to switch to working on a different issue, remember to check out the appropriate branch.
+. When you are ready to submit your patch, first be sure that HBase builds cleanly and behaves as expected in your modified branch.
+ If you have made documentation changes, be sure the documentation and website builds by running `mvn clean site`.
++
+NOTE: Before you use the `site` target the very first time, be sure you have built HBase at least once, in order to fetch all the Maven dependencies you need.
++
+----
+$ mvn clean install -DskipTests # Builds HBase
+----
++
+----
+$ mvn clean site -DskipTests # Builds the website and documentation
+----
++
+If any errors occur, address them.
+
+. If it takes you several days or weeks to implement your fix, or you know that the area of the code you are working in has had a lot of changes lately, make sure you rebase your branch against the remote master and take care of any conflicts before submitting your patch.
++
+----
+
+$ git checkout HBASE-123456
+$ git rebase origin/master
+----
+
+. Generate your patch against the remote master.
+ Run the following command from the top level of your git repository (usually called `hbase`):
++
+----
+$ git format-patch --stdout origin/master > HBASE-123456.patch
+----
++
+The name of the patch should contain the JIRA ID.
+Look over the patch file to be sure that you did not change any additional files by accident and that there are no other surprises.
+When you are satisfied, attach the patch to the JIRA and click the btn:[Patch Available] button.
+A reviewer will review your patch.
+If you need to submit a new version of the patch, leave the old one on the JIRA and add a version number to the name of the new patch.
+
+. After a change has been committed, there is no need to keep your local branch around.
+ Instead you should run `git pull` to get the new change into your master branch.
+
+=== Editing the HBase Website
+
+The source for the HBase website is in the HBase source, in the _src/main/site/_ directory.
+Within this directory, source for the individual pages is in the _xdocs/_ directory, and images referenced in those pages are in the _images/_ directory.
+This directory also stores images used in the HBase Reference Guide.
+
+The website's pages are written in an HTML-like XML dialect called xdoc, which has a reference guide at link:http://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html.
+You can edit these files in a plain-text editor, an IDE, or an XML editor such as XML Mind XML Editor (XXE) or Oxygen XML Author.
+
+To preview your changes, build the website using the +mvn clean site
+ -DskipTests+ command.
+The HTML output resides in the _target/site/_ directory.
+When you are satisfied with your changes, follow the procedure in <<submit_doc_patch_procedure,submit doc patch procedure>> to submit your patch.
+
+=== HBase Reference Guide Style Guide and Cheat Sheet
+
+The HBase Reference Guide is written in Asciidoc and built using link:http://asciidoctor.org[AsciiDoctor]. The following cheat sheet is included for your reference. More nuanced and comprehensive documentation is available at link:http://asciidoctor.org/docs/user-manual/.
+
+.AsciiDoc Cheat Sheet
+[cols="1,1,a",options="header"]
+|===
+| Element Type | Desired Rendering | How to do it
+| A paragraph | a paragraph | Just type some text with a blank line at the top and bottom.
+| Add line breaks within a paragraph without adding blank lines | Manual line breaks | This will break + at the plus sign. Or prefix the whole paragraph with a line containing '[%hardbreaks]'
+| Give a title to anything | Colored italic bold differently-sized text | .MyTitle (no space between the period and the words) on the line before the thing to be titled
+| In-Line Code or commands | monospace | \`text`
+| In-line literal content (things to be typed exactly as shown) | bold mono | \*\`typethis`*
+| In-line replaceable content (things to substitute with your own values) | bold italic mono | \*\_typesomething_*
+| Code blocks with highlighting | monospace, highlighted, preserve space |
+........
+[source,java]
+----
+ myAwesomeCode() {
+}
+----
+........
+| Code block included from a separate file | included just as though it were part of the main file |
+................
+[source,ruby]
+----
+include\::path/to/app.rb[]
+----
+................
+| Include only part of a separate file | Similar to Javadoc | See link:http://asciidoctor.org/docs/user-manual/#by-tagged-regions
+| Filenames, directory names, new terms | italic | \_hbase-default.xml_
+| External naked URLs | A link with the URL as link text |
+----
+link:http://www.google.com
+----
+
+| External URLs with text | A link with arbitrary link text |
+----
+link:http://www.google.com[Google]
+----
+
+| Create an internal anchor to cross-reference | not rendered |
+----
+[[anchor_name]]
+----
+| Cross-reference an existing anchor using its default title| an internal hyperlink using the element title if available, otherwise using the anchor name |
+----
+<<anchor_name>>
+----
+| Cross-reference an existing anchor using custom text | an internal hyperlink using arbitrary text |
+----
+<<anchor_name,Anchor Text>>
+----
+| A block image | The image with alt text |
+----
+image::sunset.jpg[Alt Text]
+----
+(put the image in the src/main/site/resources/images directory)
+| An inline image | The image with alt text, as part of the text flow |
+----
+image:sunset.jpg [Alt Text]
+----
+(only one colon)
+| Link to a remote image | show an image hosted elsewhere |
+----
+image::http://inkscape.org/doc/examples/tux.svg[Tux,250,350]
+----
+(or `image:`)
+| Add dimensions or a URL to the image | depends | inside the brackets after the alt text, specify width, height and/or link="http://my_link.com"
+| A footnote | subscript link which takes you to the footnote |
+----
+Some text.footnote:[The footnote text.]
+----
+| A note or warning with no title | The admonition image followed by the admonition |
+----
+NOTE:My note here
+----
+
+----
+WARNING:My warning here
+----
+| A complex note | The note has a title and/or multiple paragraphs and/or code blocks or lists, etc |
+........
+.The Title
+[NOTE]
+====
+Here is the note text. Everything until the second set of four equals signs is part of the note.
+----
+some source code
+----
+====
+........
+| Bullet lists | bullet lists |
+----
+* list item 1
+----
+(see http://asciidoctor.org/docs/user-manual/#unordered-lists)
+| Numbered lists | numbered list |
+----
+. list item 2
+----
+(see http://asciidoctor.org/docs/user-manual/#ordered-lists)
+| Checklists | Checked or unchecked boxes |
+Checked:
+----
+- [*]
+----
+Unchecked:
+----
+- [ ]
+----
+| Multiple levels of lists | bulleted or numbered or combo |
+----
+. Numbered (1), at top level
+* Bullet (2), nested under 1
+* Bullet (3), nested under 1
+. Numbered (4), at top level
+* Bullet (5), nested under 4
+** Bullet (6), nested under 5
+- [x] Checked (7), at top level
+----
+| Labelled lists / variablelists | a list item title or summary followed by content |
+----
+Title:: content
+
+Title::
+ content
+----
+| Sidebars, quotes, or other blocks of text | a block of text, formatted differently from the default | Delimited using different delimiters, see link:http://asciidoctor.org/docs/user-manual/#built-in-blocks-summary. Some of the examples above use delimiters like \...., ----,====.
+........
+[example]
+====
+This is an example block.
+====
+
+[source]
+----
+This is a source block.
+----
+
+[note]
+====
+This is a note block.
+====
+
+[quote]
+____
+This is a quote block.
+____
+........
+
+If you want to insert literal Asciidoc content that keeps being interpreted, when in doubt, use eight dots as the delimiter at the top and bottom.
+| Nested Sections | chapter, section, sub-section, etc |
+----
+= Book (or chapter if the chapter can be built alone, see the leveloffset info below)
+
+== Chapter (or section if the chapter is standalone)
+
+=== Section (or subsection, etc)
+
+==== Subsection
+----
+
+and so on up to 6 levels (think carefully about going deeper than 4 levels, maybe you can just titled paragraphs or lists instead). Note that you can include a book inside another book by adding the `:leveloffset:+1` macro directive directly before your include, and resetting it to 0 directly after. See the _book.adoc_ source for examples, as this is how this guide handles chapters. *Don't do it for prefaces, glossaries, appendixes, or other special types of chapters.*
+
+| Include one file from another | Content is included as though it were inline |
+
+----
+include::[/path/to/file.adoc]
+----
+
+For plenty of examples. see _book.adoc_.
+| A table | a table | See http://asciidoctor.org/docs/user-manual/#tables. Generally rows are separated by newlines and columns by pipes
+| Comment out a single line | A line is skipped during rendering |
+`+//+ This line won't show up`
+| Comment out a block | A section of the file is skipped during rendering |
+----
+////
+Nothing between the slashes will show up.
+////
+----
+| Highlight text for review | text shows up with yellow background |
+----
+Test between #hash marks# is highlighted yellow.
+----
+|===
+
+
+=== Auto-Generated Content
+
+Some parts of the HBase Reference Guide, most notably <<config.files,config.files>>, are generated automatically, so that this area of the documentation stays in sync with the code.
+This is done by means of an XSLT transform, which you can examine in the source at _src/main/xslt/configuration_to_asciidoc_chapter.xsl_.
+This transforms the _hbase-common/src/main/resources/hbase-default.xml_ file into an Asciidoc output which can be included in the Reference Guide.
+Sometimes, it is necessary to add configuration parameters or modify their descriptions.
+Make the modifications to the source file, and they will be included in the Reference Guide when it is rebuilt.
+
+It is possible that other types of content can and will be automatically generated from HBase source files in the future.
+
+
+=== Images in the HBase Reference Guide
+
+You can include images in the HBase Reference Guide. It is important to include an image title if possible, and alternate text always.
+This allows screen readers to navigate to the image and also provides alternative text for the image.
+The following is an example of an image with a title and alternate text. Notice the double colon.
+
+[source,asciidoc]
+----
+.My Image Title
+image::sunset.jpg[Alt Text]
+----
+
+Here is an example of an inline image with alternate text. Notice the single colon. Inline images cannot have titles. They are generally small images like GUI buttons.
+
+[source,asciidoc]
+----
+image:sunset.jpg[Alt Text]
+----
+
+
+When doing a local build, save the image to the _src/main/site/resources/images/_ directory.
+When you link to the image, do not include the directory portion of the path.
+The image will be copied to the appropriate target location during the build of the output.
+
+When you submit a patch which includes adding an image to the HBase Reference Guide, attach the image to the JIRA.
+If the committer asks where the image should be committed, it should go into the above directory.
+
+=== Adding a New Chapter to the HBase Reference Guide
+
+If you want to add a new chapter to the HBase Reference Guide, the easiest way is to copy an existing chapter file, rename it, and change the ID (in double brackets) and title. Chapters are located in the _src/main/asciidoc/_chapters/_ directory.
+
+Delete the existing content and create the new content.
+Then open the _src/main/asciidoc/book.adoc_ file, which is the main file for the HBase Reference Guide, and copy an existing `include` element to include your new chapter in the appropriate location.
+Be sure to add your new file to your Git repository before creating your patch.
+
+When in doubt, check to see how other files have been included.
+
+=== Common Documentation Issues
+
+The following documentation issues come up often.
+Some of these are preferences, but others can create mysterious build errors or other problems.
+
+[qanda]
+Isolate Changes for Easy Diff Review.::
+ Be careful with pretty-printing or re-formatting an entire XML file, even if the formatting has degraded over time. If you need to reformat a file, do that in a separate JIRA where you do not change any content. Be careful because some XML editors do a bulk-reformat when you open a new file, especially if you use GUI mode in the editor.
+
+Syntax Highlighting::
+ The HBase Reference Guide uses `coderay` for syntax highlighting. To enable syntax highlighting for a given code listing, use the following type of syntax:
++
+........
+[source,xml]
+----
+<name>My Name</name>
+----
+........
++
+Several syntax types are supported. The most interesting ones for the HBase Reference Guide are `java`, `xml`, `sql`, and `bash`.
+
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/appendix_hfile_format.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/appendix_hfile_format.adoc b/src/main/asciidoc/_chapters/appendix_hfile_format.adoc
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--- /dev/null
+++ b/src/main/asciidoc/_chapters/appendix_hfile_format.adoc
@@ -0,0 +1,355 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+== HFile format
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+:toc: left
+:source-language: java
+
+This appendix describes the evolution of the HFile format.
+
+[[hfilev1]]
+=== HBase File Format (version 1)
+
+As we will be discussing changes to the HFile format, it is useful to give a short overview of the original (HFile version 1) format.
+
+[[hfilev1.overview]]
+==== Overview of Version 1
+
+An HFile in version 1 format is structured as follows:
+
+.HFile V1 Format
+image::hfile.png[HFile Version 1]
+
+==== Block index format in version 1
+
+The block index in version 1 is very straightforward.
+For each entry, it contains:
+
+. Offset (long)
+. Uncompressed size (int)
+. Key (a serialized byte array written using Bytes.writeByteArray)
+.. Key length as a variable-length integer (VInt)
+.. Key bytes
+
+
+The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to the method that reads the block index.
+One of the limitations of the block index in version 1 is that it does not provide the compressed size of a block, which turns out to be necessary for decompression.
+Therefore, the HFile reader has to infer this compressed size from the offset difference between blocks.
+We fix this limitation in version 2, where we store on-disk block size instead of uncompressed size, and get uncompressed size from the block header.
+
+[[hfilev2]]
+=== HBase file format with inline blocks (version 2)
+
+Note: this feature was introduced in HBase 0.92
+
+==== Motivation
+
+We found it necessary to revise the HFile format after encountering high memory usage and slow startup times caused by large Bloom filters and block indexes in the region server.
+Bloom filters can get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions.
+Block indexes can grow as large as 6 GB in aggregate size over the same set of regions.
+A region is not considered opened until all of its block index data is loaded.
+Large Bloom filters produce a different performance problem: the first get request that requires a Bloom filter lookup will incur the latency of loading the entire Bloom filter bit array.
+
+To speed up region server startup we break Bloom filters and block indexes into multiple blocks and write those blocks out as they fill up, which also reduces the HFile writer's memory footprint.
+In the Bloom filter case, "filling up a block" means accumulating enough keys to efficiently utilize a fixed-size bit array, and in the block index case we accumulate an "index block" of the desired size.
+Bloom filter blocks and index blocks (we call these "inline blocks") become interspersed with data blocks, and as a side effect we can no longer rely on the difference between block offsets to determine data block length, as it was done in version 1.
+
+HFile is a low-level file format by design, and it should not deal with application-specific details such as Bloom filters, which are handled at StoreFile level.
+Therefore, we call Bloom filter blocks in an HFile "inline" blocks.
+We also supply HFile with an interface to write those inline blocks.
+
+Another format modification aimed at reducing the region server startup time is to use a contiguous "load-on-open" section that has to be loaded in memory at the time an HFile is being opened.
+Currently, as an HFile opens, there are separate seek operations to read the trailer, data/meta indexes, and file info.
+To read the Bloom filter, there are two more seek operations for its "data" and "meta" portions.
+In version 2, we seek once to read the trailer and seek again to read everything else we need to open the file from a contiguous block.
+
+[[hfilev2.overview]]
+==== Overview of Version 2
+
+The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only writes version 2 HFiles.
+A version 2 HFile is structured as follows:
+
+.HFile Version 2 Structure
+image:hfilev2.png[HFile Version 2]
+
+==== Unified version 2 block format
+
+In the version 2 every block in the data section contains the following fields:
+
+. 8 bytes: Block type, a sequence of bytes equivalent to version 1's "magic records". Supported block types are:
+.. DATA – data blocks
+.. LEAF_INDEX – leaf-level index blocks in a multi-level-block-index
+.. BLOOM_CHUNK – Bloom filter chunks
+.. META – meta blocks (not used for Bloom filters in version 2 anymore)
+.. INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex
+.. ROOT_INDEX – root>level index blocks in a multi>level block index
+.. FILE_INFO – the ``file info'' block, a small key>value map of metadata
+.. BLOOM_META – a Bloom filter metadata block in the load>on>open section
+.. TRAILER – a fixed>size file trailer.
+ As opposed to the above, this is not an HFile v2 block but a fixed>size (for each HFile version) data structure
+.. INDEX_V1 – this block type is only used for legacy HFile v1 block
+. Compressed size of the block's data, not including the header (int).
++
+Can be used for skipping the current data block when scanning HFile data.
+. Uncompressed size of the block's data, not including the header (int)
++
+This is equal to the compressed size if the compression algorithm is NONE
+. File offset of the previous block of the same type (long)
++
+Can be used for seeking to the previous data/index block
+. Compressed data (or uncompressed data if the compression algorithm is NONE).
+
+The above format of blocks is used in the following HFile sections:
+
+Scanned block section::
+ The section is named so because it contains all data blocks that need to be read when an HFile is scanned sequentially.
+ Also contains leaf block index and Bloom chunk blocks.
+Non-scanned block section::
+ This section still contains unified-format v2 blocks but it does not have to be read when doing a sequential scan.
+ This section contains "meta" blocks and intermediate-level index blocks.
+
+We are supporting "meta" blocks in version 2 the same way they were supported in version 1, even though we do not store Bloom filter data in these blocks anymore.
+
+==== Block index in version 2
+
+There are three types of block indexes in HFile version 2, stored in two different formats (root and non-root):
+
+. Data index -- version 2 multi-level block index, consisting of:
+.. Version 2 root index, stored in the data block index section of the file
+.. Optionally, version 2 intermediate levels, stored in the non%root format in the data index section of the file. Intermediate levels can only be present if leaf level blocks are present
+.. Optionally, version 2 leaf levels, stored in the non%root format inline with data blocks
+. Meta index -- version 2 root index format only, stored in the meta index section of the file
+. Bloom index -- version 2 root index format only, stored in the ``load-on-open'' section as part of Bloom filter metadata.
+
+==== Root block index format in version 2
+
+This format applies to:
+
+. Root level of the version 2 data index
+. Entire meta and Bloom indexes in version 2, which are always single-level.
+
+A version 2 root index block is a sequence of entries of the following format, similar to entries of a version 1 block index, but storing on-disk size instead of uncompressed size.
+
+. Offset (long)
++
+This offset may point to a data block or to a deeper>level index block.
+
+. On-disk size (int)
+. Key (a serialized byte array stored using Bytes.writeByteArray)
++
+. Key (VInt)
+. Key bytes
+
+
+A single-level version 2 block index consists of just a single root index block.
+To read a root index block of version 2, one needs to know the number of entries.
+For the data index and the meta index the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound Bloom filter metadata.
+
+For a multi-level block index we also store the following fields in the root index block in the load-on-open section of the HFile, in addition to the data structure described above:
+
+. Middle leaf index block offset
+. Middle leaf block on-disk size (meaning the leaf index block containing the reference to the ``middle'' data block of the file)
+. The index of the mid-key (defined below) in the middle leaf-level block.
+
+
+
+These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits, which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total number of blocks in the HFile is n.
+This definition is consistent with how the mid-key was determined in HFile version 1, and is reasonable in general, because blocks are likely to be the same size on average, but we don't have any estimates on individual key/value pair sizes.
+
+
+
+When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index block is kept track of.
+When we finish writing and the total number of leaf-level blocks is determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are computed.
+When reading the HFile and the mid-key is requested, we retrieve the middle leaf index block (potentially from the block cache) and get the mid-key value from the appropriate position inside that leaf block.
+
+==== Non-root block index format in version 2
+
+This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block index.
+Every non-root index block is structured as follows.
+
+. numEntries: the number of entries (int).
+. entryOffsets: the ``secondary index'' of offsets of entries in the block, to facilitate a quick binary search on the key (numEntries + 1 int values). The last value is the total length of all entries in this index block.
+ For example, in a non-root index block with entry sizes 60, 80, 50 the ``secondary index'' will contain the following int array: {0, 60, 140, 190}.
+. Entries.
+ Each entry contains:
++
+. Offset of the block referenced by this entry in the file (long)
+. On>disk size of the referenced block (int)
+. Key.
+ The length can be calculated from entryOffsets.
+
+
+==== Bloom filters in version 2
+
+In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open section of the HFile for quick startup.
+
+. A compound Bloom filter.
++
+. Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the Bloom filter version number 2
+. The total byte size of all compound Bloom filter chunks (long)
+. Number of hash functions (int
+. Type of hash functions (int)
+. The total key count inserted into the Bloom filter (long)
+. The maximum total number of keys in the Bloom filter (long)
+. The number of chunks (int)
+. Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using Bytes.writeByteArray
+. Bloom block index in the version 2 root block index format
+
+
+==== File Info format in versions 1 and 2
+
+The file info block is a serialized link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/HbaseMapWritable.html[HbaseMapWritable] (essentially a map from byte arrays to byte arrays) with the following keys, among others.
+StoreFile-level logic adds more keys to this.
+
+[cols="1,1", frame="all"]
+|===
+|hfile.LASTKEY| The last key of the file (byte array)
+|hfile.AVG_KEY_LEN| The average key length in the file (int)
+|hfile.AVG_VALUE_LEN| The average value length in the file (int)
+|===
+
+File info format did not change in version 2.
+However, we moved the file info to the final section of the file, which can be loaded as one block at the time the HFile is being opened.
+Also, we do not store comparator in the version 2 file info anymore.
+Instead, we store it in the fixed file trailer.
+This is because we need to know the comparator at the time of parsing the load-on-open section of the HFile.
+
+==== Fixed file trailer format differences between versions 1 and 2
+
+The following table shows common and different fields between fixed file trailers in versions 1 and 2.
+Note that the size of the trailer is different depending on the version, so it is ``fixed'' only within one version.
+However, the version is always stored as the last four-byte integer in the file.
+
+.Differences between HFile Versions 1 and 2
+[cols="1,1", frame="all"]
+|===
+| Version 1 | Version 2
+| |File info offset (long)
+| Data index offset (long)| loadOnOpenOffset (long) /The offset of the sectionthat we need toload when opening the file./
+| | Number of data index entries (int)
+| metaIndexOffset (long) /This field is not being used by the version 1 reader, so we removed it from version 2./ | uncompressedDataIndexSize (long) /The total uncompressed size of the whole data block index, including root-level, intermediate-level, and leaf-level blocks./
+| | Number of meta index entries (int)
+| | Total uncompressed bytes (long)
+| numEntries (int) | numEntries (long)
+| Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int) | Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int)
+| | The number of levels in the data block index (int)
+| | firstDataBlockOffset (long) /The offset of the first first data block. Used when scanning./
+| | lastDataBlockEnd (long) /The offset of the first byte after the last key/value data block. We don't need to go beyond this offset when scanning./
+| Version: 1 (int) | Version: 2 (int)
+|===
+
+
+
+==== getShortMidpointKey(an optimization for data index block)
+
+Note: this optimization was introduced in HBase 0.95+
+
+HFiles contain many blocks that contain a range of sorted Cells.
+Each cell has a key.
+To save IO when reading Cells, the HFile also has an index that maps a Cell's start key to the offset of the beginning of a particular block.
+Prior to this optimization, HBase would use the key of the first cell in each data block as the index key.
+
+In HBASE-7845, we generate a new key that is lexicographically larger than the last key of the previous block and lexicographically equal or smaller than the start key of the current block.
+While actual keys can potentially be very long, this "fake key" or "virtual key" can be much shorter.
+For example, if the stop key of previous block is "the quick brown fox", the start key of current block is "the who", we could use "the r" as our virtual key in our hfile index.
+
+There are two benefits to this:
+
+* having shorter keys reduces the hfile index size, (allowing us to keep more indexes in memory), and
+* using something closer to the end key of the previous block allows us to avoid a potential extra IO when the target key lives in between the "virtual key" and the key of the first element in the target block.
+
+This optimization (implemented by the getShortMidpointKey method) is inspired by LevelDB's ByteWiseComparatorImpl::FindShortestSeparator() and FindShortSuccessor().
+
+[[hfilev3]]
+=== HBase File Format with Security Enhancements (version 3)
+
+Note: this feature was introduced in HBase 0.98
+
+[[hfilev3.motivation]]
+==== Motivation
+
+Version 3 of HFile makes changes needed to ease management of encryption at rest and cell-level metadata (which in turn is needed for cell-level ACLs and cell-level visibility labels). For more information see <<hbase.encryption.server,hbase.encryption.server>>, <<hbase.tags,hbase.tags>>, <<hbase.accesscontrol.configuration,hbase.accesscontrol.configuration>>, and <<hbase.visibility.labels,hbase.visibility.labels>>.
+
+[[hfilev3.overview]]
+==== Overview
+
+The version of HBase introducing the above features reads HFiles in versions 1, 2, and 3 but only writes version 3 HFiles.
+Version 3 HFiles are structured the same as version 2 HFiles.
+For more information see <<hfilev2.overview,hfilev2.overview>>.
+
+[[hvilev3.infoblock]]
+==== File Info Block in Version 3
+
+Version 3 added two additional pieces of information to the reserved keys in the file info block.
+
+[cols="1,1", frame="all"]
+|===
+| hfile.MAX_TAGS_LEN | The maximum number of bytes needed to store the serialized tags for any single cell in this hfile (int)
+ | hfile.TAGS_COMPRESSED | Does the block encoder for this hfile compress tags? (boolean). Should only be present if hfile.MAX_TAGS_LEN is also present.
+|===
+
+When reading a Version 3 HFile the presence of `MAX_TAGS_LEN` is used to determine how to deserialize the cells within a data block.
+Therefore, consumers must read the file's info block prior to reading any data blocks.
+
+When writing a Version 3 HFile, HBase will always include `MAX_TAGS_LEN ` when flushing the memstore to underlying filesystem and when using prefix tree encoding for data blocks, as described in <<compression,compression>>.
+
+When compacting extant files, the default writer will omit `MAX_TAGS_LEN` if all of the files selected do not themselves contain any cells with tags.
+
+See <<compaction,compaction>> for details on the compaction file selection algorithm.
+
+[[hfilev3.datablock]]
+==== Data Blocks in Version 3
+
+Within an HFile, HBase cells are stored in data blocks as a sequence of KeyValues (see <<hfilev1.overview,hfilev1.overview>>, or link:http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html[Lars George's
+ excellent introduction to HBase Storage]). In version 3, these KeyValue optionally will include a set of 0 or more tags:
+
+[cols="1,1", frame="all"]
+|===
+| Version 1 & 2, Version 3 without MAX_TAGS_LEN | Version 3 with MAX_TAGS_LEN
+2+| Key Length (4 bytes)
+2+| Value Length (4 bytes)
+2+| Key bytes (variable)
+2+| Value bytes (variable)
+| | Tags Length (2 bytes)
+| | Tags bytes (variable)
+|===
+
+If the info block for a given HFile contains an entry for `MAX_TAGS_LEN` each cell will have the length of that cell's tags included, even if that length is zero.
+The actual tags are stored as a sequence of tag length (2 bytes), tag type (1 byte), tag bytes (variable). The format an individual tag's bytes depends on the tag type.
+
+Note that the dependence on the contents of the info block implies that prior to reading any data blocks you must first process a file's info block.
+It also implies that prior to writing a data block you must know if the file's info block will include `MAX_TAGS_LEN`.
+
+[[hfilev3.fixedtrailer]]
+==== Fixed File Trailer in Version 3
+
+The fixed file trailers written with HFile version 3 are always serialized with protocol buffers.
+Additionally, it adds an optional field to the version 2 protocol buffer named encryption_key.
+If HBase is configured to encrypt HFiles this field will store a data encryption key for this particular HFile, encrypted with the current cluster master key using AES.
+For more information see <<hbase.encryption.server,hbase.encryption.server>>.
+
+:numbered:
[15/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/configuration.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/configuration.adoc b/src/main/asciidoc/_chapters/configuration.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[configuration]]
+= Apache HBase Configuration
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter expands upon the <<getting_started>> chapter to further explain configuration of Apache HBase.
+Please read this chapter carefully, especially the <<basic.prerequisites,Basic Prerequisites>> to ensure that your HBase testing and deployment goes smoothly, and prevent data loss.
+
+== Configuration Files
+Apache HBase uses the same configuration system as Apache Hadoop.
+All configuration files are located in the _conf/_ directory, which needs to be kept in sync for each node on your cluster.
+
+.HBase Configuration File Descriptions
+_backup-masters_::
+ Not present by default.
+ A plain-text file which lists hosts on which the Master should start a backup Master process, one host per line.
+
+_hadoop-metrics2-hbase.properties_::
+ Used to connect HBase Hadoop's Metrics2 framework.
+ See the link:http://wiki.apache.org/hadoop/HADOOP-6728-MetricsV2[Hadoop Wiki entry] for more information on Metrics2.
+ Contains only commented-out examples by default.
+
+_hbase-env.cmd_ and _hbase-env.sh_::
+ Script for Windows and Linux / Unix environments to set up the working environment for HBase, including the location of Java, Java options, and other environment variables.
+ The file contains many commented-out examples to provide guidance.
+
+_hbase-policy.xml_::
+ The default policy configuration file used by RPC servers to make authorization decisions on client requests.
+ Only used if HBase <<security,security>> is enabled.
+
+_hbase-site.xml_::
+ The main HBase configuration file.
+ This file specifies configuration options which override HBase's default configuration.
+ You can view (but do not edit) the default configuration file at _docs/hbase-default.xml_.
+ You can also view the entire effective configuration for your cluster (defaults and overrides) in the [label]#HBase Configuration# tab of the HBase Web UI.
+
+_log4j.properties_::
+ Configuration file for HBase logging via `log4j`.
+
+_regionservers_::
+ A plain-text file containing a list of hosts which should run a RegionServer in your HBase cluster.
+ By default this file contains the single entry `localhost`.
+ It should contain a list of hostnames or IP addresses, one per line, and should only contain `localhost` if each node in your cluster will run a RegionServer on its `localhost` interface.
+
+.Checking XML Validity
+[TIP]
+====
+When you edit XML, it is a good idea to use an XML-aware editor to be sure that your syntax is correct and your XML is well-formed.
+You can also use the `xmllint` utility to check that your XML is well-formed.
+By default, `xmllint` re-flows and prints the XML to standard output.
+To check for well-formedness and only print output if errors exist, use the command `xmllint -noout filename.xml`.
+====
+.Keep Configuration In Sync Across the Cluster
+[WARNING]
+====
+When running in distributed mode, after you make an edit to an HBase configuration, make sure you copy the content of the _conf/_ directory to all nodes of the cluster.
+HBase will not do this for you.
+Use `rsync`, `scp`, or another secure mechanism for copying the configuration files to your nodes.
+For most configuration, a restart is needed for servers to pick up changes An exception is dynamic configuration.
+to be described later below.
+====
+
+[[basic.prerequisites]]
+== Basic Prerequisites
+
+This section lists required services and some required system configuration.
+
+[[java]]
+.Java
+[cols="1,1,1,4", options="header"]
+|===
+|HBase Version
+|JDK 6
+|JDK 7
+|JDK 8
+
+|1.1
+|link:http://search-hadoop.com/m/DHED4Zlz0R1[Not Supported]
+|yes
+|Running with JDK 8 will work but is not well tested.
+
+|1.0
+|link:http://search-hadoop.com/m/DHED4Zlz0R1[Not Supported]
+|yes
+|Running with JDK 8 will work but is not well tested.
+
+|0.98
+|yes
+|yes
+|Running with JDK 8 works but is not well tested. Building with JDK 8 would require removal of the
+deprecated `remove()` method of the `PoolMap` class and is under consideration. See
+link:https://issues.apache.org/jira/browse/HBASE-7608[HBASE-7608] for more information about JDK 8
+support.
+
+|0.96
+|yes
+|yes
+|N/A
+
+|0.94
+|yes
+|yes
+|N/A
+|===
+
+NOTE: In HBase 0.98.5 and newer, you must set `JAVA_HOME` on each node of your cluster. _hbase-env.sh_ provides a handy mechanism to do this.
+
+.Operating System Utilities
+ssh::
+ HBase uses the Secure Shell (ssh) command and utilities extensively to communicate between cluster nodes. Each server in the cluster must be running `ssh` so that the Hadoop and HBase daemons can be managed. You must be able to connect to all nodes via SSH, including the local node, from the Master as well as any backup Master, using a shared key rather than a password. You can see the basic methodology for such a set-up in Linux or Unix systems at "<<passwordless.ssh.quickstart>>". If your cluster nodes use OS X, see the section, link:http://wiki.apache.org/hadoop/Running_Hadoop_On_OS_X_10.5_64-bit_%28Single-Node_Cluster%29[SSH: Setting up Remote Desktop and Enabling Self-Login] on the Hadoop wiki.
+
+DNS::
+ HBase uses the local hostname to self-report its IP address. Both forward and reverse DNS resolving must work in versions of HBase previous to 0.92.0. The link:https://github.com/sujee/hadoop-dns-checker[hadoop-dns-checker] tool can be used to verify DNS is working correctly on the cluster. The project `README` file provides detailed instructions on usage.
+
+Loopback IP::
+ Prior to hbase-0.96.0, HBase only used the IP address `127.0.0.1` to refer to `localhost`, and this could not be configured.
+ See <<loopback.ip,Loopback IP>> for more details.
+
+NTP::
+ The clocks on cluster nodes should be synchronized. A small amount of variation is acceptable, but larger amounts of skew can cause erratic and unexpected behavior. Time synchronization is one of the first things to check if you see unexplained problems in your cluster. It is recommended that you run a Network Time Protocol (NTP) service, or another time-synchronization mechanism, on your cluster, and that all nodes look to the same service for time synchronization. See the link:http://www.tldp.org/LDP/sag/html/basic-ntp-config.html[Basic NTP Configuration] at [citetitle]_The Linux Documentation Project (TLDP)_ to set up NTP.
+
+Limits on Number of Files and Processes (ulimit)::
+ Apache HBase is a database. It requires the ability to open a large number of files at once. Many Linux distributions limit the number of files a single user is allowed to open to `1024` (or `256` on older versions of OS X). You can check this limit on your servers by running the command `ulimit -n` when logged in as the user which runs HBase. See <<trouble.rs.runtime.filehandles,the Troubleshooting section>> for some of the problems you may experience if the limit is too low. You may also notice errors such as the following:
++
+----
+2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException
+2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901
+----
++
+It is recommended to raise the ulimit to at least 10,000, but more likely 10,240, because the value is usually expressed in multiples of 1024. Each ColumnFamily has at least one StoreFile, and possibly more than six StoreFiles if the region is under load. The number of open files required depends upon the number of ColumnFamilies and the number of regions. The following is a rough formula for calculating the potential number of open files on a RegionServer.
++
+.Calculate the Potential Number of Open Files
+----
+(StoreFiles per ColumnFamily) x (regions per RegionServer)
+----
++
+For example, assuming that a schema had 3 ColumnFamilies per region with an average of 3 StoreFiles per ColumnFamily, and there are 100 regions per RegionServer, the JVM will open `3 * 3 * 100 = 900` file descriptors, not counting open JAR files, configuration files, and others. Opening a file does not take many resources, and the risk of allowing a user to open too many files is minimal.
++
+Another related setting is the number of processes a user is allowed to run at once. In Linux and Unix, the number of processes is set using the `ulimit -u` command. This should not be confused with the `nproc` command, which controls the number of CPUs available to a given user. Under load, a `ulimit -u` that is too low can cause OutOfMemoryError exceptions. See Jack Levin's major HDFS issues thread on the hbase-users mailing list, from 2011.
++
+Configuring the maximum number of file descriptors and processes for the user who is running the HBase process is an operating system configuration, rather than an HBase configuration. It is also important to be sure that the settings are changed for the user that actually runs HBase. To see which user started HBase, and that user's ulimit configuration, look at the first line of the HBase log for that instance. A useful read setting config on you hadoop cluster is Aaron Kimballs' Configuration Parameters: What can you just ignore?
++
+.`ulimit` Settings on Ubuntu
+====
+To configure ulimit settings on Ubuntu, edit _/etc/security/limits.conf_, which is a space-delimited file with four columns. Refer to the man page for _limits.conf_ for details about the format of this file. In the following example, the first line sets both soft and hard limits for the number of open files (nofile) to 32768 for the operating system user with the username hadoop. The second line sets the number of processes to 32000 for the same user.
+----
+hadoop - nofile 32768
+hadoop - nproc 32000
+----
+The settings are only applied if the Pluggable Authentication Module (PAM) environment is directed to use them. To configure PAM to use these limits, be sure that the _/etc/pam.d/common-session_ file contains the following line:
+----
+session required pam_limits.so
+----
+====
+
+Linux Shell::
+ All of the shell scripts that come with HBase rely on the link:http://www.gnu.org/software/bash[GNU Bash] shell.
+
+Windows::
+ Prior to HBase 0.96, testing for running HBase on Microsoft Windows was limited.
+ Running a on Windows nodes is not recommended for production systems.
+
+
+[[hadoop]]
+=== link:http://hadoop.apache.org[Hadoop](((Hadoop)))
+
+The following table summarizes the versions of Hadoop supported with each version of HBase.
+Based on the version of HBase, you should select the most appropriate version of Hadoop.
+You can use Apache Hadoop, or a vendor's distribution of Hadoop.
+No distinction is made here.
+See link:http://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support[the Hadoop wiki] for information about vendors of Hadoop.
+
+.Hadoop 2.x is recommended.
+[TIP]
+====
+Hadoop 2.x is faster and includes features, such as short-circuit reads, which will help improve your HBase random read profile.
+Hadoop 2.x also includes important bug fixes that will improve your overall HBase experience.
+HBase 0.98 drops support for Hadoop 1.0, deprecates use of Hadoop 1.1+, and HBase 1.0 will not support Hadoop 1.x.
+====
+
+Use the following legend to interpret this table:
+
+.Hadoop version support matrix
+
+* "S" = supported
+* "X" = not supported
+* "NT" = Not tested
+
+[cols="1,1,1,1,1,1,1", options="header"]
+|===
+| | HBase-0.92.x | HBase-0.94.x | HBase-0.96.x | HBase-0.98.x (Support for Hadoop 1.1+ is deprecated.) | HBase-1.0.x (Hadoop 1.x is NOT supported) | HBase-1.1.x
+|Hadoop-0.20.205 | S | X | X | X | X | X
+|Hadoop-0.22.x | S | X | X | X | X | X
+|Hadoop-1.0.x |X | X | X | X | X | X
+|Hadoop-1.1.x | NT | S | S | NT | X | X
+|Hadoop-0.23.x | X | S | NT | X | X | X
+|Hadoop-2.0.x-alpha | X | NT | X | X | X | X
+|Hadoop-2.1.0-beta | X | NT | S | X | X | X
+|Hadoop-2.2.0 | X | NT | S | S | NT | NT
+|Hadoop-2.3.x | X | NT | S | S | NT | NT
+|Hadoop-2.4.x | X | NT | S | S | S | S
+|Hadoop-2.5.x | X | NT | S | S | S | S
+|Hadoop-2.6.x | X | NT | NT | NT | S | S
+|Hadoop-2.7.x | X | NT | NT | NT | NT | NT
+|===
+
+.Replace the Hadoop Bundled With HBase!
+[NOTE]
+====
+Because HBase depends on Hadoop, it bundles an instance of the Hadoop jar under its _lib_ directory.
+The bundled jar is ONLY for use in standalone mode.
+In distributed mode, it is _critical_ that the version of Hadoop that is out on your cluster match what is under HBase.
+Replace the hadoop jar found in the HBase lib directory with the hadoop jar you are running on your cluster to avoid version mismatch issues.
+Make sure you replace the jar in HBase everywhere on your cluster.
+Hadoop version mismatch issues have various manifestations but often all looks like its hung up.
+====
+
+[[hadoop2.hbase_0.94]]
+==== Apache HBase 0.94 with Hadoop 2
+
+To get 0.94.x to run on Hadoop 2.2.0, you need to change the hadoop 2 and protobuf versions in the _pom.xml_: Here is a diff with pom.xml changes:
+
+[source]
+----
+$ svn diff pom.xml
+Index: pom.xml
+===================================================================
+--- pom.xml (revision 1545157)
++++ pom.xml (working copy)
+@@ -1034,7 +1034,7 @@
+ <slf4j.version>1.4.3</slf4j.version>
+ <log4j.version>1.2.16</log4j.version>
+ <mockito-all.version>1.8.5</mockito-all.version>
+- <protobuf.version>2.4.0a</protobuf.version>
++ <protobuf.version>2.5.0</protobuf.version>
+ <stax-api.version>1.0.1</stax-api.version>
+ <thrift.version>0.8.0</thrift.version>
+ <zookeeper.version>3.4.5</zookeeper.version>
+@@ -2241,7 +2241,7 @@
+ </property>
+ </activation>
+ <properties>
+- <hadoop.version>2.0.0-alpha</hadoop.version>
++ <hadoop.version>2.2.0</hadoop.version>
+ <slf4j.version>1.6.1</slf4j.version>
+ </properties>
+ <dependencies>
+----
+
+The next step is to regenerate Protobuf files and assuming that the Protobuf has been installed:
+
+* Go to the HBase root folder, using the command line;
+* Type the following commands:
++
+
+[source,bourne]
+----
+$ protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/hbase.proto
+----
++
+
+[source,bourne]
+----
+$ protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/ErrorHandling.proto
+----
+
+
+Building against the hadoop 2 profile by running something like the following command:
+
+----
+$ mvn clean install assembly:single -Dhadoop.profile=2.0 -DskipTests
+----
+
+[[hadoop.hbase_0.94]]
+==== Apache HBase 0.92 and 0.94
+
+HBase 0.92 and 0.94 versions can work with Hadoop versions, 0.20.205, 0.22.x, 1.0.x, and 1.1.x.
+HBase-0.94 can additionally work with Hadoop-0.23.x and 2.x, but you may have to recompile the code using the specific maven profile (see top level pom.xml)
+
+[[hadoop.hbase_0.96]]
+==== Apache HBase 0.96
+
+As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required.
+Hadoop 2 is strongly encouraged (faster but also has fixes that help MTTR). We will no longer run properly on older Hadoops such as 0.20.205 or branch-0.20-append.
+Do not move to Apache HBase 0.96.x if you cannot upgrade your Hadoop. See link:http://search-hadoop.com/m/7vFVx4EsUb2[HBase, mail # dev - DISCUSS:
+ Have hbase require at least hadoop 1.0.0 in hbase 0.96.0?]
+
+[[hadoop.older.versions]]
+==== Hadoop versions 0.20.x - 1.x
+
+HBase will lose data unless it is running on an HDFS that has a durable `sync` implementation.
+DO NOT use Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0 which DO NOT have this attribute.
+Currently only Hadoop versions 0.20.205.x or any release in excess of this version -- this includes hadoop-1.0.0 -- have a working, durable sync.
+The Cloudera blog post link:http://www.cloudera.com/blog/2012/01/an-update-on-apache-hadoop-1-0/[An
+ update on Apache Hadoop 1.0] by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate.
+It's worth checking out if you are having trouble making sense of the Hadoop version morass.
+
+Sync has to be explicitly enabled by setting `dfs.support.append` equal to true on both the client side -- in _hbase-site.xml_ -- and on the serverside in _hdfs-site.xml_ (The sync facility HBase needs is a subset of the append code path).
+
+[source,xml]
+----
+
+<property>
+ <name>dfs.support.append</name>
+ <value>true</value>
+</property>
+----
+
+You will have to restart your cluster after making this edit.
+Ignore the chicken-little comment you'll find in the _hdfs-default.xml_ in the description for the `dfs.support.append` configuration.
+
+[[hadoop.security]]
+==== Apache HBase on Secure Hadoop
+
+Apache HBase will run on any Hadoop 0.20.x that incorporates Hadoop security features as long as you do as suggested above and replace the Hadoop jar that ships with HBase with the secure version.
+If you want to read more about how to setup Secure HBase, see <<hbase.secure.configuration,hbase.secure.configuration>>.
+
+
+[[dfs.datanode.max.transfer.threads]]
+==== `dfs.datanode.max.transfer.threads` (((dfs.datanode.max.transfer.threads)))
+
+An HDFS DataNode has an upper bound on the number of files that it will serve at any one time.
+Before doing any loading, make sure you have configured Hadoop's _conf/hdfs-site.xml_, setting the `dfs.datanode.max.transfer.threads` value to at least the following:
+
+[source,xml]
+----
+
+<property>
+ <name>dfs.datanode.max.transfer.threads</name>
+ <value>4096</value>
+</property>
+----
+
+Be sure to restart your HDFS after making the above configuration.
+
+Not having this configuration in place makes for strange-looking failures.
+One manifestation is a complaint about missing blocks.
+For example:
+
+----
+10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
+ blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No live nodes
+ contain current block. Will get new block locations from namenode and retry...
+----
+
+See also <<casestudies.max.transfer.threads,casestudies.max.transfer.threads>> and note that this property was previously known as `dfs.datanode.max.xcievers` (e.g. link:http://ccgtech.blogspot.com/2010/02/hadoop-hdfs-deceived-by-xciever.html[Hadoop HDFS: Deceived by Xciever]).
+
+[[zookeeper.requirements]]
+=== ZooKeeper Requirements
+
+ZooKeeper 3.4.x is required as of HBase 1.0.0.
+HBase makes use of the `multi` functionality that is only available since 3.4.0 (The `useMulti` configuration option defaults to `true` in HBase 1.0.0).
+See link:https://issues.apache.org/jira/browse/HBASE-12241[HBASE-12241 (The crash of regionServer when taking deadserver's replication queue breaks replication)] and link:https://issues.apache.org/jira/browse/HBASE-6775[HBASE-6775 (Use ZK.multi when available for HBASE-6710 0.92/0.94 compatibility fix)] for background.
+
+[[standalone_dist]]
+== HBase run modes: Standalone and Distributed
+
+HBase has two run modes: <<standalone,standalone>> and <<distributed,distributed>>.
+Out of the box, HBase runs in standalone mode.
+Whatever your mode, you will need to configure HBase by editing files in the HBase _conf_ directory.
+At a minimum, you must edit [code]+conf/hbase-env.sh+ to tell HBase which +java+ to use.
+In this file you set HBase environment variables such as the heapsize and other options for the `JVM`, the preferred location for log files, etc.
+Set [var]+JAVA_HOME+ to point at the root of your +java+ install.
+
+[[standalone]]
+=== Standalone HBase
+
+This is the default mode.
+Standalone mode is what is described in the <<quickstart,quickstart>> section.
+In standalone mode, HBase does not use HDFS -- it uses the local filesystem instead -- and it runs all HBase daemons and a local ZooKeeper all up in the same JVM.
+Zookeeper binds to a well known port so clients may talk to HBase.
+
+=== Distributed
+
+Distributed mode can be subdivided into distributed but all daemons run on a single node -- a.k.a _pseudo-distributed_ -- and _fully-distributed_ where the daemons are spread across all nodes in the cluster.
+The _pseudo-distributed_ vs. _fully-distributed_ nomenclature comes from Hadoop.
+
+Pseudo-distributed mode can run against the local filesystem or it can run against an instance of the _Hadoop Distributed File System_ (HDFS). Fully-distributed mode can ONLY run on HDFS.
+See the Hadoop link:http://hadoop.apache.org/docs/current/[documentation] for how to set up HDFS.
+A good walk-through for setting up HDFS on Hadoop 2 can be found at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide.
+
+[[pseudo]]
+==== Pseudo-distributed
+
+.Pseudo-Distributed Quickstart
+[NOTE]
+====
+A quickstart has been added to the <<quickstart,quickstart>> chapter.
+See <<quickstart_pseudo,quickstart-pseudo>>.
+Some of the information that was originally in this section has been moved there.
+====
+
+A pseudo-distributed mode is simply a fully-distributed mode run on a single host.
+Use this configuration testing and prototyping on HBase.
+Do not use this configuration for production nor for evaluating HBase performance.
+
+[[fully_dist]]
+=== Fully-distributed
+
+By default, HBase runs in standalone mode.
+Both standalone mode and pseudo-distributed mode are provided for the purposes of small-scale testing.
+For a production environment, distributed mode is appropriate.
+In distributed mode, multiple instances of HBase daemons run on multiple servers in the cluster.
+
+Just as in pseudo-distributed mode, a fully distributed configuration requires that you set the `hbase-cluster.distributed` property to `true`.
+Typically, the `hbase.rootdir` is configured to point to a highly-available HDFS filesystem.
+
+In addition, the cluster is configured so that multiple cluster nodes enlist as RegionServers, ZooKeeper QuorumPeers, and backup HMaster servers.
+These configuration basics are all demonstrated in <<quickstart_fully_distributed,quickstart-fully-distributed>>.
+
+.Distributed RegionServers
+Typically, your cluster will contain multiple RegionServers all running on different servers, as well as primary and backup Master and Zookeeper daemons.
+The _conf/regionservers_ file on the master server contains a list of hosts whose RegionServers are associated with this cluster.
+Each host is on a separate line.
+All hosts listed in this file will have their RegionServer processes started and stopped when the master server starts or stops.
+
+.ZooKeeper and HBase
+See the <<zookeeper,ZooKeeper>> section for ZooKeeper setup instructions for HBase.
+
+.Example Distributed HBase Cluster
+====
+This is a bare-bones _conf/hbase-site.xml_ for a distributed HBase cluster.
+A cluster that is used for real-world work would contain more custom configuration parameters.
+Most HBase configuration directives have default values, which are used unless the value is overridden in the _hbase-site.xml_.
+See "<<config.files,Configuration Files>>" for more information.
+
+[source,xml]
+----
+
+<configuration>
+ <property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://namenode.example.org:8020/hbase</value>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
+ </property>
+</configuration>
+----
+
+This is an example _conf/regionservers_ file, which contains a list of nodes that should run a RegionServer in the cluster.
+These nodes need HBase installed and they need to use the same contents of the _conf/_ directory as the Master server
+
+[source]
+----
+
+node-a.example.com
+node-b.example.com
+node-c.example.com
+----
+
+This is an example _conf/backup-masters_ file, which contains a list of each node that should run a backup Master instance.
+The backup Master instances will sit idle unless the main Master becomes unavailable.
+
+[source]
+----
+
+node-b.example.com
+node-c.example.com
+----
+====
+
+.Distributed HBase Quickstart
+See <<quickstart_fully_distributed,quickstart-fully-distributed>> for a walk-through of a simple three-node cluster configuration with multiple ZooKeeper, backup HMaster, and RegionServer instances.
+
+.Procedure: HDFS Client Configuration
+. Of note, if you have made HDFS client configuration changes on your Hadoop cluster, such as configuration directives for HDFS clients, as opposed to server-side configurations, you must use one of the following methods to enable HBase to see and use these configuration changes:
++
+a. Add a pointer to your `HADOOP_CONF_DIR` to the `HBASE_CLASSPATH` environment variable in _hbase-env.sh_.
+b. Add a copy of _hdfs-site.xml_ (or _hadoop-site.xml_) or, better, symlinks, under _${HBASE_HOME}/conf_, or
+c. if only a small set of HDFS client configurations, add them to _hbase-site.xml_.
+
+
+An example of such an HDFS client configuration is `dfs.replication`.
+If for example, you want to run with a replication factor of 5, HBase will create files with the default of 3 unless you do the above to make the configuration available to HBase.
+
+[[confirm]]
+== Running and Confirming Your Installation
+
+Make sure HDFS is running first.
+Start and stop the Hadoop HDFS daemons by running _bin/start-hdfs.sh_ over in the `HADOOP_HOME` directory.
+You can ensure it started properly by testing the `put` and `get` of files into the Hadoop filesystem.
+HBase does not normally use the MapReduce or YARN daemons. These do not need to be started.
+
+_If_ you are managing your own ZooKeeper, start it and confirm it's running, else HBase will start up ZooKeeper for you as part of its start process.
+
+Start HBase with the following command:
+
+----
+bin/start-hbase.sh
+----
+
+Run the above from the `HBASE_HOME` directory.
+
+You should now have a running HBase instance.
+HBase logs can be found in the _logs_ subdirectory.
+Check them out especially if HBase had trouble starting.
+
+HBase also puts up a UI listing vital attributes.
+By default it's deployed on the Master host at port 16010 (HBase RegionServers listen on port 16020 by default and put up an informational HTTP server at port 16030). If the Master is running on a host named `master.example.org` on the default port, point your browser at _http://master.example.org:16010_ to see the web interface.
+
+Prior to HBase 0.98 the master UI was deployed on port 60010, and the HBase RegionServers UI on port 60030.
+
+Once HBase has started, see the <<shell_exercises,shell exercises>> section for how to create tables, add data, scan your insertions, and finally disable and drop your tables.
+
+To stop HBase after exiting the HBase shell enter
+
+----
+$ ./bin/stop-hbase.sh
+stopping hbase...............
+----
+
+Shutdown can take a moment to complete.
+It can take longer if your cluster is comprised of many machines.
+If you are running a distributed operation, be sure to wait until HBase has shut down completely before stopping the Hadoop daemons.
+
+[[config.files]]
+== Default Configuration
+
+[[hbase.site]]
+=== _hbase-site.xml_ and _hbase-default.xml_
+
+Just as in Hadoop where you add site-specific HDFS configuration to the _hdfs-site.xml_ file, for HBase, site specific customizations go into the file _conf/hbase-site.xml_.
+For the list of configurable properties, see <<hbase_default_configurations,hbase default configurations>> below or view the raw _hbase-default.xml_ source file in the HBase source code at _src/main/resources_.
+
+Not all configuration options make it out to _hbase-default.xml_.
+Configuration that it is thought rare anyone would change can exist only in code; the only way to turn up such configurations is via a reading of the source code itself.
+
+Currently, changes here will require a cluster restart for HBase to notice the change.
+// hbase/src/main/asciidoc
+//
+include::../../../../target/asciidoc/hbase-default.adoc[]
+
+
+[[hbase.env.sh]]
+=== _hbase-env.sh_
+
+Set HBase environment variables in this file.
+Examples include options to pass the JVM on start of an HBase daemon such as heap size and garbage collector configs.
+You can also set configurations for HBase configuration, log directories, niceness, ssh options, where to locate process pid files, etc.
+Open the file at _conf/hbase-env.sh_ and peruse its content.
+Each option is fairly well documented.
+Add your own environment variables here if you want them read by HBase daemons on startup.
+
+Changes here will require a cluster restart for HBase to notice the change.
+
+[[log4j]]
+=== _log4j.properties_
+
+Edit this file to change rate at which HBase files are rolled and to change the level at which HBase logs messages.
+
+Changes here will require a cluster restart for HBase to notice the change though log levels can be changed for particular daemons via the HBase UI.
+
+[[client_dependencies]]
+=== Client configuration and dependencies connecting to an HBase cluster
+
+If you are running HBase in standalone mode, you don't need to configure anything for your client to work provided that they are all on the same machine.
+
+Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for current critical locations.
+ZooKeeper is where all these values are kept.
+Thus clients require the location of the ZooKeeper ensemble before they can do anything else.
+Usually this the ensemble location is kept out in the _hbase-site.xml_ and is picked up by the client from the `CLASSPATH`.
+
+If you are configuring an IDE to run a HBase client, you should include the _conf/_ directory on your classpath so _hbase-site.xml_ settings can be found (or add _src/test/resources_ to pick up the hbase-site.xml used by tests).
+
+Minimally, a client of HBase needs several libraries in its `CLASSPATH` when connecting to a cluster, including:
+[source]
+----
+
+commons-configuration (commons-configuration-1.6.jar)
+commons-lang (commons-lang-2.5.jar)
+commons-logging (commons-logging-1.1.1.jar)
+hadoop-core (hadoop-core-1.0.0.jar)
+hbase (hbase-0.92.0.jar)
+log4j (log4j-1.2.16.jar)
+slf4j-api (slf4j-api-1.5.8.jar)
+slf4j-log4j (slf4j-log4j12-1.5.8.jar)
+zookeeper (zookeeper-3.4.2.jar)
+----
+
+An example basic _hbase-site.xml_ for client only might look as follows:
+[source,xml]
+----
+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<configuration>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>example1,example2,example3</value>
+ <description>The directory shared by region servers.
+ </description>
+ </property>
+</configuration>
+----
+
+[[java.client.config]]
+==== Java client configuration
+
+The configuration used by a Java client is kept in an link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HBaseConfiguration[HBaseConfiguration] instance.
+
+The factory method on HBaseConfiguration, `HBaseConfiguration.create();`, on invocation, will read in the content of the first _hbase-site.xml_ found on the client's `CLASSPATH`, if one is present (Invocation will also factor in any _hbase-default.xml_ found; an _hbase-default.xml_ ships inside the _hbase.X.X.X.jar_). It is also possible to specify configuration directly without having to read from a _hbase-site.xml_.
+For example, to set the ZooKeeper ensemble for the cluster programmatically do as follows:
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper locally
+----
+
+If multiple ZooKeeper instances make up your ZooKeeper ensemble, they may be specified in a comma-separated list (just as in the _hbase-site.xml_ file). This populated `Configuration` instance can then be passed to an link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table], and so on.
+
+[[example_config]]
+== Example Configurations
+
+=== Basic Distributed HBase Install
+
+Here is an example basic configuration for a distributed ten node cluster:
+* The nodes are named `example0`, `example1`, etc., through node `example9` in this example.
+* The HBase Master and the HDFS NameNode are running on the node `example0`.
+* RegionServers run on nodes `example1`-`example9`.
+* A 3-node ZooKeeper ensemble runs on `example1`, `example2`, and `example3` on the default ports.
+* ZooKeeper data is persisted to the directory _/export/zookeeper_.
+
+Below we show what the main configuration files -- _hbase-site.xml_, _regionservers_, and _hbase-env.sh_ -- found in the HBase _conf_ directory might look like.
+
+[[hbase_site]]
+==== _hbase-site.xml_
+
+[source,xml]
+----
+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<configuration>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>example1,example2,example3</value>
+ <description>The directory shared by RegionServers.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/export/zookeeper</value>
+ <description>Property from ZooKeeper config zoo.cfg.
+ The directory where the snapshot is stored.
+ </description>
+ </property>
+ <property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://example0:8020/hbase</value>
+ <description>The directory shared by RegionServers.
+ </description>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ <description>The mode the cluster will be in. Possible values are
+ false: standalone and pseudo-distributed setups with managed Zookeeper
+ true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
+ </description>
+ </property>
+</configuration>
+----
+
+[[regionservers]]
+==== _regionservers_
+
+In this file you list the nodes that will run RegionServers.
+In our case, these nodes are `example1`-`example9`.
+
+[source]
+----
+example1
+example2
+example3
+example4
+example5
+example6
+example7
+example8
+example9
+----
+
+[[hbase_env]]
+==== _hbase-env.sh_
+
+The following lines in the _hbase-env.sh_ file show how to set the `JAVA_HOME` environment variable (required for HBase 0.98.5 and newer) and set the heap to 4 GB (rather than the default value of 1 GB). If you copy and paste this example, be sure to adjust the `JAVA_HOME` to suit your environment.
+
+----
+# The java implementation to use.
+export JAVA_HOME=/usr/java/jdk1.7.0/
+
+# The maximum amount of heap to use. Default is left to JVM default.
+export HBASE_HEAPSIZE=4G
+----
+
+Use +rsync+ to copy the content of the _conf_ directory to all nodes of the cluster.
+
+[[important_configurations]]
+== The Important Configurations
+
+Below we list some _important_ configurations.
+We've divided this section into required configuration and worth-a-look recommended configs.
+
+[[required_configuration]]
+=== Required Configurations
+
+Review the <<os,os>> and <<hadoop,hadoop>> sections.
+
+[[big.cluster.config]]
+==== Big Cluster Configurations
+
+If you have a cluster with a lot of regions, it is possible that a Regionserver checks in briefly after the Master starts while all the remaining RegionServers lag behind. This first server to check in will be assigned all regions which is not optimal.
+To prevent the above scenario from happening, up the `hbase.master.wait.on.regionservers.mintostart` property from its default value of 1.
+See link:https://issues.apache.org/jira/browse/HBASE-6389[HBASE-6389 Modify the
+ conditions to ensure that Master waits for sufficient number of Region Servers before
+ starting region assignments] for more detail.
+
+[[backup.master.fail.fast]]
+==== If a backup Master exists, make the primary Master fail fast
+
+If the primary Master loses its connection with ZooKeeper, it will fall into a loop where it keeps trying to reconnect.
+Disable this functionality if you are running more than one Master: i.e. a backup Master.
+Failing to do so, the dying Master may continue to receive RPCs though another Master has assumed the role of primary.
+See the configuration <<fail.fast.expired.active.master,fail.fast.expired.active.master>>.
+
+=== Recommended Configurations
+
+[[recommended_configurations.zk]]
+==== ZooKeeper Configuration
+
+[[sect.zookeeper.session.timeout]]
+===== `zookeeper.session.timeout`
+
+The default timeout is three minutes (specified in milliseconds). This means that if a server crashes, it will be three minutes before the Master notices the crash and starts recovery.
+You might like to tune the timeout down to a minute or even less so the Master notices failures the sooner.
+Before changing this value, be sure you have your JVM garbage collection configuration under control otherwise, a long garbage collection that lasts beyond the ZooKeeper session timeout will take out your RegionServer (You might be fine with this -- you probably want recovery to start on the server if a RegionServer has been in GC for a long period of time).
+
+To change this configuration, edit _hbase-site.xml_, copy the changed file around the cluster and restart.
+
+We set this value high to save our having to field questions up on the mailing lists asking why a RegionServer went down during a massive import.
+The usual cause is that their JVM is untuned and they are running into long GC pauses.
+Our thinking is that while users are getting familiar with HBase, we'd save them having to know all of its intricacies.
+Later when they've built some confidence, then they can play with configuration such as this.
+
+[[zookeeper.instances]]
+===== Number of ZooKeeper Instances
+
+See <<zookeeper,zookeeper>>.
+
+[[recommended.configurations.hdfs]]
+==== HDFS Configurations
+
+[[dfs.datanode.failed.volumes.tolerated]]
+===== dfs.datanode.failed.volumes.tolerated
+
+This is the "...number of volumes that are allowed to fail before a DataNode stops offering service.
+By default any volume failure will cause a datanode to shutdown" from the _hdfs-default.xml_ description.
+You might want to set this to about half the amount of your available disks.
+
+[[hbase.regionserver.handler.count_description]]
+==== `hbase.regionserver.handler.count`
+
+This setting defines the number of threads that are kept open to answer incoming requests to user tables.
+The rule of thumb is to keep this number low when the payload per request approaches the MB (big puts, scans using a large cache) and high when the payload is small (gets, small puts, ICVs, deletes). The total size of the queries in progress is limited by the setting `hbase.ipc.server.max.callqueue.size`.
+
+It is safe to set that number to the maximum number of incoming clients if their payload is small, the typical example being a cluster that serves a website since puts aren't typically buffered and most of the operations are gets.
+
+The reason why it is dangerous to keep this setting high is that the aggregate size of all the puts that are currently happening in a region server may impose too much pressure on its memory, or even trigger an OutOfMemoryError.
+A RegionServer running on low memory will trigger its JVM's garbage collector to run more frequently up to a point where GC pauses become noticeable (the reason being that all the memory used to keep all the requests' payloads cannot be trashed, no matter how hard the garbage collector tries). After some time, the overall cluster throughput is affected since every request that hits that RegionServer will take longer, which exacerbates the problem even more.
+
+You can get a sense of whether you have too little or too many handlers by <<rpc.logging,rpc.logging>> on an individual RegionServer then tailing its logs (Queued requests consume memory).
+
+[[big_memory]]
+==== Configuration for large memory machines
+
+HBase ships with a reasonable, conservative configuration that will work on nearly all machine types that people might want to test with.
+If you have larger machines -- HBase has 8G and larger heap -- you might the following configuration options helpful.
+TODO.
+
+[[config.compression]]
+==== Compression
+
+You should consider enabling ColumnFamily compression.
+There are several options that are near-frictionless and in most all cases boost performance by reducing the size of StoreFiles and thus reducing I/O.
+
+See <<compression,compression>> for more information.
+
+[[config.wals]]
+==== Configuring the size and number of WAL files
+
+HBase uses <<wal,wal>> to recover the memstore data that has not been flushed to disk in case of an RS failure.
+These WAL files should be configured to be slightly smaller than HDFS block (by default a HDFS block is 64Mb and a WAL file is ~60Mb).
+
+HBase also has a limit on the number of WAL files, designed to ensure there's never too much data that needs to be replayed during recovery.
+This limit needs to be set according to memstore configuration, so that all the necessary data would fit.
+It is recommended to allocate enough WAL files to store at least that much data (when all memstores are close to full). For example, with 16Gb RS heap, default memstore settings (0.4), and default WAL file size (~60Mb), 16Gb*0.4/60, the starting point for WAL file count is ~109.
+However, as all memstores are not expected to be full all the time, less WAL files can be allocated.
+
+[[disable.splitting]]
+==== Managed Splitting
+
+HBase generally handles splitting your regions, based upon the settings in your _hbase-default.xml_ and _hbase-site.xml_ configuration files.
+Important settings include `hbase.regionserver.region.split.policy`, `hbase.hregion.max.filesize`, `hbase.regionserver.regionSplitLimit`.
+A simplistic view of splitting is that when a region grows to `hbase.hregion.max.filesize`, it is split.
+For most use patterns, most of the time, you should use automatic splitting.
+See <<manual_region_splitting_decisions,manual region splitting decisions>> for more information about manual region splitting.
+
+Instead of allowing HBase to split your regions automatically, you can choose to manage the splitting yourself.
+This feature was added in HBase 0.90.0.
+Manually managing splits works if you know your keyspace well, otherwise let HBase figure where to split for you.
+Manual splitting can mitigate region creation and movement under load.
+It also makes it so region boundaries are known and invariant (if you disable region splitting). If you use manual splits, it is easier doing staggered, time-based major compactions to spread out your network IO load.
+
+.Disable Automatic Splitting
+To disable automatic splitting, set `hbase.hregion.max.filesize` to a very large value, such as `100 GB` It is not recommended to set it to its absolute maximum value of `Long.MAX_VALUE`.
+
+.Automatic Splitting Is Recommended
+[NOTE]
+====
+If you disable automatic splits to diagnose a problem or during a period of fast data growth, it is recommended to re-enable them when your situation becomes more stable.
+The potential benefits of managing region splits yourself are not undisputed.
+====
+
+.Determine the Optimal Number of Pre-Split Regions
+The optimal number of pre-split regions depends on your application and environment.
+A good rule of thumb is to start with 10 pre-split regions per server and watch as data grows over time.
+It is better to err on the side of too few regions and perform rolling splits later.
+The optimal number of regions depends upon the largest StoreFile in your region.
+The size of the largest StoreFile will increase with time if the amount of data grows.
+The goal is for the largest region to be just large enough that the compaction selection algorithm only compacts it during a timed major compaction.
+Otherwise, the cluster can be prone to compaction storms where a large number of regions under compaction at the same time.
+It is important to understand that the data growth causes compaction storms, and not the manual split decision.
+
+If the regions are split into too many large regions, you can increase the major compaction interval by configuring `HConstants.MAJOR_COMPACTION_PERIOD`.
+HBase 0.90 introduced `org.apache.hadoop.hbase.util.RegionSplitter`, which provides a network-IO-safe rolling split of all regions.
+
+[[managed.compactions]]
+==== Managed Compactions
+
+By default, major compactions are scheduled to run once in a 7-day period.
+Prior to HBase 0.96.x, major compactions were scheduled to happen once per day by default.
+
+If you need to control exactly when and how often major compaction runs, you can disable managed major compactions.
+See the entry for `hbase.hregion.majorcompaction` in the <<compaction.parameters,compaction.parameters>> table for details.
+
+.Do Not Disable Major Compactions
+[WARNING]
+====
+Major compactions are absolutely necessary for StoreFile clean-up.
+Do not disable them altogether.
+You can run major compactions manually via the HBase shell or via the http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html#majorCompact(org.apache.hadoop.hbase.TableName)[Admin API].
+====
+
+For more information about compactions and the compaction file selection process, see <<compaction,compaction>>
+
+[[spec.ex]]
+==== Speculative Execution
+
+Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it is generally advised to turn off Speculative Execution at a system-level unless you need it for a specific case, where it can be configured per-job.
+Set the properties `mapreduce.map.speculative` and `mapreduce.reduce.speculative` to false.
+
+[[other_configuration]]
+=== Other Configurations
+
+[[balancer_config]]
+==== Balancer
+
+The balancer is a periodic operation which is run on the master to redistribute regions on the cluster.
+It is configured via `hbase.balancer.period` and defaults to 300000 (5 minutes).
+
+See <<master.processes.loadbalancer,master.processes.loadbalancer>> for more information on the LoadBalancer.
+
+[[disabling.blockcache]]
+==== Disabling Blockcache
+
+Do not turn off block cache (You'd do it by setting `hbase.block.cache.size` to zero). Currently we do not do well if you do this because the RegionServer will spend all its time loading HFile indices over and over again.
+If your working set it such that block cache does you no good, at least size the block cache such that HFile indices will stay up in the cache (you can get a rough idea on the size you need by surveying RegionServer UIs; you'll see index block size accounted near the top of the webpage).
+
+[[nagles]]
+==== link:http://en.wikipedia.org/wiki/Nagle's_algorithm[Nagle's] or the small package problem
+
+If a big 40ms or so occasional delay is seen in operations against HBase, try the Nagles' setting.
+For example, see the user mailing list thread, link:http://search-hadoop.com/m/pduLg2fydtE/Inconsistent+scan+performance+with+caching+set+&subj=Re+Inconsistent+scan+performance+with+caching+set+to+1[Inconsistent scan performance with caching set to 1] and the issue cited therein where setting `notcpdelay` improved scan speeds.
+You might also see the graphs on the tail of link:https://issues.apache.org/jira/browse/HBASE-7008[HBASE-7008 Set scanner caching to a better default] where our Lars Hofhansl tries various data sizes w/ Nagle's on and off measuring the effect.
+
+[[mttr]]
+==== Better Mean Time to Recover (MTTR)
+
+This section is about configurations that will make servers come back faster after a fail.
+See the Deveraj Das an Nicolas Liochon blog post link:http://hortonworks.com/blog/introduction-to-hbase-mean-time-to-recover-mttr/[Introduction to HBase Mean Time to Recover (MTTR)] for a brief introduction.
+
+The issue link:https://issues.apache.org/jira/browse/HBASE-8389[HBASE-8354 forces Namenode into loop with lease recovery requests] is messy but has a bunch of good discussion toward the end on low timeouts and how to effect faster recovery including citation of fixes added to HDFS. Read the Varun Sharma comments.
+The below suggested configurations are Varun's suggestions distilled and tested.
+Make sure you are running on a late-version HDFS so you have the fixes he refers too and himself adds to HDFS that help HBase MTTR (e.g.
+HDFS-3703, HDFS-3712, and HDFS-4791 -- Hadoop 2 for sure has them and late Hadoop 1 has some). Set the following in the RegionServer.
+
+[source,xml]
+----
+<property>
+ <name>hbase.lease.recovery.dfs.timeout</name>
+ <value>23000</value>
+ <description>How much time we allow elapse between calls to recover lease.
+ Should be larger than the dfs timeout.</description>
+</property>
+<property>
+ <name>dfs.client.socket-timeout</name>
+ <value>10000</value>
+ <description>Down the DFS timeout from 60 to 10 seconds.</description>
+</property>
+----
+
+And on the NameNode/DataNode side, set the following to enable 'staleness' introduced in HDFS-3703, HDFS-3912.
+
+[source,xml]
+----
+<property>
+ <name>dfs.client.socket-timeout</name>
+ <value>10000</value>
+ <description>Down the DFS timeout from 60 to 10 seconds.</description>
+</property>
+<property>
+ <name>dfs.datanode.socket.write.timeout</name>
+ <value>10000</value>
+ <description>Down the DFS timeout from 8 * 60 to 10 seconds.</description>
+</property>
+<property>
+ <name>ipc.client.connect.timeout</name>
+ <value>3000</value>
+ <description>Down from 60 seconds to 3.</description>
+</property>
+<property>
+ <name>ipc.client.connect.max.retries.on.timeouts</name>
+ <value>2</value>
+ <description>Down from 45 seconds to 3 (2 == 3 retries).</description>
+</property>
+<property>
+ <name>dfs.namenode.avoid.read.stale.datanode</name>
+ <value>true</value>
+ <description>Enable stale state in hdfs</description>
+</property>
+<property>
+ <name>dfs.namenode.stale.datanode.interval</name>
+ <value>20000</value>
+ <description>Down from default 30 seconds</description>
+</property>
+<property>
+ <name>dfs.namenode.avoid.write.stale.datanode</name>
+ <value>true</value>
+ <description>Enable stale state in hdfs</description>
+</property>
+----
+
+[[jmx_config]]
+==== JMX
+
+JMX (Java Management Extensions) provides built-in instrumentation that enables you to monitor and manage the Java VM.
+To enable monitoring and management from remote systems, you need to set system property `com.sun.management.jmxremote.port` (the port number through which you want to enable JMX RMI connections) when you start the Java VM.
+See the link:http://docs.oracle.com/javase/6/docs/technotes/guides/management/agent.html[official documentation] for more information.
+Historically, besides above port mentioned, JMX opens two additional random TCP listening ports, which could lead to port conflict problem. (See link:https://issues.apache.org/jira/browse/HBASE-10289[HBASE-10289] for details)
+
+As an alternative, You can use the coprocessor-based JMX implementation provided by HBase.
+To enable it in 0.99 or above, add below property in _hbase-site.xml_:
+
+[source,xml]
+----
+<property>
+ <name>hbase.coprocessor.regionserver.classes</name>
+ <value>org.apache.hadoop.hbase.JMXListener</value>
+</property>
+----
+
+NOTE: DO NOT set `com.sun.management.jmxremote.port` for Java VM at the same time.
+
+Currently it supports Master and RegionServer Java VM.
+By default, the JMX listens on TCP port 10102, you can further configure the port using below properties:
+
+[source,xml]
+----
+<property>
+ <name>regionserver.rmi.registry.port</name>
+ <value>61130</value>
+</property>
+<property>
+ <name>regionserver.rmi.connector.port</name>
+ <value>61140</value>
+</property>
+----
+
+The registry port can be shared with connector port in most cases, so you only need to configure regionserver.rmi.registry.port.
+However if you want to use SSL communication, the 2 ports must be configured to different values.
+
+By default the password authentication and SSL communication is disabled.
+To enable password authentication, you need to update _hbase-env.sh_ like below:
+[source,bash]
+----
+export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.authenticate=true \
+ -Dcom.sun.management.jmxremote.password.file=your_password_file \
+ -Dcom.sun.management.jmxremote.access.file=your_access_file"
+
+export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
+export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "
+----
+
+See example password/access file under _$JRE_HOME/lib/management_.
+
+To enable SSL communication with password authentication, follow below steps:
+
+[source,bash]
+----
+#1. generate a key pair, stored in myKeyStore
+keytool -genkey -alias jconsole -keystore myKeyStore
+
+#2. export it to file jconsole.cert
+keytool -export -alias jconsole -keystore myKeyStore -file jconsole.cert
+
+#3. copy jconsole.cert to jconsole client machine, import it to jconsoleKeyStore
+keytool -import -alias jconsole -keystore jconsoleKeyStore -file jconsole.cert
+----
+
+And then update _hbase-env.sh_ like below:
+
+[source,bash]
+----
+export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=true \
+ -Djavax.net.ssl.keyStore=/home/tianq/myKeyStore \
+ -Djavax.net.ssl.keyStorePassword=your_password_in_step_1 \
+ -Dcom.sun.management.jmxremote.authenticate=true \
+ -Dcom.sun.management.jmxremote.password.file=your_password file \
+ -Dcom.sun.management.jmxremote.access.file=your_access_file"
+
+export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
+export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "
+----
+
+Finally start `jconsole` on the client using the key store:
+
+[source,bash]
+----
+jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore
+----
+
+NOTE: To enable the HBase JMX implementation on Master, you also need to add below property in _hbase-site.xml_:
+
+[source,xml]
+----
+<property>
+ <ame>hbase.coprocessor.master.classes</name>
+ <value>org.apache.hadoop.hbase.JMXListener</value>
+</property>
+----
+
+The corresponding properties for port configuration are `master.rmi.registry.port` (by default 10101) and `master.rmi.connector.port` (by default the same as registry.port)
+
+[[dyn_config]]
+== Dynamic Configuration
+
+Since HBase 1.0.0, it is possible to change a subset of the configuration without requiring a server restart.
+In the HBase shell, there are new operators, `update_config` and `update_all_config` that will prompt a server or all servers to reload configuration.
+
+Only a subset of all configurations can currently be changed in the running server.
+Here is an incomplete list: `hbase.regionserver.thread.compaction.large`, `hbase.regionserver.thread.compaction.small`, `hbase.regionserver.thread.split`, `hbase.regionserver.thread.merge`, as well as compaction policy and configurations and adjustment to offpeak hours.
+For the full list consult the patch attached to link:https://issues.apache.org/jira/browse/HBASE-12147[HBASE-12147 Porting Online Config Change from 89-fb].
+
+ifdef::backend-docbook[]
+[index]
+== Index
+// Generated automatically by the DocBook toolchain.
+endif::backend-docbook[]
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/cp.adoc
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diff --git a/src/main/asciidoc/_chapters/cp.adoc b/src/main/asciidoc/_chapters/cp.adoc
new file mode 100644
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--- /dev/null
+++ b/src/main/asciidoc/_chapters/cp.adoc
@@ -0,0 +1,230 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[cp]]
+= Apache HBase Coprocessors
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+HBase coprocessors are modeled after the coprocessors which are part of Google's BigTable (http://www.scribd.com/doc/21631448/Dean-Keynote-Ladis2009, pages 66-67.). Coprocessors function in a similar way to Linux kernel modules.
+They provide a way to run server-level code against locally-stored data.
+The functionality they provide is very powerful, but also carries great risk and can have adverse effects on the system, at the level of the operating system.
+The information in this chapter is primarily sourced and heavily reused from Mingjie Lai's blog post at https://blogs.apache.org/hbase/entry/coprocessor_introduction.
+
+Coprocessors are not designed to be used by end users of HBase, but by HBase developers who need to add specialized functionality to HBase.
+One example of the use of coprocessors is pluggable compaction and scan policies, which are provided as coprocessors in link:https://issues.apache.org/jira/browse/HBASE-6427[HBASE-6427].
+
+== Coprocessor Framework
+
+The implementation of HBase coprocessors diverges from the BigTable implementation.
+The HBase framework provides a library and runtime environment for executing user code within the HBase region server and master processes.
+
+The framework API is provided in the link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/coprocessor/package-summary.html[coprocessor] package.
+
+Two different types of coprocessors are provided by the framework, based on their scope.
+
+.Types of Coprocessors
+
+System Coprocessors::
+ System coprocessors are loaded globally on all tables and regions hosted by a region server.
+
+Table Coprocessors::
+ You can specify which coprocessors should be loaded on all regions for a table on a per-table basis.
+
+The framework provides two different aspects of extensions as well: _observers_ and _endpoints_.
+
+Observers::
+ Observers are analogous to triggers in conventional databases.
+ They allow you to insert user code by overriding upcall methods provided by the coprocessor framework.
+ Callback functions are executed from core HBase code when events occur.
+ Callbacks are handled by the framework, and the coprocessor itself only needs to insert the extended or alternate functionality.
+
+Endpoints (HBase 0.96.x and later)::
+ The implementation for endpoints changed significantly in HBase 0.96.x due to the introduction of protocol buffers (protobufs) (link:https://issues.apache.org/jira/browse/HBASE-5448[HBASE-5488]). If you created endpoints before 0.96.x, you will need to rewrite them.
+ Endpoints are now defined and callable as protobuf services, rather than endpoint invocations passed through as Writable blobs
+
+Endpoints (HBase 0.94.x and earlier)::
+ Dynamic RPC endpoints resemble stored procedures.
+ An endpoint can be invoked at any time from the client.
+ When it is invoked, it is executed remotely at the target region or regions, and results of the executions are returned to the client.
+
+== Examples
+
+An example of an observer is included in _hbase-examples/src/test/java/org/apache/hadoop/hbase/coprocessor/example/TestZooKeeperScanPolicyObserver.java_.
+Several endpoint examples are included in the same directory.
+
+== Building A Coprocessor
+
+Before you can build a processor, it must be developed, compiled, and packaged in a JAR file.
+The next step is to configure the coprocessor framework to use your coprocessor.
+You can load the coprocessor from your HBase configuration, so that the coprocessor starts with HBase, or you can configure the coprocessor from the HBase shell, as a table attribute, so that it is loaded dynamically when the table is opened or reopened.
+
+=== Load from Configuration
+
+To configure a coprocessor to be loaded when HBase starts, modify the RegionServer's _hbase-site.xml_ and configure one of the following properties, based on the type of observer you are configuring:
+
+* `hbase.coprocessor.region.classes`for RegionObservers and Endpoints
+* `hbase.coprocessor.wal.classes`for WALObservers
+* `hbase.coprocessor.master.classes`for MasterObservers
+
+.Example RegionObserver Configuration
+====
+In this example, one RegionObserver is configured for all the HBase tables.
+
+[source,xml]
+----
+<property>
+ <name>hbase.coprocessor.region.classes</name>
+ <value>org.apache.hadoop.hbase.coprocessor.AggregateImplementation</value>
+</property>
+----
+====
+
+If multiple classes are specified for loading, the class names must be comma-separated.
+The framework attempts to load all the configured classes using the default class loader.
+Therefore, the jar file must reside on the server-side HBase classpath.
+
+Coprocessors which are loaded in this way will be active on all regions of all tables.
+These are the system coprocessor introduced earlier.
+The first listed coprocessors will be assigned the priority `Coprocessor.Priority.SYSTEM`.
+Each subsequent coprocessor in the list will have its priority value incremented by one (which reduces its priority, because priorities have the natural sort order of Integers).
+
+When calling out to registered observers, the framework executes their callbacks methods in the sorted order of their priority.
+Ties are broken arbitrarily.
+
+=== Load from the HBase Shell
+
+You can load a coprocessor on a specific table via a table attribute.
+The following example will load the `FooRegionObserver` observer when table `t1` is read or re-read.
+
+.Load a Coprocessor On a Table Using HBase Shell
+====
+----
+hbase(main):005:0> alter 't1', METHOD => 'table_att',
+ 'coprocessor'=>'hdfs:///foo.jar|com.foo.FooRegionObserver|1001|arg1=1,arg2=2'
+Updating all regions with the new schema...
+1/1 regions updated.
+Done.
+0 row(s) in 1.0730 seconds
+
+hbase(main):006:0> describe 't1'
+DESCRIPTION ENABLED
+ {NAME => 't1', coprocessor$1 => 'hdfs:///foo.jar|com.foo.FooRegio false
+ nObserver|1001|arg1=1,arg2=2', FAMILIES => [{NAME => 'c1', DATA_B
+ LOCK_ENCODING => 'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE
+ => '0', VERSIONS => '3', COMPRESSION => 'NONE', MIN_VERSIONS =>
+ '0', TTL => '2147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZ
+ E => '65536', IN_MEMORY => 'false', ENCODE_ON_DISK => 'true', BLO
+ CKCACHE => 'true'}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE',
+ BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3'
+ , COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2147483647'
+ , KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY
+ => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true'}]}
+1 row(s) in 0.0190 seconds
+----
+====
+
+The coprocessor framework will try to read the class information from the coprocessor table attribute value.
+The value contains four pieces of information which are separated by the `|` character.
+
+* File path: The jar file containing the coprocessor implementation must be in a location where all region servers can read it.
+ You could copy the file onto the local disk on each region server, but it is recommended to store it in HDFS.
+* Class name: The full class name of the coprocessor.
+* Priority: An integer.
+ The framework will determine the execution sequence of all configured observers registered at the same hook using priorities.
+ This field can be left blank.
+ In that case the framework will assign a default priority value.
+* Arguments: This field is passed to the coprocessor implementation.
+
+.Unload a Coprocessor From a Table Using HBase Shell
+====
+----
+
+hbase(main):007:0> alter 't1', METHOD => 'table_att_unset',
+hbase(main):008:0* NAME => 'coprocessor$1'
+Updating all regions with the new schema...
+1/1 regions updated.
+Done.
+0 row(s) in 1.1130 seconds
+
+hbase(main):009:0> describe 't1'
+DESCRIPTION ENABLED
+ {NAME => 't1', FAMILIES => [{NAME => 'c1', DATA_BLOCK_ENCODING => false
+ 'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSION
+ S => '3', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '214
+ 7483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN
+ _MEMORY => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true
+ '}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER =>
+ 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3', COMPRESSION =>
+ 'NONE', MIN_VERSIONS => '0', TTL => '2147483647', KEEP_DELETED_C
+ ELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false', ENCO
+ DE_ON_DISK => 'true', BLOCKCACHE => 'true'}]}
+1 row(s) in 0.0180 seconds
+----
+====
+
+WARNING: There is no guarantee that the framework will load a given coprocessor successfully.
+For example, the shell command neither guarantees a jar file exists at a particular location nor verifies whether the given class is actually contained in the jar file.
+
+== Check the Status of a Coprocessor
+
+To check the status of a coprocessor after it has been configured, use the `status` HBase Shell command.
+
+----
+
+hbase(main):020:0> status 'detailed'
+version 0.92-tm-6
+0 regionsInTransition
+master coprocessors: []
+1 live servers
+ localhost:52761 1328082515520
+ requestsPerSecond=3, numberOfOnlineRegions=3, usedHeapMB=32, maxHeapMB=995
+ -ROOT-,,0
+ numberOfStores=1, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0,
+storefileIndexSizeMB=0, readRequestsCount=54, writeRequestsCount=1, rootIndexSizeKB=0, totalStaticIndexSizeKB=0,
+totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[]
+ .META.,,1
+ numberOfStores=1, numberOfStorefiles=0, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0,
+storefileIndexSizeMB=0, readRequestsCount=97, writeRequestsCount=4, rootIndexSizeKB=0, totalStaticIndexSizeKB=0,
+totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[]
+ t1,,1328082575190.c0491168a27620ffe653ec6c04c9b4d1.
+ numberOfStores=2, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0,
+storefileIndexSizeMB=0, readRequestsCount=0, writeRequestsCount=0, rootIndexSizeKB=0, totalStaticIndexSizeKB=0,
+totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN,
+coprocessors=[AggregateImplementation]
+0 dead servers
+----
+
+== Monitor Time Spent in Coprocessors
+
+HBase 0.98.5 introduced the ability to monitor some statistics relating to the amount of time spent executing a given coprocessor.
+You can see these statistics via the HBase Metrics framework (see <<hbase_metrics>> or the Web UI for a given Region Server, via the _Coprocessor Metrics_ tab.
+These statistics are valuable for debugging and benchmarking the performance impact of a given coprocessor on your cluster.
+Tracked statistics include min, max, average, and 90th, 95th, and 99th percentile.
+All times are shown in milliseconds.
+The statistics are calculated over coprocessor execution samples recorded during the reporting interval, which is 10 seconds by default.
+The metrics sampling rate as described in <<hbase_metrics>>.
+
+.Coprocessor Metrics UI
+image::coprocessor_stats.png[]
[03/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/unit_testing.adoc
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diff --git a/src/main/asciidoc/_chapters/unit_testing.adoc b/src/main/asciidoc/_chapters/unit_testing.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[unit.tests]]
+= Unit Testing HBase Applications
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter discusses unit testing your HBase application using JUnit, Mockito, MRUnit, and HBaseTestingUtility.
+Much of the information comes from link:http://blog.cloudera.com/blog/2013/09/how-to-test-hbase-applications-using-popular-tools/[a community blog post about testing HBase applications].
+For information on unit tests for HBase itself, see <<hbase.tests,hbase.tests>>.
+
+== JUnit
+
+HBase uses link:http://junit.org[JUnit] 4 for unit tests
+
+This example will add unit tests to the following example class:
+
+[source,java]
+----
+
+public class MyHBaseDAO {
+
+ public static void insertRecord(Table.getTable(table), HBaseTestObj obj)
+ throws Exception {
+ Put put = createPut(obj);
+ table.put(put);
+ }
+
+ private static Put createPut(HBaseTestObj obj) {
+ Put put = new Put(Bytes.toBytes(obj.getRowKey()));
+ put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1"),
+ Bytes.toBytes(obj.getData1()));
+ put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2"),
+ Bytes.toBytes(obj.getData2()));
+ return put;
+ }
+}
+----
+
+The first step is to add JUnit dependencies to your Maven POM file:
+
+[source,xml]
+----
+
+<dependency>
+ <groupId>junit</groupId>
+ <artifactId>junit</artifactId>
+ <version>4.11</version>
+ <scope>test</scope>
+</dependency>
+----
+
+Next, add some unit tests to your code.
+Tests are annotated with `@Test`.
+Here, the unit tests are in bold.
+
+[source,java]
+----
+
+public class TestMyHbaseDAOData {
+ @Test
+ public void testCreatePut() throws Exception {
+ HBaseTestObj obj = new HBaseTestObj();
+ obj.setRowKey("ROWKEY-1");
+ obj.setData1("DATA-1");
+ obj.setData2("DATA-2");
+ Put put = MyHBaseDAO.createPut(obj);
+ assertEquals(obj.getRowKey(), Bytes.toString(put.getRow()));
+ assertEquals(obj.getData1(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")).get(0).getValue()));
+ assertEquals(obj.getData2(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")).get(0).getValue()));
+ }
+}
+----
+
+These tests ensure that your `createPut` method creates, populates, and returns a `Put` object with expected values.
+Of course, JUnit can do much more than this.
+For an introduction to JUnit, see link:https://github.com/junit-team/junit/wiki/Getting-started.
+
+== Mockito
+
+Mockito is a mocking framework.
+It goes further than JUnit by allowing you to test the interactions between objects without having to replicate the entire environment.
+You can read more about Mockito at its project site, link:https://code.google.com/p/mockito/.
+
+You can use Mockito to do unit testing on smaller units.
+For instance, you can mock a `org.apache.hadoop.hbase.Server` instance or a `org.apache.hadoop.hbase.master.MasterServices` interface reference rather than a full-blown `org.apache.hadoop.hbase.master.HMaster`.
+
+This example builds upon the example code in <<unit.tests,unit.tests>>, to test the `insertRecord` method.
+
+First, add a dependency for Mockito to your Maven POM file.
+
+[source,xml]
+----
+
+<dependency>
+ <groupId>org.mockito</groupId>
+ <artifactId>mockito-all</artifactId>
+ <version>1.9.5</version>
+ <scope>test</scope>
+</dependency>
+----
+
+Next, add a `@RunWith` annotation to your test class, to direct it to use Mockito.
+
+[source,java]
+----
+
+@RunWith(MockitoJUnitRunner.class)
+public class TestMyHBaseDAO{
+ @Mock
+ Configuration config = HBaseConfiguration.create();
+ @Mock
+ Connection connection = ConnectionFactory.createConnection(config);
+ @Mock
+ private Table table;
+ @Captor
+ private ArgumentCaptor putCaptor;
+
+ @Test
+ public void testInsertRecord() throws Exception {
+ //return mock table when getTable is called
+ when(connection.getTable(TableName.valueOf("tablename")).thenReturn(table);
+ //create test object and make a call to the DAO that needs testing
+ HBaseTestObj obj = new HBaseTestObj();
+ obj.setRowKey("ROWKEY-1");
+ obj.setData1("DATA-1");
+ obj.setData2("DATA-2");
+ MyHBaseDAO.insertRecord(table, obj);
+ verify(table).put(putCaptor.capture());
+ Put put = putCaptor.getValue();
+
+ assertEquals(Bytes.toString(put.getRow()), obj.getRowKey());
+ assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")));
+ assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")));
+ assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-1")).get(0).getValue()), "DATA-1");
+ assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-2")).get(0).getValue()), "DATA-2");
+ }
+}
+----
+
+This code populates `HBaseTestObj` with ``ROWKEY-1'', ``DATA-1'', ``DATA-2'' as values.
+It then inserts the record into the mocked table.
+The Put that the DAO would have inserted is captured, and values are tested to verify that they are what you expected them to be.
+
+The key here is to manage Connection and Table instance creation outside the DAO.
+This allows you to mock them cleanly and test Puts as shown above.
+Similarly, you can now expand into other operations such as Get, Scan, or Delete.
+
+== MRUnit
+
+link:http://mrunit.apache.org/[Apache MRUnit] is a library that allows you to unit-test MapReduce jobs.
+You can use it to test HBase jobs in the same way as other MapReduce jobs.
+
+Given a MapReduce job that writes to an HBase table called `MyTest`, which has one column family called `CF`, the reducer of such a job could look like the following:
+
+[source,java]
+----
+
+public class MyReducer extends TableReducer<Text, Text, ImmutableBytesWritable> {
+ public static final byte[] CF = "CF".getBytes();
+ public static final byte[] QUALIFIER = "CQ-1".getBytes();
+ public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
+ //bunch of processing to extract data to be inserted, in our case, lets say we are simply
+ //appending all the records we receive from the mapper for this particular
+ //key and insert one record into HBase
+ StringBuffer data = new StringBuffer();
+ Put put = new Put(Bytes.toBytes(key.toString()));
+ for (Text val : values) {
+ data = data.append(val);
+ }
+ put.add(CF, QUALIFIER, Bytes.toBytes(data.toString()));
+ //write to HBase
+ context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())), put);
+ }
+ }
+----
+
+To test this code, the first step is to add a dependency to MRUnit to your Maven POM file.
+
+[source,xml]
+----
+
+<dependency>
+ <groupId>org.apache.mrunit</groupId>
+ <artifactId>mrunit</artifactId>
+ <version>1.0.0 </version>
+ <scope>test</scope>
+</dependency>
+----
+
+Next, use the ReducerDriver provided by MRUnit, in your Reducer job.
+
+[source,java]
+----
+
+public class MyReducerTest {
+ ReduceDriver<Text, Text, ImmutableBytesWritable, Writable> reduceDriver;
+ byte[] CF = "CF".getBytes();
+ byte[] QUALIFIER = "CQ-1".getBytes();
+
+ @Before
+ public void setUp() {
+ MyReducer reducer = new MyReducer();
+ reduceDriver = ReduceDriver.newReduceDriver(reducer);
+ }
+
+ @Test
+ public void testHBaseInsert() throws IOException {
+ String strKey = "RowKey-1", strValue = "DATA", strValue1 = "DATA1",
+strValue2 = "DATA2";
+ List<Text> list = new ArrayList<Text>();
+ list.add(new Text(strValue));
+ list.add(new Text(strValue1));
+ list.add(new Text(strValue2));
+ //since in our case all that the reducer is doing is appending the records that the mapper
+ //sends it, we should get the following back
+ String expectedOutput = strValue + strValue1 + strValue2;
+ //Setup Input, mimic what mapper would have passed
+ //to the reducer and run test
+ reduceDriver.withInput(new Text(strKey), list);
+ //run the reducer and get its output
+ List<Pair<ImmutableBytesWritable, Writable>> result = reduceDriver.run();
+
+ //extract key from result and verify
+ assertEquals(Bytes.toString(result.get(0).getFirst().get()), strKey);
+
+ //extract value for CF/QUALIFIER and verify
+ Put a = (Put)result.get(0).getSecond();
+ String c = Bytes.toString(a.get(CF, QUALIFIER).get(0).getValue());
+ assertEquals(expectedOutput,c );
+ }
+
+}
+----
+
+Your MRUnit test verifies that the output is as expected, the Put that is inserted into HBase has the correct value, and the ColumnFamily and ColumnQualifier have the correct values.
+
+MRUnit includes a MapperDriver to test mapping jobs, and you can use MRUnit to test other operations, including reading from HBase, processing data, or writing to HDFS,
+
+== Integration Testing with a HBase Mini-Cluster
+
+HBase ships with HBaseTestingUtility, which makes it easy to write integration tests using a [firstterm]_mini-cluster_.
+The first step is to add some dependencies to your Maven POM file.
+Check the versions to be sure they are appropriate.
+
+[source,xml]
+----
+
+<dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-common</artifactId>
+ <version>2.0.0</version>
+ <type>test-jar</type>
+ <scope>test</scope>
+</dependency>
+
+<dependency>
+ <groupId>org.apache.hbase</groupId>
+ <artifactId>hbase</artifactId>
+ <version>0.98.3</version>
+ <type>test-jar</type>
+ <scope>test</scope>
+</dependency>
+
+<dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-hdfs</artifactId>
+ <version>2.0.0</version>
+ <type>test-jar</type>
+ <scope>test</scope>
+</dependency>
+
+<dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-hdfs</artifactId>
+ <version>2.0.0</version>
+ <scope>test</scope>
+</dependency>
+----
+
+This code represents an integration test for the MyDAO insert shown in <<unit.tests,unit.tests>>.
+
+[source,java]
+----
+
+public class MyHBaseIntegrationTest {
+ private static HBaseTestingUtility utility;
+ byte[] CF = "CF".getBytes();
+ byte[] QUALIFIER = "CQ-1".getBytes();
+
+ @Before
+ public void setup() throws Exception {
+ utility = new HBaseTestingUtility();
+ utility.startMiniCluster();
+ }
+
+ @Test
+ public void testInsert() throws Exception {
+ HTableInterface table = utility.createTable(Bytes.toBytes("MyTest"),
+ Bytes.toBytes("CF"));
+ HBaseTestObj obj = new HBaseTestObj();
+ obj.setRowKey("ROWKEY-1");
+ obj.setData1("DATA-1");
+ obj.setData2("DATA-2");
+ MyHBaseDAO.insertRecord(table, obj);
+ Get get1 = new Get(Bytes.toBytes(obj.getRowKey()));
+ get1.addColumn(CF, CQ1);
+ Result result1 = table.get(get1);
+ assertEquals(Bytes.toString(result1.getRow()), obj.getRowKey());
+ assertEquals(Bytes.toString(result1.value()), obj.getData1());
+ Get get2 = new Get(Bytes.toBytes(obj.getRowKey()));
+ get2.addColumn(CF, CQ2);
+ Result result2 = table.get(get2);
+ assertEquals(Bytes.toString(result2.getRow()), obj.getRowKey());
+ assertEquals(Bytes.toString(result2.value()), obj.getData2());
+ }
+}
+----
+
+This code creates an HBase mini-cluster and starts it.
+Next, it creates a table called `MyTest` with one column family, `CF`.
+A record is inserted, a Get is performed from the same table, and the insertion is verified.
+
+NOTE: Starting the mini-cluster takes about 20-30 seconds, but that should be appropriate for integration testing.
+
+To use an HBase mini-cluster on Microsoft Windows, you need to use a Cygwin environment.
+
+See the paper at link:http://blog.sematext.com/2010/08/30/hbase-case-study-using-hbasetestingutility-for-local-testing-development/[HBase Case-Study: Using HBaseTestingUtility for Local Testing and
+ Development] (2010) for more information about HBaseTestingUtility.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/upgrading.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/upgrading.adoc b/src/main/asciidoc/_chapters/upgrading.adoc
new file mode 100644
index 0000000..6b63833
--- /dev/null
+++ b/src/main/asciidoc/_chapters/upgrading.adoc
@@ -0,0 +1,452 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[upgrading]]
+= Upgrading
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+You cannot skip major versions when upgrading. If you are upgrading from version 0.90.x to 0.94.x, you must first go from 0.90.x to 0.92.x and then go from 0.92.x to 0.94.x.
+
+NOTE: It may be possible to skip across versions -- for example go from 0.92.2 straight to 0.98.0 just following the 0.96.x upgrade instructions -- but these scenarios are untested.
+
+Review <<configuration>>, in particular <<hadoop>>.
+
+[[hbase.versioning]]
+== HBase version number and compatibility
+
+HBase has two versioning schemes, pre-1.0 and post-1.0. Both are detailed below.
+
+[[hbase.versioning.post10]]
+=== Post 1.0 versions
+
+Starting with the 1.0.0 release, HBase is working towards link:http://semver.org/[Semantic Versioning] for its release versioning. In summary:
+
+.Given a version number MAJOR.MINOR.PATCH, increment the:
+* MAJOR version when you make incompatible API changes,
+* MINOR version when you add functionality in a backwards-compatible manner, and
+* PATCH version when you make backwards-compatible bug fixes.
+* Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.
+
+[[hbase.versioning.compat]]
+.Compatibility Dimensions
+In addition to the usual API versioning considerations HBase has other compatibility dimensions that we need to consider.
+
+.Client-Server wire protocol compatibility
+* Allows updating client and server out of sync.
+* We could only allow upgrading the server first. I.e. the server would be backward compatible to an old client, that way new APIs are OK.
+* Example: A user should be able to use an old client to connect to an upgraded cluster.
+
+.Server-Server protocol compatibility
+* Servers of different versions can co-exist in the same cluster.
+* The wire protocol between servers is compatible.
+* Workers for distributed tasks, such as replication and log splitting, can co-exist in the same cluster.
+* Dependent protocols (such as using ZK for coordination) will also not be changed.
+* Example: A user can perform a rolling upgrade.
+
+.File format compatibility
+* Support file formats backward and forward compatible
+* Example: File, ZK encoding, directory layout is upgraded automatically as part of an HBase upgrade. User can rollback to the older version and everything will continue to work.
+
+.Client API compatibility
+* Allow changing or removing existing client APIs.
+* An API needs to deprecated for a major version before we will change/remove it.
+* APIs available in a patch version will be available in all later patch versions. However, new APIs may be added which will not be available in earlier patch versions.
+* Example: A user using a newly deprecated api does not need to modify application code with hbase api calls until the next major version.
+
+.Client Binary compatibility
+* Client code written to APIs available in a given patch release can run unchanged (no recompilation needed) against the new jars of later patch versions.
+* Client code written to APIs available in a given patch release might not run against the old jars from an earlier patch version.
+* Example: Old compiled client code will work unchanged with the new jars.
+
+.Server-Side Limited API compatibility (taken from Hadoop)
+* Internal APIs are marked as Stable, Evolving, or Unstable
+* This implies binary compatibility for coprocessors and plugins (pluggable classes, including replication) as long as these are only using marked interfaces/classes.
+* Example: Old compiled Coprocessor, Filter, or Plugin code will work unchanged with the new jars.
+
+.Dependency Compatibility
+* An upgrade of HBase will not require an incompatible upgrade of a dependent project, including the Java runtime.
+* Example: An upgrade of Hadoop will not invalidate any of the compatibilities guarantees we made.
+
+.Operational Compatibility
+* Metric changes
+* Behavioral changes of services
+* Web page APIs
+
+.Summary
+* A patch upgrade is a drop-in replacement. Any change that is not Java binary compatible would not be allowed.footnote:[See http://docs.oracle.com/javase/specs/jls/se7/html/jls-13.html.]. Downgrading versions within patch releases may not be compatible.
+
+* A minor upgrade requires no application/client code modification. Ideally it would be a drop-in replacement but client code, coprocessors, filters, etc might have to be recompiled if new jars are used.
+
+* A major upgrade allows the HBase community to make breaking changes.
+
+.Compatibility Matrix footnote:[Note that this indicates what could break, not that it will break. We will/should add specifics in our release notes.]
+[cols="1,1,1,1"]
+|===
+| | Major | Minor | Patch
+|Client-Server wire Compatibility| N |Y |Y
+|Server-Server Compatibility |N |Y |Y
+|File Format Compatibility | N footnote:[comp_matrix_offline_upgrade_note,Running an offline upgrade tool without rollback might be needed. We will typically only support migrating data from major version X to major version X+1.] | Y |Y
+|Client API Compatibility | N | Y |Y
+|Client Binary Compatibility | N | N |Y
+4+|Server-Side Limited API Compatibility
+>| Stable | N | Y | Y
+>| Evolving | N |N |Y
+>| Unstable | N |N |N
+|Dependency Compatibility | N |Y |Y
+|Operational Compatibility | N |N |Y
+|===
+
+[[hbase.client.api.surface]]
+==== HBase API Surface
+
+HBase has a lot of API points, but for the compatibility matrix above, we differentiate between Client API, Limited Private API, and Private API. HBase uses a version of link:https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/Compatibility.html[Hadoop's Interface classification]. HBase's Interface classification classes can be found link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/classification/package-summary.html[here].
+
+* InterfaceAudience: captures the intended audience, possible values are Public (for end users and external projects), LimitedPrivate (for other Projects, Coprocessors or other plugin points), and Private (for internal use).
+* InterfaceStability: describes what types of interface changes are permitted. Possible values are Stable, Evolving, Unstable, and Deprecated.
+
+[[hbase.client.api]]
+HBase Client API::
+ HBase Client API consists of all the classes or methods that are marked with InterfaceAudience.Public interface. All main classes in hbase-client and dependent modules have either InterfaceAudience.Public, InterfaceAudience.LimitedPrivate, or InterfaceAudience.Private marker. Not all classes in other modules (hbase-server, etc) have the marker. If a class is not annotated with one of these, it is assumed to be a InterfaceAudience.Private class.
+
+[[hbase.limitetprivate.api]]
+HBase LimitedPrivate API::
+ LimitedPrivate annotation comes with a set of target consumers for the interfaces. Those consumers are coprocessors, phoenix, replication endpoint implemnetations or similar. At this point, HBase only guarantees source and binary compatibility for these interfaces between patch versions.
+
+[[hbase.private.api]]
+HBase Private API::
+ All classes annotated with InterfaceAudience.Private or all classes that do not have the annotation are for HBase internal use only. The interfaces and method signatures can change at any point in time. If you are relying on a particular interface that is marked Private, you should open a jira to propose changing the interface to be Public or LimitedPrivate, or an interface exposed for this purpose.
+
+[[hbase.versioning.pre10]]
+=== Pre 1.0 versions
+
+Before the semantic versioning scheme pre-1.0, HBase tracked either Hadoop's versions (0.2x) or 0.9x versions. If you are into the arcane, checkout our old wiki page on link:http://wiki.apache.org/hadoop/Hbase/HBaseVersions[HBase Versioning] which tries to connect the HBase version dots. Below sections cover ONLY the releases before 1.0.
+
+[[hbase.development.series]]
+.Odd/Even Versioning or "Development" Series Releases
+Ahead of big releases, we have been putting up preview versions to start the feedback cycle turning-over earlier. These "Development" Series releases, always odd-numbered, come with no guarantees, not even regards being able to upgrade between two sequential releases (we reserve the right to break compatibility across "Development" Series releases). Needless to say, these releases are not for production deploys. They are a preview of what is coming in the hope that interested parties will take the release for a test drive and flag us early if we there are issues we've missed ahead of our rolling a production-worthy release.
+
+Our first "Development" Series was the 0.89 set that came out ahead of HBase 0.90.0. HBase 0.95 is another "Development" Series that portends HBase 0.96.0. 0.99.x is the last series in "developer preview" mode before 1.0. Afterwards, we will be using semantic versioning naming scheme (see above).
+
+[[hbase.binary.compatibility]]
+.Binary Compatibility
+When we say two HBase versions are compatible, we mean that the versions are wire and binary compatible. Compatible HBase versions means that clients can talk to compatible but differently versioned servers. It means too that you can just swap out the jars of one version and replace them with the jars of another, compatible version and all will just work. Unless otherwise specified, HBase point versions are (mostly) binary compatible. You can safely do rolling upgrades between binary compatible versions; i.e. across point versions: e.g. from 0.94.5 to 0.94.6. See link:[Does compatibility between versions also mean binary compatibility?] discussion on the HBase dev mailing list.
+
+[[hbase.rolling.upgrade]]
+=== Rolling Upgrades
+
+A rolling upgrade is the process by which you update the servers in your cluster a server at a time. You can rolling upgrade across HBase versions if they are binary or wire compatible. See <<hbase.rolling.restart>> for more on what this means. Coarsely, a rolling upgrade is a graceful stop each server, update the software, and then restart. You do this for each server in the cluster. Usually you upgrade the Master first and then the RegionServers. See <<rolling>> for tools that can help use the rolling upgrade process.
+
+For example, in the below, HBase was symlinked to the actual HBase install. On upgrade, before running a rolling restart over the cluser, we changed the symlink to point at the new HBase software version and then ran
+
+[source,bash]
+----
+$ HADOOP_HOME=~/hadoop-2.6.0-CRC-SNAPSHOT ~/hbase/bin/rolling-restart.sh --config ~/conf_hbase
+----
+
+The rolling-restart script will first gracefully stop and restart the master, and then each of the RegionServers in turn. Because the symlink was changed, on restart the server will come up using the new HBase version. Check logs for errors as the rolling upgrade proceeds.
+
+[[hbase.rolling.restart]]
+.Rolling Upgrade Between Versions that are Binary/Wire Compatible
+Unless otherwise specified, HBase point versions are binary compatible. You can do a <<hbase.rolling.upgrade>> between HBase point versions. For example, you can go to 0.94.6 from 0.94.5 by doing a rolling upgrade across the cluster replacing the 0.94.5 binary with a 0.94.6 binary.
+
+In the minor version-particular sections below, we call out where the versions are wire/protocol compatible and in this case, it is also possible to do a <<hbase.rolling.upgrade>>. For example, in <<upgrade1.0.rolling.upgrade>>, we state that it is possible to do a rolling upgrade between hbase-0.98.x and hbase-1.0.0.
+
+== Upgrade Paths
+
+[[upgrade1.0]]
+=== Upgrading from 0.98.x to 1.0.x
+
+In this section we first note the significant changes that come in with 1.0.0 HBase and then we go over the upgrade process. Be sure to read the significant changes section with care so you avoid surprises.
+
+==== Changes of Note!
+
+In here we list important changes that are in 1.0.0 since 0.98.x., changes you should be aware that will go into effect once you upgrade.
+
+[[zookeeper.3.4]]
+.ZooKeeper 3.4 is required in HBase 1.0.0
+See <<zookeeper.requirements>>.
+
+[[default.ports.changed]]
+.HBase Default Ports Changed
+The ports used by HBase changed. They used to be in the 600XX range. In HBase 1.0.0 they have been moved up out of the ephemeral port range and are 160XX instead (Master web UI was 60010 and is now 16010; the RegionServer web UI was 60030 and is now 16030, etc.). If you want to keep the old port locations, copy the port setting configs from _hbase-default.xml_ into _hbase-site.xml_, change them back to the old values from the HBase 0.98.x era, and ensure you've distributed your configurations before you restart.
+
+[[upgrade1.0.hbase.bucketcache.percentage.in.combinedcache]]
+.hbase.bucketcache.percentage.in.combinedcache configuration has been REMOVED
+You may have made use of this configuration if you are using BucketCache. If NOT using BucketCache, this change does not effect you. Its removal means that your L1 LruBlockCache is now sized using `hfile.block.cache.size` -- i.e. the way you would size the on-heap L1 LruBlockCache if you were NOT doing BucketCache -- and the BucketCache size is not whatever the setting for `hbase.bucketcache.size` is. You may need to adjust configs to get the LruBlockCache and BucketCache sizes set to what they were in 0.98.x and previous. If you did not set this config., its default value was 0.9. If you do nothing, your BucketCache will increase in size by 10%. Your L1 LruBlockCache will become `hfile.block.cache.size` times your java heap size (`hfile.block.cache.size` is a float between 0.0 and 1.0). To read more, see link:https://issues.apache.org/jira/browse/HBASE-11520[HBASE-11520 Simplify offheap cache config by removing the confusing "hbase.bucketcache.percentage.in.combinedcache"].
+
+[[hbase-12068]]
+.If you have your own customer filters.
+See the release notes on the issue link:https://issues.apache.org/jira/browse/HBASE-12068[HBASE-12068 [Branch-1\] Avoid need to always do KeyValueUtil#ensureKeyValue for Filter transformCell]; be sure to follow the recommendations therein.
+
+[[dlr]]
+.Distributed Log Replay
+<<distributed.log.replay>> is off by default in HBase 1.0.0. Enabling it can make a big difference improving HBase MTTR. Enable this feature if you are doing a clean stop/start when you are upgrading. You cannot rolling upgrade to this feature (caveat if you are running on a version of HBase in excess of HBase 0.98.4 -- see link:https://issues.apache.org/jira/browse/HBASE-12577[HBASE-12577 Disable distributed log replay by default] for more).
+
+[[upgrade1.0.rolling.upgrade]]
+==== Rolling upgrade from 0.98.x to HBase 1.0.0
+.From 0.96.x to 1.0.0
+NOTE: You cannot do a <<hbase.rolling.upgrade,rolling upgrade>> from 0.96.x to 1.0.0 without first doing a rolling upgrade to 0.98.x. See comment in link:https://issues.apache.org/jira/browse/HBASE-11164?focusedCommentId=14182330&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14182330[HBASE-11164 Document and test rolling updates from 0.98 -> 1.0] for the why. Also because HBase 1.0.0 enables HFile v3 by default, link:https://issues.apache.org/jira/browse/HBASE-9801[HBASE-9801 Change the default HFile version to V3], and support for HFile v3 only arrives in 0.98, this is another reason you cannot rolling upgrade from HBase 0.96.x; if the rolling upgrade stalls, the 0.96.x servers cannot open files written by the servers running the newer HBase 1.0.0 with HFile's of version 3.
+
+There are no known issues running a <<hbase.rolling.upgrade,rolling upgrade>> from HBase 0.98.x to HBase 1.0.0.
+
+[[upgrade1.0.from.0.94]]
+==== Upgrading to 1.0 from 0.94
+You cannot rolling upgrade from 0.94.x to 1.x.x. You must stop your cluster, install the 1.x.x software, run the migration described at <<executing.the.0.96.upgrade>> (substituting 1.x.x. wherever we make mention of 0.96.x in the section below), and then restart. Be sure to upgrade your ZooKeeper if it is a version less than the required 3.4.x.
+
+[[upgrade0.98]]
+=== Upgrading from 0.96.x to 0.98.x
+A rolling upgrade from 0.96.x to 0.98.x works. The two versions are not binary compatible.
+
+Additional steps are required to take advantage of some of the new features of 0.98.x, including cell visibility labels, cell ACLs, and transparent server side encryption. See <<security>> for more information. Significant performance improvements include a change to the write ahead log threading model that provides higher transaction throughput under high load, reverse scanners, MapReduce over snapshot files, and striped compaction.
+
+Clients and servers can run with 0.98.x and 0.96.x versions. However, applications may need to be recompiled due to changes in the Java API.
+
+=== Upgrading from 0.94.x to 0.98.x
+A rolling upgrade from 0.94.x directly to 0.98.x does not work. The upgrade path follows the same procedures as <<upgrade0.96>>. Additional steps are required to use some of the new features of 0.98.x. See <<upgrade0.98>> for an abbreviated list of these features.
+
+[[upgrade0.96]]
+=== Upgrading from 0.94.x to 0.96.x
+
+==== The "Singularity"
+
+.HBase 0.96.x was EOL'd, September 1st, 2014
+NOTE: Do not deploy 0.96.x Deploy at least 0.98.x. See link:https://issues.apache.org/jira/browse/HBASE-11642[EOL 0.96].
+
+You will have to stop your old 0.94.x cluster completely to upgrade. If you are replicating between clusters, both clusters will have to go down to upgrade. Make sure it is a clean shutdown. The less WAL files around, the faster the upgrade will run (the upgrade will split any log files it finds in the filesystem as part of the upgrade process). All clients must be upgraded to 0.96 too.
+
+The API has changed. You will need to recompile your code against 0.96 and you may need to adjust applications to go against new APIs (TODO: List of changes).
+
+[[executing.the.0.96.upgrade]]
+==== Executing the 0.96 Upgrade
+
+.HDFS and ZooKeeper must be up!
+NOTE: HDFS and ZooKeeper should be up and running during the upgrade process.
+
+HBase 0.96.0 comes with an upgrade script. Run
+
+[source,bash]
+----
+$ bin/hbase upgrade
+----
+to see its usage. The script has two main modes: `-check`, and `-execute`.
+
+.check
+The check step is run against a running 0.94 cluster. Run it from a downloaded 0.96.x binary. The check step is looking for the presence of HFile v1 files. These are unsupported in HBase 0.96.0. To have them rewritten as HFile v2 you must run a compaction.
+
+The check step prints stats at the end of its run (grep for `“Result:”` in the log) printing absolute path of the tables it scanned, any HFile v1 files found, the regions containing said files (these regions will need a major compaction), and any corrupted files if found. A corrupt file is unreadable, and so is undefined (neither HFile v1 nor HFile v2).
+
+To run the check step, run
+
+[source,bash]
+----
+$ bin/hbase upgrade -check
+----
+
+Here is sample output:
+----
+Tables Processed:
+hdfs://localhost:41020/myHBase/.META.
+hdfs://localhost:41020/myHBase/usertable
+hdfs://localhost:41020/myHBase/TestTable
+hdfs://localhost:41020/myHBase/t
+
+Count of HFileV1: 2
+HFileV1:
+hdfs://localhost:41020/myHBase/usertable /fa02dac1f38d03577bd0f7e666f12812/family/249450144068442524
+hdfs://localhost:41020/myHBase/usertable /ecdd3eaee2d2fcf8184ac025555bb2af/family/249450144068442512
+
+Count of corrupted files: 1
+Corrupted Files:
+hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812/family/1
+Count of Regions with HFileV1: 2
+Regions to Major Compact:
+hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812
+hdfs://localhost:41020/myHBase/usertable/ecdd3eaee2d2fcf8184ac025555bb2af
+
+There are some HFileV1, or corrupt files (files with incorrect major version)
+----
+
+In the above sample output, there are two HFile v1 files in two regions, and one corrupt file. Corrupt files should probably be removed. The regions that have HFile v1s need to be major compacted. To major compact, start up the hbase shell and review how to compact an individual region. After the major compaction is done, rerun the check step and the HFile v1 files should be gone, replaced by HFile v2 instances.
+
+By default, the check step scans the HBase root directory (defined as `hbase.rootdir` in the configuration). To scan a specific directory only, pass the `-dir` option.
+[source,bash]
+----
+$ bin/hbase upgrade -check -dir /myHBase/testTable
+----
+The above command would detect HFile v1 files in the _/myHBase/testTable_ directory.
+
+Once the check step reports all the HFile v1 files have been rewritten, it is safe to proceed with the upgrade.
+
+.execute
+After the _check_ step shows the cluster is free of HFile v1, it is safe to proceed with the upgrade. Next is the _execute_ step. You must *SHUTDOWN YOUR 0.94.x CLUSTER* before you can run the execute step. The execute step will not run if it detects running HBase masters or RegionServers.
+
+[NOTE]
+====
+HDFS and ZooKeeper should be up and running during the upgrade process. If zookeeper is managed by HBase, then you can start zookeeper so it is available to the upgrade by running
+[source,bash]
+----
+$ ./hbase/bin/hbase-daemon.sh start zookeeper
+----
+====
+
+The execute upgrade step is made of three substeps.
+
+* Namespaces: HBase 0.96.0 has support for namespaces. The upgrade needs to reorder directories in the filesystem for namespaces to work.
+
+* ZNodes: All znodes are purged so that new ones can be written in their place using a new protobuf'ed format and a few are migrated in place: e.g. replication and table state znodes
+
+* WAL Log Splitting: If the 0.94.x cluster shutdown was not clean, we'll split WAL logs as part of migration before we startup on 0.96.0. This WAL splitting runs slower than the native distributed WAL splitting because it is all inside the single upgrade process (so try and get a clean shutdown of the 0.94.0 cluster if you can).
+
+To run the _execute_ step, make sure that first you have copied HBase 0.96.0 binaries everywhere under servers and under clients. Make sure the 0.94.0 cluster is down. Then do as follows:
+[source,bash]
+----
+$ bin/hbase upgrade -execute
+----
+Here is some sample output.
+
+----
+Starting Namespace upgrade
+Created version file at hdfs://localhost:41020/myHBase with version=7
+Migrating table testTable to hdfs://localhost:41020/myHBase/.data/default/testTable
+.....
+Created version file at hdfs://localhost:41020/myHBase with version=8
+Successfully completed NameSpace upgrade.
+Starting Znode upgrade
+.....
+Successfully completed Znode upgrade
+
+Starting Log splitting
+...
+Successfully completed Log splitting
+----
+
+If the output from the execute step looks good, stop the zookeeper instance you started to do the upgrade:
+[source,bash]
+----
+$ ./hbase/bin/hbase-daemon.sh stop zookeeper
+----
+Now start up hbase-0.96.0.
+
+[[s096.migration.troubleshooting]]
+=== Troubleshooting
+
+[[s096.migration.troubleshooting.old.client]]
+.Old Client connecting to 0.96 cluster
+It will fail with an exception like the below. Upgrade.
+----
+17:22:15 Exception in thread "main" java.lang.IllegalArgumentException: Not a host:port pair: PBUF
+17:22:15 *
+17:22:15 api-compat-8.ent.cloudera.com �� ���(
+17:22:15 at org.apache.hadoop.hbase.util.Addressing.parseHostname(Addressing.java:60)
+17:22:15 at org.apache.hadoop.hbase.ServerName.&init>(ServerName.java:101)
+17:22:15 at org.apache.hadoop.hbase.ServerName.parseVersionedServerName(ServerName.java:283)
+17:22:15 at org.apache.hadoop.hbase.MasterAddressTracker.bytesToServerName(MasterAddressTracker.java:77)
+17:22:15 at org.apache.hadoop.hbase.MasterAddressTracker.getMasterAddress(MasterAddressTracker.java:61)
+17:22:15 at org.apache.hadoop.hbase.client.HConnectionManager$HConnectionImplementation.getMaster(HConnectionManager.java:703)
+17:22:15 at org.apache.hadoop.hbase.client.HBaseAdmin.&init>(HBaseAdmin.java:126)
+17:22:15 at Client_4_3_0.setup(Client_4_3_0.java:716)
+17:22:15 at Client_4_3_0.main(Client_4_3_0.java:63)
+----
+
+==== Upgrading `META` to use Protocol Buffers (Protobuf)
+
+When you upgrade from versions prior to 0.96, `META` needs to be converted to use protocol buffers. This is controlled by the configuration option `hbase.MetaMigrationConvertingToPB`, which is set to `true` by default. Therefore, by default, no action is required on your part.
+
+The migration is a one-time event. However, every time your cluster starts, `META` is scanned to ensure that it does not need to be converted. If you have a very large number of regions, this scan can take a long time. Starting in 0.98.5, you can set `hbase.MetaMigrationConvertingToPB` to `false` in _hbase-site.xml_, to disable this start-up scan. This should be considered an expert-level setting.
+
+[[upgrade0.94]]
+=== Upgrading from 0.92.x to 0.94.x
+We used to think that 0.92 and 0.94 were interface compatible and that you can do a rolling upgrade between these versions but then we figured that link:https://issues.apache.org/jira/browse/HBASE-5357[HBASE-5357 Use builder pattern in HColumnDescriptor] changed method signatures so rather than return `void` they instead return `HColumnDescriptor`. This will throw`java.lang.NoSuchMethodError: org.apache.hadoop.hbase.HColumnDescriptor.setMaxVersions(I)V` so 0.92 and 0.94 are NOT compatible. You cannot do a rolling upgrade between them.
+
+[[upgrade0.92]]
+=== Upgrading from 0.90.x to 0.92.x
+==== Upgrade Guide
+You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few things to watch out for upgrading from 0.90.x to 0.92.0.
+
+.tl:dr
+[NOTE]
+====
+These are the important things to know before upgrading.
+. Once you upgrade, you can’t go back.
+
+. MSLAB is on by default. Watch that heap usage if you have a lot of regions.
+
+. Distributed Log Splitting is on by default. It should make RegionServer failover faster.
+
+. There’s a separate tarball for security.
+
+. If `-XX:MaxDirectMemorySize` is set in your _hbase-env.sh_, it’s going to enable the experimental off-heap cache (You may not want this).
+====
+
+.You can’t go back!
+To move to 0.92.0, all you need to do is shutdown your cluster, replace your HBase 0.90.x with HBase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart). On startup, the `.META.` table content is rewritten removing the table schema from the `info:regioninfo` column. Also, any flushes done post first startup will write out data in the new 0.92.0 file format, <<hfilev2>>. This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.
+
+.MSLAB is ON by default
+In 0.92.0, the `<<hbase.hregion.memstore.mslab.enabled,hbase.hregion.memstore.mslab.enabled>>` flag is set to `true` (See <<gcpause>>). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per RegionServer in a 0.90.x cluster (and MSLAB was off), you may find yourself OOME'ing on upgrade because the `thousands of regions * number of column families * 2MB MSLAB` (at a minimum) puts your heap over the top. Set `hbase.hregion.memstore.mslab.enabled` to `false` or set the MSLAB size down from 2MB by setting `hbase.hregion.memstore.mslab.chunksize` to something less.
+
+[[dls]]
+.Distributed Log Splitting is on by default
+Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log splitting is done by the cluster (See link:https://issues.apache.org/jira/browse/hbase-1364[HBASE-1364 [performance\] Distributed splitting of regionserver commit logs] or see the blog post link:http://blog.cloudera.com/blog/2012/07/hbase-log-splitting/[Apache HBase Log Splitting]). This should cut down significantly on the amount of time it takes splitting logs and getting regions back online again.
+
+.Memory accounting is different now
+In 0.92.0, <<hfilev2>> indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem. In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you have less space for block caching. Adjust your block cache accordingly. See the <<block.cache>> for more detail. The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.
+
+.On the Hadoop version to use
+Run 0.92.0 on Hadoop 1.0.x (or CDH3u3). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. See <<hadoop>>.
+
+If running on Hadoop 1.0.x (or CDH3u3), enable local read. See link:http://files.meetup.com/1350427/hug_ebay_jdcryans.pdf[Practical Caching] presentation for ruminations on the performance benefits ‘going local’ (and for how to enable local reads).
+
+.HBase 0.92.0 ships with ZooKeeper 3.4.2
+If you can, upgrade your ZooKeeper. If you can’t, 3.4.2 clients should work against 3.3.X ensembles (HBase makes use of 3.4.2 API).
+
+.Online alter is off by default
+In 0.92.0, we’ve added an experimental online schema alter facility (See <<hbase.online.schema.update.enable,hbase.online.schema.update.enable>>). It's off by default. Enable it at your own risk. Online alter and splitting tables do not play well together so be sure your cluster quiescent using this feature (for now).
+
+.WebUI
+The web UI has had a few additions made in 0.92.0. It now shows a list of the regions currently transitioning, recent compactions/flushes, and a process list of running processes (usually empty if all is well and requests are being handled promptly). Other additions including requests by region, a debugging servlet dump, etc.
+
+.Security tarball
+We now ship with two tarballs; secure and insecure HBase. Documentation on how to setup a secure HBase is on the way.
+
+.Changes in HBase replication
+0.92.0 adds two new features: multi-slave and multi-master replication. The way to enable this is the same as adding a new peer, so in order to have multi-master you would just run add_peer for each cluster that acts as a master to the other slave clusters. Collisions are handled at the timestamp level which may or may not be what you want, this needs to be evaluated on a per use case basis. Replication is still experimental in 0.92 and is disabled by default, run it at your own risk.
+
+.RegionServer now aborts if OOME
+If an OOME, we now have the JVM kill -9 the RegionServer process so it goes down fast. Previous, a RegionServer might stick around after incurring an OOME limping along in some wounded state. To disable this facility, and recommend you leave it in place, you’d need to edit the bin/hbase file. Look for the addition of the -XX:OnOutOfMemoryError="kill -9 %p" arguments (See link:https://issues.apache.org/jira/browse/HBASE-4769[HBASE-4769 - ‘Abort RegionServer Immediately on OOME’]).
+
+.HFile v2 and the “Bigger, Fewer” Tendency
+0.92.0 stores data in a new format, <<hfilev2>>. As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run. HFile v2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions. If you have lots of regions now -- more than 100s per host -- you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is now 1G, up from 256M), and then running online merge tool (See link:https://issues.apache.org/jira/browse/HBASE-1621[HBASE-1621 merge tool should work on online cluster, but disabled table]).
+
+[[upgrade0.90]]
+=== Upgrading to HBase 0.90.x from 0.20.x or 0.89.x
+This version of 0.90.x HBase can be started on data written by HBase 0.20.x or HBase 0.89.x. There is no need of a migration step. HBase 0.89.x and 0.90.x does write out the name of region directories differently -- it names them with a md5 hash of the region name rather than a jenkins hash -- so this means that once started, there is no going back to HBase 0.20.x.
+
+Be sure to remove the _hbase-default.xml_ from your _conf_ directory on upgrade. A 0.20.x version of this file will have sub-optimal configurations for 0.90.x HBase. The _hbase-default.xml_ file is now bundled into the HBase jar and read from there. If you would like to review the content of this file, see it in the src tree at _src/main/resources/hbase-default.xml_ or see <<hbase_default_configurations>>.
+
+Finally, if upgrading from 0.20.x, check your .META. schema in the shell. In the past we would recommend that users run with a 16kb MEMSTORE_FLUSHSIZE. Run
+----
+hbase> scan '-ROOT-'
+----
+in the shell. This will output the current `.META.` schema. Check `MEMSTORE_FLUSHSIZE` size. Is it 16kb (16384)? If so, you will need to change this (The 'normal'/default value is 64MB (67108864)). Run the script `bin/set_meta_memstore_size.rb`. This will make the necessary edit to your `.META.` schema. Failure to run this change will make for a slow cluster. See link:https://issues.apache.org/jira/browse/HBASE-3499[HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE].
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/ycsb.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/ycsb.adoc b/src/main/asciidoc/_chapters/ycsb.adoc
new file mode 100644
index 0000000..d8ec628
--- /dev/null
+++ b/src/main/asciidoc/_chapters/ycsb.adoc
@@ -0,0 +1,42 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+== YCSB
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+link:https://github.com/brianfrankcooper/YCSB/[YCSB: The
+ Yahoo! Cloud Serving Benchmark] and HBase
+
+TODO: Describe how YCSB is poor for putting up a decent cluster load.
+
+TODO: Describe setup of YCSB for HBase.
+In particular, presplit your tables before you start a run.
+See link:https://issues.apache.org/jira/browse/HBASE-4163[HBASE-4163 Create Split Strategy for YCSB Benchmark] for why and a little shell command for how to do it.
+
+Ted Dunning redid YCSB so it's mavenized and added facility for verifying workloads.
+See link:https://github.com/tdunning/YCSB[Ted Dunning's YCSB].
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/zookeeper.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/zookeeper.adoc b/src/main/asciidoc/_chapters/zookeeper.adoc
new file mode 100644
index 0000000..f6134b7
--- /dev/null
+++ b/src/main/asciidoc/_chapters/zookeeper.adoc
@@ -0,0 +1,451 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[zookeeper]]
+= ZooKeeper(((ZooKeeper)))
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+A distributed Apache HBase installation depends on a running ZooKeeper cluster.
+All participating nodes and clients need to be able to access the running ZooKeeper ensemble.
+Apache HBase by default manages a ZooKeeper "cluster" for you.
+It will start and stop the ZooKeeper ensemble as part of the HBase start/stop process.
+You can also manage the ZooKeeper ensemble independent of HBase and just point HBase at the cluster it should use.
+To toggle HBase management of ZooKeeper, use the `HBASE_MANAGES_ZK` variable in _conf/hbase-env.sh_.
+This variable, which defaults to `true`, tells HBase whether to start/stop the ZooKeeper ensemble servers as part of HBase start/stop.
+
+When HBase manages the ZooKeeper ensemble, you can specify ZooKeeper configuration using its native _zoo.cfg_ file, or, the easier option is to just specify ZooKeeper options directly in _conf/hbase-site.xml_.
+A ZooKeeper configuration option can be set as a property in the HBase _hbase-site.xml_ XML configuration file by prefacing the ZooKeeper option name with `hbase.zookeeper.property`.
+For example, the `clientPort` setting in ZooKeeper can be changed by setting the `hbase.zookeeper.property.clientPort` property.
+For all default values used by HBase, including ZooKeeper configuration, see <<hbase_default_configurations,hbase default configurations>>.
+Look for the `hbase.zookeeper.property` prefix.
+For the full list of ZooKeeper configurations, see ZooKeeper's _zoo.cfg_.
+HBase does not ship with a _zoo.cfg_ so you will need to browse the _conf_ directory in an appropriate ZooKeeper download.
+
+You must at least list the ensemble servers in _hbase-site.xml_ using the `hbase.zookeeper.quorum` property.
+This property defaults to a single ensemble member at `localhost` which is not suitable for a fully distributed HBase.
+(It binds to the local machine only and remote clients will not be able to connect).
+
+.How many ZooKeepers should I run?
+[NOTE]
+====
+You can run a ZooKeeper ensemble that comprises 1 node only but in production it is recommended that you run a ZooKeeper ensemble of 3, 5 or 7 machines; the more members an ensemble has, the more tolerant the ensemble is of host failures.
+Also, run an odd number of machines.
+In ZooKeeper, an even number of peers is supported, but it is normally not used because an even sized ensemble requires, proportionally, more peers to form a quorum than an odd sized ensemble requires.
+For example, an ensemble with 4 peers requires 3 to form a quorum, while an ensemble with 5 also requires 3 to form a quorum.
+Thus, an ensemble of 5 allows 2 peers to fail, and thus is more fault tolerant than the ensemble of 4, which allows only 1 down peer.
+
+Give each ZooKeeper server around 1GB of RAM, and if possible, its own dedicated disk (A dedicated disk is the best thing you can do to ensure a performant ZooKeeper ensemble). For very heavily loaded clusters, run ZooKeeper servers on separate machines from RegionServers (DataNodes and TaskTrackers).
+====
+
+For example, to have HBase manage a ZooKeeper quorum on nodes _rs{1,2,3,4,5}.example.com_, bound to port 2222 (the default is 2181) ensure `HBASE_MANAGE_ZK` is commented out or set to `true` in _conf/hbase-env.sh_ and then edit _conf/hbase-site.xml_ and set `hbase.zookeeper.property.clientPort` and `hbase.zookeeper.quorum`.
+You should also set `hbase.zookeeper.property.dataDir` to other than the default as the default has ZooKeeper persist data under _/tmp_ which is often cleared on system restart.
+In the example below we have ZooKeeper persist to _/user/local/zookeeper_.
+
+[source,java]
+----
+
+ <configuration>
+ ...
+ <property>
+ <name>hbase.zookeeper.property.clientPort</name>
+ <value>2222</value>
+ <description>Property from ZooKeeper's config zoo.cfg.
+ The port at which the clients will connect.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com</value>
+ <description>Comma separated list of servers in the ZooKeeper Quorum.
+ For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
+ By default this is set to localhost for local and pseudo-distributed modes
+ of operation. For a fully-distributed setup, this should be set to a full
+ list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
+ this is the list of servers which we will start/stop ZooKeeper on.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/usr/local/zookeeper</value>
+ <description>Property from ZooKeeper's config zoo.cfg.
+ The directory where the snapshot is stored.
+ </description>
+ </property>
+ ...
+ </configuration>
+----
+
+.What verion of ZooKeeper should I use?
+[CAUTION]
+====
+The newer version, the better.
+For example, some folks have been bitten by link:https://issues.apache.org/jira/browse/ZOOKEEPER-1277[ZOOKEEPER-1277].
+If running zookeeper 3.5+, you can ask hbase to make use of the new multi operation by enabling <<hbase.zookeeper.usemulti,hbase.zookeeper.useMulti>>" in your _hbase-site.xml_.
+====
+
+.ZooKeeper Maintenance
+[CAUTION]
+====
+Be sure to set up the data dir cleaner described under link:http://zookeeper.apache.org/doc/r3.1.2/zookeeperAdmin.html#sc_maintenance[Zookeeper
+ Maintenance] else you could have 'interesting' problems a couple of months in; i.e.
+zookeeper could start dropping sessions if it has to run through a directory of hundreds of thousands of logs which is wont to do around leader reelection time -- a process rare but run on occasion whether because a machine is dropped or happens to hiccup.
+====
+
+== Using existing ZooKeeper ensemble
+
+To point HBase at an existing ZooKeeper cluster, one that is not managed by HBase, set `HBASE_MANAGES_ZK` in _conf/hbase-env.sh_ to false
+
+----
+
+ ...
+ # Tell HBase whether it should manage its own instance of Zookeeper or not.
+ export HBASE_MANAGES_ZK=false
+----
+
+Next set ensemble locations and client port, if non-standard, in _hbase-site.xml_, or add a suitably configured _zoo.cfg_ to HBase's _CLASSPATH_.
+HBase will prefer the configuration found in _zoo.cfg_ over any settings in _hbase-site.xml_.
+
+When HBase manages ZooKeeper, it will start/stop the ZooKeeper servers as a part of the regular start/stop scripts.
+If you would like to run ZooKeeper yourself, independent of HBase start/stop, you would do the following
+
+----
+
+${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper
+----
+
+Note that you can use HBase in this manner to spin up a ZooKeeper cluster, unrelated to HBase.
+Just make sure to set `HBASE_MANAGES_ZK` to `false` if you want it to stay up across HBase restarts so that when HBase shuts down, it doesn't take ZooKeeper down with it.
+
+For more information about running a distinct ZooKeeper cluster, see the ZooKeeper link:http://hadoop.apache.org/zookeeper/docs/current/zookeeperStarted.html[Getting
+ Started Guide].
+Additionally, see the link:http://wiki.apache.org/hadoop/ZooKeeper/FAQ#A7[ZooKeeper Wiki] or the link:http://zookeeper.apache.org/doc/r3.3.3/zookeeperAdmin.html#sc_zkMulitServerSetup[ZooKeeper
+ documentation] for more information on ZooKeeper sizing.
+
+[[zk.sasl.auth]]
+== SASL Authentication with ZooKeeper
+
+Newer releases of Apache HBase (>= 0.92) will support connecting to a ZooKeeper Quorum that supports SASL authentication (which is available in Zookeeper versions 3.4.0 or later).
+
+This describes how to set up HBase to mutually authenticate with a ZooKeeper Quorum.
+ZooKeeper/HBase mutual authentication (link:https://issues.apache.org/jira/browse/HBASE-2418[HBASE-2418]) is required as part of a complete secure HBase configuration (link:https://issues.apache.org/jira/browse/HBASE-3025[HBASE-3025]). For simplicity of explication, this section ignores additional configuration required (Secure HDFS and Coprocessor configuration). It's recommended to begin with an HBase-managed Zookeeper configuration (as opposed to a standalone Zookeeper quorum) for ease of learning.
+
+=== Operating System Prerequisites
+
+You need to have a working Kerberos KDC setup.
+For each `$HOST` that will run a ZooKeeper server, you should have a principle `zookeeper/$HOST`.
+For each such host, add a service key (using the `kadmin` or `kadmin.local` tool's `ktadd` command) for `zookeeper/$HOST` and copy this file to `$HOST`, and make it readable only to the user that will run zookeeper on `$HOST`.
+Note the location of this file, which we will use below as _$PATH_TO_ZOOKEEPER_KEYTAB_.
+
+Similarly, for each `$HOST` that will run an HBase server (master or regionserver), you should have a principle: `hbase/$HOST`.
+For each host, add a keytab file called _hbase.keytab_ containing a service key for `hbase/$HOST`, copy this file to `$HOST`, and make it readable only to the user that will run an HBase service on `$HOST`.
+Note the location of this file, which we will use below as _$PATH_TO_HBASE_KEYTAB_.
+
+Each user who will be an HBase client should also be given a Kerberos principal.
+This principal should usually have a password assigned to it (as opposed to, as with the HBase servers, a keytab file) which only this user knows.
+The client's principal's `maxrenewlife` should be set so that it can be renewed enough so that the user can complete their HBase client processes.
+For example, if a user runs a long-running HBase client process that takes at most 3 days, we might create this user's principal within `kadmin` with: `addprinc -maxrenewlife 3days`.
+The Zookeeper client and server libraries manage their own ticket refreshment by running threads that wake up periodically to do the refreshment.
+
+On each host that will run an HBase client (e.g. `hbase shell`), add the following file to the HBase home directory's _conf_ directory:
+
+[source,java]
+----
+
+Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=false
+ useTicketCache=true;
+};
+----
+
+We'll refer to this JAAS configuration file as _$CLIENT_CONF_ below.
+
+=== HBase-managed Zookeeper Configuration
+
+On each node that will run a zookeeper, a master, or a regionserver, create a link:http://docs.oracle.com/javase/1.4.2/docs/guide/security/jgss/tutorials/LoginConfigFile.html[JAAS] configuration file in the conf directory of the node's _HBASE_HOME_ directory that looks like the following:
+
+[source,java]
+----
+
+Server {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
+ storeKey=true
+ useTicketCache=false
+ principal="zookeeper/$HOST";
+};
+Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ useTicketCache=false
+ keyTab="$PATH_TO_HBASE_KEYTAB"
+ principal="hbase/$HOST";
+};
+----
+
+where the _$PATH_TO_HBASE_KEYTAB_ and _$PATH_TO_ZOOKEEPER_KEYTAB_ files are what you created above, and `$HOST` is the hostname for that node.
+
+The `Server` section will be used by the Zookeeper quorum server, while the `Client` section will be used by the HBase master and regionservers.
+The path to this file should be substituted for the text _$HBASE_SERVER_CONF_ in the _hbase-env.sh_ listing below.
+
+The path to this file should be substituted for the text _$CLIENT_CONF_ in the _hbase-env.sh_ listing below.
+
+Modify your _hbase-env.sh_ to include the following:
+
+[source,bourne]
+----
+
+export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
+export HBASE_MANAGES_ZK=true
+export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+----
+
+where _$HBASE_SERVER_CONF_ and _$CLIENT_CONF_ are the full paths to the JAAS configuration files created above.
+
+Modify your _hbase-site.xml_ on each node that will run zookeeper, master or regionserver to contain:
+
+[source,java]
+----
+
+<configuration>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>$ZK_NODES</value>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.authProvider.1</name>
+ <value>org.apache.zookeeper.server.auth.SASLAuthenticationProvider</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.kerberos.removeHostFromPrincipal</name>
+ <value>true</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.kerberos.removeRealmFromPrincipal</name>
+ <value>true</value>
+ </property>
+</configuration>
+----
+
+where `$ZK_NODES` is the comma-separated list of hostnames of the Zookeeper Quorum hosts.
+
+Start your hbase cluster by running one or more of the following set of commands on the appropriate hosts:
+
+----
+
+bin/hbase zookeeper start
+bin/hbase master start
+bin/hbase regionserver start
+----
+
+=== External Zookeeper Configuration
+
+Add a JAAS configuration file that looks like:
+
+[source,java]
+----
+
+Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ useTicketCache=false
+ keyTab="$PATH_TO_HBASE_KEYTAB"
+ principal="hbase/$HOST";
+};
+----
+
+where the _$PATH_TO_HBASE_KEYTAB_ is the keytab created above for HBase services to run on this host, and `$HOST` is the hostname for that node.
+Put this in the HBase home's configuration directory.
+We'll refer to this file's full pathname as _$HBASE_SERVER_CONF_ below.
+
+Modify your hbase-env.sh to include the following:
+
+[source,bourne]
+----
+
+export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
+export HBASE_MANAGES_ZK=false
+export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+----
+
+Modify your _hbase-site.xml_ on each node that will run a master or regionserver to contain:
+
+[source,xml]
+----
+
+<configuration>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>$ZK_NODES</value>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ </property>
+</configuration>
+----
+
+where `$ZK_NODES` is the comma-separated list of hostnames of the Zookeeper Quorum hosts.
+
+Add a _zoo.cfg_ for each Zookeeper Quorum host containing:
+
+[source,java]
+----
+
+authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
+kerberos.removeHostFromPrincipal=true
+kerberos.removeRealmFromPrincipal=true
+----
+
+Also on each of these hosts, create a JAAS configuration file containing:
+
+[source,java]
+----
+
+Server {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
+ storeKey=true
+ useTicketCache=false
+ principal="zookeeper/$HOST";
+};
+----
+
+where `$HOST` is the hostname of each Quorum host.
+We will refer to the full pathname of this file as _$ZK_SERVER_CONF_ below.
+
+Start your Zookeepers on each Zookeeper Quorum host with:
+
+[source,bourne]
+----
+
+SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start
+----
+
+Start your HBase cluster by running one or more of the following set of commands on the appropriate nodes:
+
+----
+
+bin/hbase master start
+bin/hbase regionserver start
+----
+
+=== Zookeeper Server Authentication Log Output
+
+If the configuration above is successful, you should see something similar to the following in your Zookeeper server logs:
+
+----
+
+11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in.
+11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port 0.0.0.0/0.0.0.0:2181
+11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started.
+11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:39 UTC 2011
+11/12/05 22:43:39 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:39 UTC 2011
+11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:36:42 UTC 2011
+..
+11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler:
+ Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN;
+ authorizationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN.
+11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase
+11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for authorizationID: hbase
+----
+
+=== Zookeeper Client Authentication Log Output
+
+On the Zookeeper client side (HBase master or regionserver), you should see something similar to the following:
+
+----
+
+11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000 watcher=master:60000
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server /10.166.175.249:2181
+11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process is 14851@ip-10-166-175-249
+11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in.
+11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL mechanism.
+11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started.
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session
+11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:59 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:59 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:30:37 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid = 0x134106594320000, negotiated timeout = 180000
+----
+
+=== Configuration from Scratch
+
+This has been tested on the current standard Amazon Linux AMI.
+First setup KDC and principals as described above.
+Next checkout code and run a sanity check.
+
+----
+
+git clone git://git.apache.org/hbase.git
+cd hbase
+mvn clean test -Dtest=TestZooKeeperACL
+----
+
+Then configure HBase as described above.
+Manually edit target/cached_classpath.txt (see below):
+
+----
+
+bin/hbase zookeeper &
+bin/hbase master &
+bin/hbase regionserver &
+----
+
+=== Future improvements
+
+==== Fix target/cached_classpath.txt
+
+You must override the standard hadoop-core jar file from the `target/cached_classpath.txt` file with the version containing the HADOOP-7070 fix.
+You can use the following script to do this:
+
+----
+
+echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt
+mv target/tmp.txt target/cached_classpath.txt
+----
+
+==== Set JAAS configuration programmatically
+
+This would avoid the need for a separate Hadoop jar that fixes link:https://issues.apache.org/jira/browse/HADOOP-7070[HADOOP-7070].
+
+==== Elimination of `kerberos.removeHostFromPrincipal` and`kerberos.removeRealmFromPrincipal`
+
+
+
+ifdef::backend-docbook[]
+[index]
+= Index
+// Generated automatically by the DocBook toolchain.
+endif::backend-docbook[]
[12/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/external_apis.adoc
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diff --git a/src/main/asciidoc/_chapters/external_apis.adoc b/src/main/asciidoc/_chapters/external_apis.adoc
new file mode 100644
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@@ -0,0 +1,66 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[external_apis]]
+= Apache HBase External APIs
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter will cover access to Apache HBase either through non-Java languages, or through custom protocols.
+For information on using the native HBase APIs, refer to link:http://hbase.apache.org/apidocs/index.html[User API Reference] and the new <<hbase_apis,HBase APIs>> chapter.
+
+[[nonjava.jvm]]
+== Non-Java Languages Talking to the JVM
+
+Currently the documentation on this topic is in the link:http://wiki.apache.org/hadoop/Hbase[Apache HBase Wiki].
+See also the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/thrift/package-summary.html#package_description[Thrift API Javadoc].
+
+== REST
+
+Currently most of the documentation on REST exists in the link:http://wiki.apache.org/hadoop/Hbase/Stargate[Apache HBase Wiki on REST] (The REST gateway used to be called 'Stargate'). There are also a nice set of blogs on link:http://blog.cloudera.com/blog/2013/03/how-to-use-the-apache-hbase-rest-interface-part-1/[How-to: Use the Apache HBase REST Interface] by Jesse Anderson.
+
+To run your REST server under SSL, set `hbase.rest.ssl.enabled` to `true` and also set the following configs when you launch the REST server: (See example commands in <<jmx_config,JMX config>>)
+
+[source]
+----
+hbase.rest.ssl.keystore.store
+hbase.rest.ssl.keystore.password
+hbase.rest.ssl.keystore.keypassword
+----
+
+HBase ships a simple REST client, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/rest/client/package-summary.html[REST client] package for details.
+To enable SSL support for it, please also import your certificate into local java cacerts keystore:
+----
+keytool -import -trustcacerts -file /home/user/restserver.cert -keystore $JAVA_HOME/jre/lib/security/cacerts
+----
+
+== Thrift
+
+Documentation about Thrift has moved to <<thrift>>.
+
+[[c]]
+== C/C++ Apache HBase Client
+
+FB's Chip Turner wrote a pure C/C++ client.
+link:https://github.com/facebook/native-cpp-hbase-client[Check it out].
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/faq.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/faq.adoc b/src/main/asciidoc/_chapters/faq.adoc
new file mode 100644
index 0000000..22e4ad3
--- /dev/null
+++ b/src/main/asciidoc/_chapters/faq.adoc
@@ -0,0 +1,141 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[faq]]
+== FAQ
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+=== General
+When should I use HBase?::
+ See <<arch.overview>> in the Architecture chapter.
+
+Are there other HBase FAQs?::
+ See the FAQ that is up on the wiki, link:http://wiki.apache.org/hadoop/Hbase/FAQ[HBase Wiki FAQ].
+
+Does HBase support SQL?::
+ Not really. SQL-ish support for HBase via link:http://hive.apache.org/[Hive] is in development, however Hive is based on MapReduce which is not generally suitable for low-latency requests. See the <<datamodel>> section for examples on the HBase client.
+
+How can I find examples of NoSQL/HBase?::
+ See the link to the BigTable paper in <<other.info>>, as well as the other papers.
+
+What is the history of HBase?::
+ See <<hbase.history,hbase.history>>.
+
+=== Upgrading
+How do I upgrade Maven-managed projects from HBase 0.94 to HBase 0.96+?::
+ In HBase 0.96, the project moved to a modular structure. Adjust your project's dependencies to rely upon the `hbase-client` module or another module as appropriate, rather than a single JAR. You can model your Maven depency after one of the following, depending on your targeted version of HBase. See Section 3.5, “Upgrading from 0.94.x to 0.96.x” or Section 3.3, “Upgrading from 0.96.x to 0.98.x” for more information.
++
+.Maven Dependency for HBase 0.98
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.hbase</groupId>
+ <artifactId>hbase-client</artifactId>
+ <version>0.98.5-hadoop2</version>
+</dependency>
+----
++
+.Maven Dependency for HBase 0.96
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.hbase</groupId>
+ <artifactId>hbase-client</artifactId>
+ <version>0.96.2-hadoop2</version>
+</dependency>
+----
++
+.Maven Dependency for HBase 0.94
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.hbase</groupId>
+ <artifactId>hbase</artifactId>
+ <version>0.94.3</version>
+</dependency>
+----
+
+
+=== Architecture
+How does HBase handle Region-RegionServer assignment and locality?::
+ See <<regions.arch>>.
+
+=== Configuration
+How can I get started with my first cluster?::
+ See <<quickstart>>.
+
+Where can I learn about the rest of the configuration options?::
+ See <<configuration>>.
+
+=== Schema Design / Data Access
+
+How should I design my schema in HBase?::
+ See <<datamodel>> and <<schema>>.
+
+How can I store (fill in the blank) in HBase?::
+ See <<supported.datatypes>>.
+
+How can I handle secondary indexes in HBase?::
+ See <<secondary.indexes>>.
+
+Can I change a table's rowkeys?::
+ This is a very common question. You can't. See <<changing.rowkeys>>.
+
+What APIs does HBase support?::
+ See <<datamodel>>, <<architecture.client>>, and <<nonjava.jvm>>.
+
+=== MapReduce
+
+How can I use MapReduce with HBase?::
+ See <<mapreduce>>.
+
+=== Performance and Troubleshooting
+
+How can I improve HBase cluster performance?::
+ See <<performance>>.
+
+How can I troubleshoot my HBase cluster?::
+ See <<trouble>>.
+
+=== Amazon EC2
+
+I am running HBase on Amazon EC2 and...::
+ EC2 issues are a special case. See <<trouble.ec2>> and <<perf.ec2>>.
+
+=== Operations
+
+How do I manage my HBase cluster?::
+ See <<ops_mgt>>.
+
+How do I back up my HBase cluster?::
+ See <<ops.backup>>.
+
+=== HBase in Action
+
+Where can I find interesting videos and presentations on HBase?::
+ See <<other.info>>.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/getting_started.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/getting_started.adoc b/src/main/asciidoc/_chapters/getting_started.adoc
new file mode 100644
index 0000000..41674a0
--- /dev/null
+++ b/src/main/asciidoc/_chapters/getting_started.adoc
@@ -0,0 +1,641 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+= Getting Started
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+== Introduction
+
+<<quickstart,Quickstart>> will get you up and running on a single-node, standalone instance of HBase, followed by a pseudo-distributed single-machine instance, and finally a fully-distributed cluster.
+
+[[quickstart]]
+== Quick Start - Standalone HBase
+
+This guide describes the setup of a standalone HBase instance running against the local filesystem.
+This is not an appropriate configuration for a production instance of HBase, but will allow you to experiment with HBase.
+This section shows you how to create a table in HBase using the `hbase shell` CLI, insert rows into the table, perform put and scan operations against the table, enable or disable the table, and start and stop HBase.
+Apart from downloading HBase, this procedure should take less than 10 minutes.
+
+.Local Filesystem and Durability
+WARNING: _The following is fixed in HBase 0.98.3 and beyond. See link:https://issues.apache.org/jira/browse/HBASE-11272[HBASE-11272] and link:https://issues.apache.org/jira/browse/HBASE-11218[HBASE-11218]._
+
+Using HBase with a local filesystem does not guarantee durability.
+The HDFS local filesystem implementation will lose edits if files are not properly closed.
+This is very likely to happen when you are experimenting with new software, starting and stopping the daemons often and not always cleanly.
+You need to run HBase on HDFS to ensure all writes are preserved.
+Running against the local filesystem is intended as a shortcut to get you familiar with how the general system works, as the very first phase of evaluation.
+See link:https://issues.apache.org/jira/browse/HBASE-3696[HBASE-3696] and its associated issues for more details about the issues of running on the local filesystem.
+
+[[loopback.ip]]
+.Loopback IP - HBase 0.94.x and earlier
+NOTE: _The below advice is for hbase-0.94.x and older versions only. This is fixed in hbase-0.96.0 and beyond._
+
+Prior to HBase 0.94.x, HBase expected the loopback IP address to be 127.0.0.1. Ubuntu and some other distributions default to 127.0.1.1 and this will cause problems for you. See link:http://devving.com/?p=414[Why does HBase care about /etc/hosts?] for detail
+
+
+.Example /etc/hosts File for Ubuntu
+====
+The following _/etc/hosts_ file works correctly for HBase 0.94.x and earlier, on Ubuntu. Use this as a template if you run into trouble.
+[listing]
+----
+127.0.0.1 localhost
+127.0.0.1 ubuntu.ubuntu-domain ubuntu
+----
+
+====
+
+
+=== JDK Version Requirements
+
+HBase requires that a JDK be installed.
+See <<java,Java>> for information about supported JDK versions.
+
+=== Get Started with HBase
+
+.Procedure: Download, Configure, and Start HBase
+. Choose a download site from this list of link:http://www.apache.org/dyn/closer.cgi/hbase/[Apache Download Mirrors].
+ Click on the suggested top link.
+ This will take you to a mirror of _HBase
+ Releases_.
+ Click on the folder named _stable_ and then download the binary file that ends in _.tar.gz_ to your local filesystem.
+ Be sure to choose the version that corresponds with the version of Hadoop you are likely to use later.
+ In most cases, you should choose the file for Hadoop 2, which will be called something like _hbase-0.98.3-hadoop2-bin.tar.gz_.
+ Do not download the file ending in _src.tar.gz_ for now.
+. Extract the downloaded file, and change to the newly-created directory.
++
+----
+
+$ tar xzvf hbase-<?eval ${project.version}?>-hadoop2-bin.tar.gz
+$ cd hbase-<?eval ${project.version}?>-hadoop2/
+----
+
+. For HBase 0.98.5 and later, you are required to set the `JAVA_HOME` environment variable before starting HBase.
+ Prior to 0.98.5, HBase attempted to detect the location of Java if the variables was not set.
+ You can set the variable via your operating system's usual mechanism, but HBase provides a central mechanism, _conf/hbase-env.sh_.
+ Edit this file, uncomment the line starting with `JAVA_HOME`, and set it to the appropriate location for your operating system.
+ The `JAVA_HOME` variable should be set to a directory which contains the executable file _bin/java_.
+ Most modern Linux operating systems provide a mechanism, such as /usr/bin/alternatives on RHEL or CentOS, for transparently switching between versions of executables such as Java.
+ In this case, you can set `JAVA_HOME` to the directory containing the symbolic link to _bin/java_, which is usually _/usr_.
++
+----
+JAVA_HOME=/usr
+----
++
+NOTE: These instructions assume that each node of your cluster uses the same configuration.
+If this is not the case, you may need to set `JAVA_HOME` separately for each node.
+
+. Edit _conf/hbase-site.xml_, which is the main HBase configuration file.
+ At this time, you only need to specify the directory on the local filesystem where HBase and ZooKeeper write data.
+ By default, a new directory is created under /tmp.
+ Many servers are configured to delete the contents of _/tmp_ upon reboot, so you should store the data elsewhere.
+ The following configuration will store HBase's data in the _hbase_ directory, in the home directory of the user called `testuser`.
+ Paste the `<property>` tags beneath the `<configuration>` tags, which should be empty in a new HBase install.
++
+.Example _hbase-site.xml_ for Standalone HBase
+====
+[source,xml]
+----
+
+<configuration>
+ <property>
+ <name>hbase.rootdir</name>
+ <value>file:///home/testuser/hbase</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/home/testuser/zookeeper</value>
+ </property>
+</configuration>
+----
+====
++
+You do not need to create the HBase data directory.
+HBase will do this for you.
+If you create the directory, HBase will attempt to do a migration, which is not what you want.
+
+. The _bin/start-hbase.sh_ script is provided as a convenient way to start HBase.
+ Issue the command, and if all goes well, a message is logged to standard output showing that HBase started successfully.
+ You can use the `jps` command to verify that you have one running process called `HMaster`.
+ In standalone mode HBase runs all daemons within this single JVM, i.e.
+ the HMaster, a single HRegionServer, and the ZooKeeper daemon.
++
+NOTE: Java needs to be installed and available.
+If you get an error indicating that Java is not installed, but it is on your system, perhaps in a non-standard location, edit the _conf/hbase-env.sh_ file and modify the `JAVA_HOME` setting to point to the directory that contains _bin/java_ your system.
+
+
+[[shell_exercises]]
+.Procedure: Use HBase For the First Time
+. Connect to HBase.
++
+Connect to your running instance of HBase using the `hbase shell` command, located in the [path]_bin/_ directory of your HBase install.
+In this example, some usage and version information that is printed when you start HBase Shell has been omitted.
+The HBase Shell prompt ends with a `>` character.
++
+----
+
+$ ./bin/hbase shell
+hbase(main):001:0>
+----
+
+. Display HBase Shell Help Text.
++
+Type `help` and press Enter, to display some basic usage information for HBase Shell, as well as several example commands.
+Notice that table names, rows, columns all must be enclosed in quote characters.
+
+. Create a table.
++
+Use the `create` command to create a new table.
+You must specify the table name and the ColumnFamily name.
++
+----
+hbase(main):001:0> create 'test', 'cf'
+0 row(s) in 0.4170 seconds
+
+=> Hbase::Table - test
+----
+
+. List Information About your Table
++
+Use the `list` command to
++
+----
+hbase(main):002:0> list 'test'
+TABLE
+test
+1 row(s) in 0.0180 seconds
+
+=> ["test"]
+----
+
+. Put data into your table.
++
+To put data into your table, use the `put` command.
++
+----
+hbase(main):003:0> put 'test', 'row1', 'cf:a', 'value1'
+0 row(s) in 0.0850 seconds
+
+hbase(main):004:0> put 'test', 'row2', 'cf:b', 'value2'
+0 row(s) in 0.0110 seconds
+
+hbase(main):005:0> put 'test', 'row3', 'cf:c', 'value3'
+0 row(s) in 0.0100 seconds
+----
++
+Here, we insert three values, one at a time.
+The first insert is at `row1`, column `cf:a`, with a value of `value1`.
+Columns in HBase are comprised of a column family prefix, `cf` in this example, followed by a colon and then a column qualifier suffix, `a` in this case.
+
+. Scan the table for all data at once.
++
+One of the ways to get data from HBase is to scan.
+Use the `scan` command to scan the table for data.
+You can limit your scan, but for now, all data is fetched.
++
+----
+hbase(main):006:0> scan 'test'
+ROW COLUMN+CELL
+ row1 column=cf:a, timestamp=1421762485768, value=value1
+ row2 column=cf:b, timestamp=1421762491785, value=value2
+ row3 column=cf:c, timestamp=1421762496210, value=value3
+3 row(s) in 0.0230 seconds
+----
+
+. Get a single row of data.
++
+To get a single row of data at a time, use the `get` command.
++
+----
+hbase(main):007:0> get 'test', 'row1'
+COLUMN CELL
+ cf:a timestamp=1421762485768, value=value1
+1 row(s) in 0.0350 seconds
+----
+
+. Disable a table.
++
+If you want to delete a table or change its settings, as well as in some other situations, you need to disable the table first, using the `disable` command.
+You can re-enable it using the `enable` command.
++
+----
+hbase(main):008:0> disable 'test'
+0 row(s) in 1.1820 seconds
+
+hbase(main):009:0> enable 'test'
+0 row(s) in 0.1770 seconds
+----
++
+Disable the table again if you tested the `enable` command above:
++
+----
+hbase(main):010:0> disable 'test'
+0 row(s) in 1.1820 seconds
+----
+
+. Drop the table.
++
+To drop (delete) a table, use the `drop` command.
++
+----
+hbase(main):011:0> drop 'test'
+0 row(s) in 0.1370 seconds
+----
+
+. Exit the HBase Shell.
++
+To exit the HBase Shell and disconnect from your cluster, use the `quit` command.
+HBase is still running in the background.
+
+
+.Procedure: Stop HBase
+. In the same way that the _bin/start-hbase.sh_ script is provided to conveniently start all HBase daemons, the _bin/stop-hbase.sh_ script stops them.
++
+----
+
+$ ./bin/stop-hbase.sh
+stopping hbase....................
+$
+----
+
+. After issuing the command, it can take several minutes for the processes to shut down.
+ Use the `jps` to be sure that the HMaster and HRegionServer processes are shut down.
+
+[[quickstart_pseudo]]
+=== Intermediate - Pseudo-Distributed Local Install
+
+After working your way through <<quickstart,quickstart>>, you can re-configure HBase to run in pseudo-distributed mode.
+Pseudo-distributed mode means that HBase still runs completely on a single host, but each HBase daemon (HMaster, HRegionServer, and Zookeeper) runs as a separate process.
+By default, unless you configure the `hbase.rootdir` property as described in <<quickstart,quickstart>>, your data is still stored in _/tmp/_.
+In this walk-through, we store your data in HDFS instead, assuming you have HDFS available.
+You can skip the HDFS configuration to continue storing your data in the local filesystem.
+
+.Hadoop Configuration
+[NOTE]
+====
+This procedure assumes that you have configured Hadoop and HDFS on your local system and or a remote system, and that they are running and available.
+It also assumes you are using Hadoop 2.
+Currently, the documentation on the Hadoop website does not include a quick start for Hadoop 2, but the guide at link:http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide is a good starting point.
+====
+
+
+. Stop HBase if it is running.
++
+If you have just finished <<quickstart,quickstart>> and HBase is still running, stop it.
+This procedure will create a totally new directory where HBase will store its data, so any databases you created before will be lost.
+
+. Configure HBase.
++
+Edit the _hbase-site.xml_ configuration.
+First, add the following property.
+which directs HBase to run in distributed mode, with one JVM instance per daemon.
++
+[source,xml]
+----
+
+<property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+</property>
+----
++
+Next, change the `hbase.rootdir` from the local filesystem to the address of your HDFS instance, using the `hdfs:////` URI syntax.
+In this example, HDFS is running on the localhost at port 8020.
++
+[source,xml]
+----
+
+<property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://localhost:8020/hbase</value>
+</property>
+----
++
+You do not need to create the directory in HDFS.
+HBase will do this for you.
+If you create the directory, HBase will attempt to do a migration, which is not what you want.
+
+. Start HBase.
++
+Use the _bin/start-hbase.sh_ command to start HBase.
+If your system is configured correctly, the `jps` command should show the HMaster and HRegionServer processes running.
+
+. Check the HBase directory in HDFS.
++
+If everything worked correctly, HBase created its directory in HDFS.
+In the configuration above, it is stored in _/hbase/_ on HDFS.
+You can use the `hadoop fs` command in Hadoop's _bin/_ directory to list this directory.
++
+----
+
+$ ./bin/hadoop fs -ls /hbase
+Found 7 items
+drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/.tmp
+drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/WALs
+drwxr-xr-x - hbase users 0 2014-06-25 18:48 /hbase/corrupt
+drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/data
+-rw-r--r-- 3 hbase users 42 2014-06-25 18:41 /hbase/hbase.id
+-rw-r--r-- 3 hbase users 7 2014-06-25 18:41 /hbase/hbase.version
+drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/oldWALs
+----
+
+. Create a table and populate it with data.
++
+You can use the HBase Shell to create a table, populate it with data, scan and get values from it, using the same procedure as in <<shell_exercises,shell exercises>>.
+
+. Start and stop a backup HBase Master (HMaster) server.
++
+NOTE: Running multiple HMaster instances on the same hardware does not make sense in a production environment, in the same way that running a pseudo-distributed cluster does not make sense for production.
+This step is offered for testing and learning purposes only.
++
+The HMaster server controls the HBase cluster.
+You can start up to 9 backup HMaster servers, which makes 10 total HMasters, counting the primary.
+To start a backup HMaster, use the `local-master-backup.sh`.
+For each backup master you want to start, add a parameter representing the port offset for that master.
+Each HMaster uses three ports (16010, 16020, and 16030 by default). The port offset is added to these ports, so using an offset of 2, the backup HMaster would use ports 16012, 16022, and 16032.
+The following command starts 3 backup servers using ports 16012/16022/16032, 16013/16023/16033, and 16015/16025/16035.
++
+----
+
+$ ./bin/local-master-backup.sh 2 3 5
+----
++
+To kill a backup master without killing the entire cluster, you need to find its process ID (PID). The PID is stored in a file with a name like _/tmp/hbase-USER-X-master.pid_.
+The only contents of the file is the PID.
+You can use the `kill -9` command to kill that PID.
+The following command will kill the master with port offset 1, but leave the cluster running:
++
+----
+
+$ cat /tmp/hbase-testuser-1-master.pid |xargs kill -9
+----
+
+. Start and stop additional RegionServers
++
+The HRegionServer manages the data in its StoreFiles as directed by the HMaster.
+Generally, one HRegionServer runs per node in the cluster.
+Running multiple HRegionServers on the same system can be useful for testing in pseudo-distributed mode.
+The `local-regionservers.sh` command allows you to run multiple RegionServers.
+It works in a similar way to the `local-master-backup.sh` command, in that each parameter you provide represents the port offset for an instance.
+Each RegionServer requires two ports, and the default ports are 16020 and 16030.
+However, the base ports for additional RegionServers are not the default ports since the default ports are used by the HMaster, which is also a RegionServer since HBase version 1.0.0.
+The base ports are 16200 and 16300 instead.
+You can run 99 additional RegionServers that are not a HMaster or backup HMaster, on a server.
+The following command starts four additional RegionServers, running on sequential ports starting at 16202/16302 (base ports 16200/16300 plus 2).
++
+----
+
+$ .bin/local-regionservers.sh start 2 3 4 5
+----
++
+To stop a RegionServer manually, use the `local-regionservers.sh` command with the `stop` parameter and the offset of the server to stop.
++
+----
+$ .bin/local-regionservers.sh stop 3
+----
+
+. Stop HBase.
++
+You can stop HBase the same way as in the <<quickstart,quickstart>> procedure, using the _bin/stop-hbase.sh_ command.
+
+
+[[quickstart_fully_distributed]]
+=== Advanced - Fully Distributed
+
+In reality, you need a fully-distributed configuration to fully test HBase and to use it in real-world scenarios.
+In a distributed configuration, the cluster contains multiple nodes, each of which runs one or more HBase daemon.
+These include primary and backup Master instances, multiple Zookeeper nodes, and multiple RegionServer nodes.
+
+This advanced quickstart adds two more nodes to your cluster.
+The architecture will be as follows:
+
+.Distributed Cluster Demo Architecture
+[cols="1,1,1,1", options="header"]
+|===
+| Node Name | Master | ZooKeeper | RegionServer
+| node-a.example.com | yes | yes | no
+| node-b.example.com | backup | yes | yes
+| node-c.example.com | no | yes | yes
+|===
+
+This quickstart assumes that each node is a virtual machine and that they are all on the same network.
+It builds upon the previous quickstart, <<quickstart_pseudo>>, assuming that the system you configured in that procedure is now `node-a`.
+Stop HBase on `node-a` before continuing.
+
+NOTE: Be sure that all the nodes have full access to communicate, and that no firewall rules are in place which could prevent them from talking to each other.
+If you see any errors like `no route to host`, check your firewall.
+
+[[passwordless.ssh.quickstart]]
+.Procedure: Configure Passwordless SSH Access
+
+`node-a` needs to be able to log into `node-b` and `node-c` (and to itself) in order to start the daemons.
+The easiest way to accomplish this is to use the same username on all hosts, and configure password-less SSH login from `node-a` to each of the others.
+
+. On `node-a`, generate a key pair.
++
+While logged in as the user who will run HBase, generate a SSH key pair, using the following command:
++
+[source,bash]
+----
+$ ssh-keygen -t rsa
+----
++
+If the command succeeds, the location of the key pair is printed to standard output.
+The default name of the public key is _id_rsa.pub_.
+
+. Create the directory that will hold the shared keys on the other nodes.
++
+On `node-b` and `node-c`, log in as the HBase user and create a _.ssh/_ directory in the user's home directory, if it does not already exist.
+If it already exists, be aware that it may already contain other keys.
+
+. Copy the public key to the other nodes.
++
+Securely copy the public key from `node-a` to each of the nodes, by using the `scp` or some other secure means.
+On each of the other nodes, create a new file called _.ssh/authorized_keys_ _if it does
+not already exist_, and append the contents of the _id_rsa.pub_ file to the end of it.
+Note that you also need to do this for `node-a` itself.
++
+----
+$ cat id_rsa.pub >> ~/.ssh/authorized_keys
+----
+
+. Test password-less login.
++
+If you performed the procedure correctly, if you SSH from `node-a` to either of the other nodes, using the same username, you should not be prompted for a password.
+
+. Since `node-b` will run a backup Master, repeat the procedure above, substituting `node-b` everywhere you see `node-a`.
+ Be sure not to overwrite your existing _.ssh/authorized_keys_ files, but concatenate the new key onto the existing file using the `>>` operator rather than the `>` operator.
+
+.Procedure: Prepare `node-a`
+
+`node-a` will run your primary master and ZooKeeper processes, but no RegionServers.
+. Stop the RegionServer from starting on `node-a`.
+
+. Edit _conf/regionservers_ and remove the line which contains `localhost`. Add lines with the hostnames or IP addresses for `node-b` and `node-c`.
++
+Even if you did want to run a RegionServer on `node-a`, you should refer to it by the hostname the other servers would use to communicate with it.
+In this case, that would be `node-a.example.com`.
+This enables you to distribute the configuration to each node of your cluster any hostname conflicts.
+Save the file.
+
+. Configure HBase to use `node-b` as a backup master.
++
+Create a new file in _conf/_ called _backup-masters_, and add a new line to it with the hostname for `node-b`.
+In this demonstration, the hostname is `node-b.example.com`.
+
+. Configure ZooKeeper
++
+In reality, you should carefully consider your ZooKeeper configuration.
+You can find out more about configuring ZooKeeper in <<zookeeper,zookeeper>>.
+This configuration will direct HBase to start and manage a ZooKeeper instance on each node of the cluster.
++
+On `node-a`, edit _conf/hbase-site.xml_ and add the following properties.
++
+[source,xml]
+----
+<property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
+</property>
+<property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/usr/local/zookeeper</value>
+</property>
+----
+
+. Everywhere in your configuration that you have referred to `node-a` as `localhost`, change the reference to point to the hostname that the other nodes will use to refer to `node-a`.
+ In these examples, the hostname is `node-a.example.com`.
+
+.Procedure: Prepare `node-b` and `node-c`
+
+`node-b` will run a backup master server and a ZooKeeper instance.
+
+. Download and unpack HBase.
++
+Download and unpack HBase to `node-b`, just as you did for the standalone and pseudo-distributed quickstarts.
+
+. Copy the configuration files from `node-a` to `node-b`.and `node-c`.
++
+Each node of your cluster needs to have the same configuration information.
+Copy the contents of the _conf/_ directory to the _conf/_ directory on `node-b` and `node-c`.
+
+
+.Procedure: Start and Test Your Cluster
+. Be sure HBase is not running on any node.
++
+If you forgot to stop HBase from previous testing, you will have errors.
+Check to see whether HBase is running on any of your nodes by using the `jps` command.
+Look for the processes `HMaster`, `HRegionServer`, and `HQuorumPeer`.
+If they exist, kill them.
+
+. Start the cluster.
++
+On `node-a`, issue the `start-hbase.sh` command.
+Your output will be similar to that below.
++
+----
+
+$ bin/start-hbase.sh
+node-c.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-c.example.com.out
+node-a.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-a.example.com.out
+node-b.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-b.example.com.out
+starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-node-a.example.com.out
+node-c.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-c.example.com.out
+node-b.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-b.example.com.out
+node-b.example.com: starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-nodeb.example.com.out
+----
++
+ZooKeeper starts first, followed by the master, then the RegionServers, and finally the backup masters.
+
+. Verify that the processes are running.
++
+On each node of the cluster, run the `jps` command and verify that the correct processes are running on each server.
+You may see additional Java processes running on your servers as well, if they are used for other purposes.
++
+.`node-a` `jps` Output
+====
+----
+
+$ jps
+20355 Jps
+20071 HQuorumPeer
+20137 HMaster
+----
+====
++
+.`node-b` `jps` Output
+====
+----
+$ jps
+15930 HRegionServer
+16194 Jps
+15838 HQuorumPeer
+16010 HMaster
+----
+====
++
+.`node-a` `jps` Output
+====
+----
+$ jps
+13901 Jps
+13639 HQuorumPeer
+13737 HRegionServer
+----
+====
++
+.ZooKeeper Process Name
+[NOTE]
+====
+The `HQuorumPeer` process is a ZooKeeper instance which is controlled and started by HBase.
+If you use ZooKeeper this way, it is limited to one instance per cluster node, , and is appropriate for testing only.
+If ZooKeeper is run outside of HBase, the process is called `QuorumPeer`.
+For more about ZooKeeper configuration, including using an external ZooKeeper instance with HBase, see <<zookeeper,zookeeper>>.
+====
+
+. Browse to the Web UI.
++
+.Web UI Port Changes
+NOTE: Web UI Port Changes
++
+In HBase newer than 0.98.x, the HTTP ports used by the HBase Web UI changed from 60010 for the
+Master and 60030 for each RegionServer to 16010 for the Master and 16030 for the RegionServer.
+
++
+If everything is set up correctly, you should be able to connect to the UI for the Master
+`http://node-a.example.com:16010/` or the secondary master at `http://node-b.example.com:16010/`
+for the secondary master, using a web browser.
+If you can connect via `localhost` but not from another host, check your firewall rules.
+You can see the web UI for each of the RegionServers at port 16030 of their IP addresses, or by
+clicking their links in the web UI for the Master.
+
+. Test what happens when nodes or services disappear.
++
+With a three-node cluster like you have configured, things will not be very resilient.
+Still, you can test what happens when the primary Master or a RegionServer disappears, by killing the processes and watching the logs.
+
+
+=== Where to go next
+
+The next chapter, <<configuration,configuration>>, gives more information about the different HBase run modes, system requirements for running HBase, and critical configuration areas for setting up a distributed HBase cluster.
[11/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/hbase-default.adoc
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diff --git a/src/main/asciidoc/_chapters/hbase-default.adoc b/src/main/asciidoc/_chapters/hbase-default.adoc
new file mode 100644
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--- /dev/null
+++ b/src/main/asciidoc/_chapters/hbase-default.adoc
@@ -0,0 +1,2254 @@
+
+
+
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[hbase_default_configurations]]
+=== HBase Default Configuration
+
+The documentation below is generated using the default hbase configuration file, _hbase-default.xml_, as source.
+
+
+[[hbase.tmp.dir]]
+*`hbase.tmp.dir`*::
++
+.Description
+Temporary directory on the local filesystem.
+ Change this setting to point to a location more permanent
+ than '/tmp', the usual resolve for java.io.tmpdir, as the
+ '/tmp' directory is cleared on machine restart.
++
+.Default
+`${java.io.tmpdir}/hbase-${user.name}`
+
+
+[[hbase.rootdir]]
+*`hbase.rootdir`*::
++
+.Description
+The directory shared by region servers and into
+ which HBase persists. The URL should be 'fully-qualified'
+ to include the filesystem scheme. For example, to specify the
+ HDFS directory '/hbase' where the HDFS instance's namenode is
+ running at namenode.example.org on port 9000, set this value to:
+ hdfs://namenode.example.org:9000/hbase. By default, we write
+ to whatever ${hbase.tmp.dir} is set too -- usually /tmp --
+ so change this configuration or else all data will be lost on
+ machine restart.
++
+.Default
+`${hbase.tmp.dir}/hbase`
+
+
+[[hbase.cluster.distributed]]
+*`hbase.cluster.distributed`*::
++
+.Description
+The mode the cluster will be in. Possible values are
+ false for standalone mode and true for distributed mode. If
+ false, startup will run all HBase and ZooKeeper daemons together
+ in the one JVM.
++
+.Default
+`false`
+
+
+[[hbase.zookeeper.quorum]]
+*`hbase.zookeeper.quorum`*::
++
+.Description
+Comma separated list of servers in the ZooKeeper ensemble
+ (This config. should have been named hbase.zookeeper.ensemble).
+ For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
+ By default this is set to localhost for local and pseudo-distributed modes
+ of operation. For a fully-distributed setup, this should be set to a full
+ list of ZooKeeper ensemble servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
+ this is the list of servers which hbase will start/stop ZooKeeper on as
+ part of cluster start/stop. Client-side, we will take this list of
+ ensemble members and put it together with the hbase.zookeeper.clientPort
+ config. and pass it into zookeeper constructor as the connectString
+ parameter.
++
+.Default
+`localhost`
+
+
+[[hbase.local.dir]]
+*`hbase.local.dir`*::
++
+.Description
+Directory on the local filesystem to be used
+ as a local storage.
++
+.Default
+`${hbase.tmp.dir}/local/`
+
+
+[[hbase.master.info.port]]
+*`hbase.master.info.port`*::
++
+.Description
+The port for the HBase Master web UI.
+ Set to -1 if you do not want a UI instance run.
++
+.Default
+`16010`
+
+
+[[hbase.master.info.bindAddress]]
+*`hbase.master.info.bindAddress`*::
++
+.Description
+The bind address for the HBase Master web UI
+
++
+.Default
+`0.0.0.0`
+
+
+[[hbase.master.logcleaner.plugins]]
+*`hbase.master.logcleaner.plugins`*::
++
+.Description
+A comma-separated list of BaseLogCleanerDelegate invoked by
+ the LogsCleaner service. These WAL cleaners are called in order,
+ so put the cleaner that prunes the most files in front. To
+ implement your own BaseLogCleanerDelegate, just put it in HBase's classpath
+ and add the fully qualified class name here. Always add the above
+ default log cleaners in the list.
++
+.Default
+`org.apache.hadoop.hbase.master.cleaner.TimeToLiveLogCleaner`
+
+
+[[hbase.master.logcleaner.ttl]]
+*`hbase.master.logcleaner.ttl`*::
++
+.Description
+Maximum time a WAL can stay in the .oldlogdir directory,
+ after which it will be cleaned by a Master thread.
++
+.Default
+`600000`
+
+
+[[hbase.master.hfilecleaner.plugins]]
+*`hbase.master.hfilecleaner.plugins`*::
++
+.Description
+A comma-separated list of BaseHFileCleanerDelegate invoked by
+ the HFileCleaner service. These HFiles cleaners are called in order,
+ so put the cleaner that prunes the most files in front. To
+ implement your own BaseHFileCleanerDelegate, just put it in HBase's classpath
+ and add the fully qualified class name here. Always add the above
+ default log cleaners in the list as they will be overwritten in
+ hbase-site.xml.
++
+.Default
+`org.apache.hadoop.hbase.master.cleaner.TimeToLiveHFileCleaner`
+
+
+[[hbase.master.catalog.timeout]]
+*`hbase.master.catalog.timeout`*::
++
+.Description
+Timeout value for the Catalog Janitor from the master to
+ META.
++
+.Default
+`600000`
+
+
+[[hbase.master.infoserver.redirect]]
+*`hbase.master.infoserver.redirect`*::
++
+.Description
+Whether or not the Master listens to the Master web
+ UI port (hbase.master.info.port) and redirects requests to the web
+ UI server shared by the Master and RegionServer.
++
+.Default
+`true`
+
+
+[[hbase.regionserver.port]]
+*`hbase.regionserver.port`*::
++
+.Description
+The port the HBase RegionServer binds to.
++
+.Default
+`16020`
+
+
+[[hbase.regionserver.info.port]]
+*`hbase.regionserver.info.port`*::
++
+.Description
+The port for the HBase RegionServer web UI
+ Set to -1 if you do not want the RegionServer UI to run.
++
+.Default
+`16030`
+
+
+[[hbase.regionserver.info.bindAddress]]
+*`hbase.regionserver.info.bindAddress`*::
++
+.Description
+The address for the HBase RegionServer web UI
++
+.Default
+`0.0.0.0`
+
+
+[[hbase.regionserver.info.port.auto]]
+*`hbase.regionserver.info.port.auto`*::
++
+.Description
+Whether or not the Master or RegionServer
+ UI should search for a port to bind to. Enables automatic port
+ search if hbase.regionserver.info.port is already in use.
+ Useful for testing, turned off by default.
++
+.Default
+`false`
+
+
+[[hbase.regionserver.handler.count]]
+*`hbase.regionserver.handler.count`*::
++
+.Description
+Count of RPC Listener instances spun up on RegionServers.
+ Same property is used by the Master for count of master handlers.
++
+.Default
+`30`
+
+
+[[hbase.ipc.server.callqueue.handler.factor]]
+*`hbase.ipc.server.callqueue.handler.factor`*::
++
+.Description
+Factor to determine the number of call queues.
+ A value of 0 means a single queue shared between all the handlers.
+ A value of 1 means that each handler has its own queue.
++
+.Default
+`0.1`
+
+
+[[hbase.ipc.server.callqueue.read.ratio]]
+*`hbase.ipc.server.callqueue.read.ratio`*::
++
+.Description
+Split the call queues into read and write queues.
+ The specified interval (which should be between 0.0 and 1.0)
+ will be multiplied by the number of call queues.
+ A value of 0 indicate to not split the call queues, meaning that both read and write
+ requests will be pushed to the same set of queues.
+ A value lower than 0.5 means that there will be less read queues than write queues.
+ A value of 0.5 means there will be the same number of read and write queues.
+ A value greater than 0.5 means that there will be more read queues than write queues.
+ A value of 1.0 means that all the queues except one are used to dispatch read requests.
+
+ Example: Given the total number of call queues being 10
+ a read.ratio of 0 means that: the 10 queues will contain both read/write requests.
+ a read.ratio of 0.3 means that: 3 queues will contain only read requests
+ and 7 queues will contain only write requests.
+ a read.ratio of 0.5 means that: 5 queues will contain only read requests
+ and 5 queues will contain only write requests.
+ a read.ratio of 0.8 means that: 8 queues will contain only read requests
+ and 2 queues will contain only write requests.
+ a read.ratio of 1 means that: 9 queues will contain only read requests
+ and 1 queues will contain only write requests.
+
++
+.Default
+`0`
+
+
+[[hbase.ipc.server.callqueue.scan.ratio]]
+*`hbase.ipc.server.callqueue.scan.ratio`*::
++
+.Description
+Given the number of read call queues, calculated from the total number
+ of call queues multiplied by the callqueue.read.ratio, the scan.ratio property
+ will split the read call queues into small-read and long-read queues.
+ A value lower than 0.5 means that there will be less long-read queues than short-read queues.
+ A value of 0.5 means that there will be the same number of short-read and long-read queues.
+ A value greater than 0.5 means that there will be more long-read queues than short-read queues
+ A value of 0 or 1 indicate to use the same set of queues for gets and scans.
+
+ Example: Given the total number of read call queues being 8
+ a scan.ratio of 0 or 1 means that: 8 queues will contain both long and short read requests.
+ a scan.ratio of 0.3 means that: 2 queues will contain only long-read requests
+ and 6 queues will contain only short-read requests.
+ a scan.ratio of 0.5 means that: 4 queues will contain only long-read requests
+ and 4 queues will contain only short-read requests.
+ a scan.ratio of 0.8 means that: 6 queues will contain only long-read requests
+ and 2 queues will contain only short-read requests.
+
++
+.Default
+`0`
+
+
+[[hbase.regionserver.msginterval]]
+*`hbase.regionserver.msginterval`*::
++
+.Description
+Interval between messages from the RegionServer to Master
+ in milliseconds.
++
+.Default
+`3000`
+
+
+[[hbase.regionserver.regionSplitLimit]]
+*`hbase.regionserver.regionSplitLimit`*::
++
+.Description
+Limit for the number of regions after which no more region
+ splitting should take place. This is not a hard limit for the number of
+ regions but acts as a guideline for the regionserver to stop splitting after
+ a certain limit. Default is MAX_INT; i.e. do not block splitting.
++
+.Default
+`2147483647`
+
+
+[[hbase.regionserver.logroll.period]]
+*`hbase.regionserver.logroll.period`*::
++
+.Description
+Period at which we will roll the commit log regardless
+ of how many edits it has.
++
+.Default
+`3600000`
+
+
+[[hbase.regionserver.logroll.errors.tolerated]]
+*`hbase.regionserver.logroll.errors.tolerated`*::
++
+.Description
+The number of consecutive WAL close errors we will allow
+ before triggering a server abort. A setting of 0 will cause the
+ region server to abort if closing the current WAL writer fails during
+ log rolling. Even a small value (2 or 3) will allow a region server
+ to ride over transient HDFS errors.
++
+.Default
+`2`
+
+
+[[hbase.regionserver.hlog.reader.impl]]
+*`hbase.regionserver.hlog.reader.impl`*::
++
+.Description
+The WAL file reader implementation.
++
+.Default
+`org.apache.hadoop.hbase.regionserver.wal.ProtobufLogReader`
+
+
+[[hbase.regionserver.hlog.writer.impl]]
+*`hbase.regionserver.hlog.writer.impl`*::
++
+.Description
+The WAL file writer implementation.
++
+.Default
+`org.apache.hadoop.hbase.regionserver.wal.ProtobufLogWriter`
+
+
+[[hbase.master.distributed.log.replay]]
+*`hbase.master.distributed.log.replay`*::
++
+.Description
+Enable 'distributed log replay' as default engine splitting
+ WAL files on server crash. This default is new in hbase 1.0. To fall
+ back to the old mode 'distributed log splitter', set the value to
+ 'false'. 'Disributed log replay' improves MTTR because it does not
+ write intermediate files. 'DLR' required that 'hfile.format.version'
+ be set to version 3 or higher.
+
++
+.Default
+`true`
+
+
+[[hbase.regionserver.global.memstore.size]]
+*`hbase.regionserver.global.memstore.size`*::
++
+.Description
+Maximum size of all memstores in a region server before new
+ updates are blocked and flushes are forced. Defaults to 40% of heap.
+ Updates are blocked and flushes are forced until size of all memstores
+ in a region server hits hbase.regionserver.global.memstore.size.lower.limit.
++
+.Default
+`0.4`
+
+
+[[hbase.regionserver.global.memstore.size.lower.limit]]
+*`hbase.regionserver.global.memstore.size.lower.limit`*::
++
+.Description
+Maximum size of all memstores in a region server before flushes are forced.
+ Defaults to 95% of hbase.regionserver.global.memstore.size.
+ A 100% value for this value causes the minimum possible flushing to occur when updates are
+ blocked due to memstore limiting.
++
+.Default
+`0.95`
+
+
+[[hbase.regionserver.optionalcacheflushinterval]]
+*`hbase.regionserver.optionalcacheflushinterval`*::
++
+.Description
+
+ Maximum amount of time an edit lives in memory before being automatically flushed.
+ Default 1 hour. Set it to 0 to disable automatic flushing.
++
+.Default
+`3600000`
+
+
+[[hbase.regionserver.catalog.timeout]]
+*`hbase.regionserver.catalog.timeout`*::
++
+.Description
+Timeout value for the Catalog Janitor from the regionserver to META.
++
+.Default
+`600000`
+
+
+[[hbase.regionserver.dns.interface]]
+*`hbase.regionserver.dns.interface`*::
++
+.Description
+The name of the Network Interface from which a region server
+ should report its IP address.
++
+.Default
+`default`
+
+
+[[hbase.regionserver.dns.nameserver]]
+*`hbase.regionserver.dns.nameserver`*::
++
+.Description
+The host name or IP address of the name server (DNS)
+ which a region server should use to determine the host name used by the
+ master for communication and display purposes.
++
+.Default
+`default`
+
+
+[[hbase.regionserver.region.split.policy]]
+*`hbase.regionserver.region.split.policy`*::
++
+.Description
+
+ A split policy determines when a region should be split. The various other split policies that
+ are available currently are ConstantSizeRegionSplitPolicy, DisabledRegionSplitPolicy,
+ DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy etc.
+
++
+.Default
+`org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy`
+
+
+[[zookeeper.session.timeout]]
+*`zookeeper.session.timeout`*::
++
+.Description
+ZooKeeper session timeout in milliseconds. It is used in two different ways.
+ First, this value is used in the ZK client that HBase uses to connect to the ensemble.
+ It is also used by HBase when it starts a ZK server and it is passed as the 'maxSessionTimeout'. See
+ http://hadoop.apache.org/zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions.
+ For example, if a HBase region server connects to a ZK ensemble that's also managed by HBase, then the
+ session timeout will be the one specified by this configuration. But, a region server that connects
+ to an ensemble managed with a different configuration will be subjected that ensemble's maxSessionTimeout. So,
+ even though HBase might propose using 90 seconds, the ensemble can have a max timeout lower than this and
+ it will take precedence. The current default that ZK ships with is 40 seconds, which is lower than HBase's.
+
++
+.Default
+`90000`
+
+
+[[zookeeper.znode.parent]]
+*`zookeeper.znode.parent`*::
++
+.Description
+Root ZNode for HBase in ZooKeeper. All of HBase's ZooKeeper
+ files that are configured with a relative path will go under this node.
+ By default, all of HBase's ZooKeeper file path are configured with a
+ relative path, so they will all go under this directory unless changed.
++
+.Default
+`/hbase`
+
+
+[[zookeeper.znode.rootserver]]
+*`zookeeper.znode.rootserver`*::
++
+.Description
+Path to ZNode holding root region location. This is written by
+ the master and read by clients and region servers. If a relative path is
+ given, the parent folder will be ${zookeeper.znode.parent}. By default,
+ this means the root location is stored at /hbase/root-region-server.
++
+.Default
+`root-region-server`
+
+
+[[zookeeper.znode.acl.parent]]
+*`zookeeper.znode.acl.parent`*::
++
+.Description
+Root ZNode for access control lists.
++
+.Default
+`acl`
+
+
+[[hbase.zookeeper.dns.interface]]
+*`hbase.zookeeper.dns.interface`*::
++
+.Description
+The name of the Network Interface from which a ZooKeeper server
+ should report its IP address.
++
+.Default
+`default`
+
+
+[[hbase.zookeeper.dns.nameserver]]
+*`hbase.zookeeper.dns.nameserver`*::
++
+.Description
+The host name or IP address of the name server (DNS)
+ which a ZooKeeper server should use to determine the host name used by the
+ master for communication and display purposes.
++
+.Default
+`default`
+
+
+[[hbase.zookeeper.peerport]]
+*`hbase.zookeeper.peerport`*::
++
+.Description
+Port used by ZooKeeper peers to talk to each other.
+ See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper
+ for more information.
++
+.Default
+`2888`
+
+
+[[hbase.zookeeper.leaderport]]
+*`hbase.zookeeper.leaderport`*::
++
+.Description
+Port used by ZooKeeper for leader election.
+ See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper
+ for more information.
++
+.Default
+`3888`
+
+
+[[hbase.zookeeper.useMulti]]
+*`hbase.zookeeper.useMulti`*::
++
+.Description
+Instructs HBase to make use of ZooKeeper's multi-update functionality.
+ This allows certain ZooKeeper operations to complete more quickly and prevents some issues
+ with rare Replication failure scenarios (see the release note of HBASE-2611 for an example).
+ IMPORTANT: only set this to true if all ZooKeeper servers in the cluster are on version 3.4+
+ and will not be downgraded. ZooKeeper versions before 3.4 do not support multi-update and
+ will not fail gracefully if multi-update is invoked (see ZOOKEEPER-1495).
++
+.Default
+`true`
+
+
+[[hbase.config.read.zookeeper.config]]
+*`hbase.config.read.zookeeper.config`*::
++
+.Description
+
+ Set to true to allow HBaseConfiguration to read the
+ zoo.cfg file for ZooKeeper properties. Switching this to true
+ is not recommended, since the functionality of reading ZK
+ properties from a zoo.cfg file has been deprecated.
++
+.Default
+`false`
+
+
+[[hbase.zookeeper.property.initLimit]]
+*`hbase.zookeeper.property.initLimit`*::
++
+.Description
+Property from ZooKeeper's config zoo.cfg.
+ The number of ticks that the initial synchronization phase can take.
++
+.Default
+`10`
+
+
+[[hbase.zookeeper.property.syncLimit]]
+*`hbase.zookeeper.property.syncLimit`*::
++
+.Description
+Property from ZooKeeper's config zoo.cfg.
+ The number of ticks that can pass between sending a request and getting an
+ acknowledgment.
++
+.Default
+`5`
+
+
+[[hbase.zookeeper.property.dataDir]]
+*`hbase.zookeeper.property.dataDir`*::
++
+.Description
+Property from ZooKeeper's config zoo.cfg.
+ The directory where the snapshot is stored.
++
+.Default
+`${hbase.tmp.dir}/zookeeper`
+
+
+[[hbase.zookeeper.property.clientPort]]
+*`hbase.zookeeper.property.clientPort`*::
++
+.Description
+Property from ZooKeeper's config zoo.cfg.
+ The port at which the clients will connect.
++
+.Default
+`2181`
+
+
+[[hbase.zookeeper.property.maxClientCnxns]]
+*`hbase.zookeeper.property.maxClientCnxns`*::
++
+.Description
+Property from ZooKeeper's config zoo.cfg.
+ Limit on number of concurrent connections (at the socket level) that a
+ single client, identified by IP address, may make to a single member of
+ the ZooKeeper ensemble. Set high to avoid zk connection issues running
+ standalone and pseudo-distributed.
++
+.Default
+`300`
+
+
+[[hbase.client.write.buffer]]
+*`hbase.client.write.buffer`*::
++
+.Description
+Default size of the HTable client write buffer in bytes.
+ A bigger buffer takes more memory -- on both the client and server
+ side since server instantiates the passed write buffer to process
+ it -- but a larger buffer size reduces the number of RPCs made.
+ For an estimate of server-side memory-used, evaluate
+ hbase.client.write.buffer * hbase.regionserver.handler.count
++
+.Default
+`2097152`
+
+
+[[hbase.client.pause]]
+*`hbase.client.pause`*::
++
+.Description
+General client pause value. Used mostly as value to wait
+ before running a retry of a failed get, region lookup, etc.
+ See hbase.client.retries.number for description of how we backoff from
+ this initial pause amount and how this pause works w/ retries.
++
+.Default
+`100`
+
+
+[[hbase.client.retries.number]]
+*`hbase.client.retries.number`*::
++
+.Description
+Maximum retries. Used as maximum for all retryable
+ operations such as the getting of a cell's value, starting a row update,
+ etc. Retry interval is a rough function based on hbase.client.pause. At
+ first we retry at this interval but then with backoff, we pretty quickly reach
+ retrying every ten seconds. See HConstants#RETRY_BACKOFF for how the backup
+ ramps up. Change this setting and hbase.client.pause to suit your workload.
++
+.Default
+`35`
+
+
+[[hbase.client.max.total.tasks]]
+*`hbase.client.max.total.tasks`*::
++
+.Description
+The maximum number of concurrent tasks a single HTable instance will
+ send to the cluster.
++
+.Default
+`100`
+
+
+[[hbase.client.max.perserver.tasks]]
+*`hbase.client.max.perserver.tasks`*::
++
+.Description
+The maximum number of concurrent tasks a single HTable instance will
+ send to a single region server.
++
+.Default
+`5`
+
+
+[[hbase.client.max.perregion.tasks]]
+*`hbase.client.max.perregion.tasks`*::
++
+.Description
+The maximum number of concurrent connections the client will
+ maintain to a single Region. That is, if there is already
+ hbase.client.max.perregion.tasks writes in progress for this region, new puts
+ won't be sent to this region until some writes finishes.
++
+.Default
+`1`
+
+
+[[hbase.client.scanner.caching]]
+*`hbase.client.scanner.caching`*::
++
+.Description
+Number of rows that will be fetched when calling next
+ on a scanner if it is not served from (local, client) memory. Higher
+ caching values will enable faster scanners but will eat up more memory
+ and some calls of next may take longer and longer times when the cache is empty.
+ Do not set this value such that the time between invocations is greater
+ than the scanner timeout; i.e. hbase.client.scanner.timeout.period
++
+.Default
+`100`
+
+
+[[hbase.client.keyvalue.maxsize]]
+*`hbase.client.keyvalue.maxsize`*::
++
+.Description
+Specifies the combined maximum allowed size of a KeyValue
+ instance. This is to set an upper boundary for a single entry saved in a
+ storage file. Since they cannot be split it helps avoiding that a region
+ cannot be split any further because the data is too large. It seems wise
+ to set this to a fraction of the maximum region size. Setting it to zero
+ or less disables the check.
++
+.Default
+`10485760`
+
+
+[[hbase.client.scanner.timeout.period]]
+*`hbase.client.scanner.timeout.period`*::
++
+.Description
+Client scanner lease period in milliseconds.
++
+.Default
+`60000`
+
+
+[[hbase.client.localityCheck.threadPoolSize]]
+*`hbase.client.localityCheck.threadPoolSize`*::
++
+.Description
+
++
+.Default
+`2`
+
+
+[[hbase.bulkload.retries.number]]
+*`hbase.bulkload.retries.number`*::
++
+.Description
+Maximum retries. This is maximum number of iterations
+ to atomic bulk loads are attempted in the face of splitting operations
+ 0 means never give up.
++
+.Default
+`10`
+
+
+[[hbase.balancer.period
+ ]]
+*`hbase.balancer.period
+ `*::
++
+.Description
+Period at which the region balancer runs in the Master.
++
+.Default
+`300000`
+
+
+[[hbase.regions.slop]]
+*`hbase.regions.slop`*::
++
+.Description
+Rebalance if any regionserver has average + (average * slop) regions.
++
+.Default
+`0.2`
+
+
+[[hbase.server.thread.wakefrequency]]
+*`hbase.server.thread.wakefrequency`*::
++
+.Description
+Time to sleep in between searches for work (in milliseconds).
+ Used as sleep interval by service threads such as log roller.
++
+.Default
+`10000`
+
+
+[[hbase.server.versionfile.writeattempts]]
+*`hbase.server.versionfile.writeattempts`*::
++
+.Description
+
+ How many time to retry attempting to write a version file
+ before just aborting. Each attempt is seperated by the
+ hbase.server.thread.wakefrequency milliseconds.
++
+.Default
+`3`
+
+
+[[hbase.hregion.memstore.flush.size]]
+*`hbase.hregion.memstore.flush.size`*::
++
+.Description
+
+ Memstore will be flushed to disk if size of the memstore
+ exceeds this number of bytes. Value is checked by a thread that runs
+ every hbase.server.thread.wakefrequency.
++
+.Default
+`134217728`
+
+
+[[hbase.hregion.percolumnfamilyflush.size.lower.bound]]
+*`hbase.hregion.percolumnfamilyflush.size.lower.bound`*::
++
+.Description
+
+ If FlushLargeStoresPolicy is used, then every time that we hit the
+ total memstore limit, we find out all the column families whose memstores
+ exceed this value, and only flush them, while retaining the others whose
+ memstores are lower than this limit. If none of the families have their
+ memstore size more than this, all the memstores will be flushed
+ (just as usual). This value should be less than half of the total memstore
+ threshold (hbase.hregion.memstore.flush.size).
+
++
+.Default
+`16777216`
+
+
+[[hbase.hregion.preclose.flush.size]]
+*`hbase.hregion.preclose.flush.size`*::
++
+.Description
+
+ If the memstores in a region are this size or larger when we go
+ to close, run a "pre-flush" to clear out memstores before we put up
+ the region closed flag and take the region offline. On close,
+ a flush is run under the close flag to empty memory. During
+ this time the region is offline and we are not taking on any writes.
+ If the memstore content is large, this flush could take a long time to
+ complete. The preflush is meant to clean out the bulk of the memstore
+ before putting up the close flag and taking the region offline so the
+ flush that runs under the close flag has little to do.
++
+.Default
+`5242880`
+
+
+[[hbase.hregion.memstore.block.multiplier]]
+*`hbase.hregion.memstore.block.multiplier`*::
++
+.Description
+
+ Block updates if memstore has hbase.hregion.memstore.block.multiplier
+ times hbase.hregion.memstore.flush.size bytes. Useful preventing
+ runaway memstore during spikes in update traffic. Without an
+ upper-bound, memstore fills such that when it flushes the
+ resultant flush files take a long time to compact or split, or
+ worse, we OOME.
++
+.Default
+`4`
+
+
+[[hbase.hregion.memstore.mslab.enabled]]
+*`hbase.hregion.memstore.mslab.enabled`*::
++
+.Description
+
+ Enables the MemStore-Local Allocation Buffer,
+ a feature which works to prevent heap fragmentation under
+ heavy write loads. This can reduce the frequency of stop-the-world
+ GC pauses on large heaps.
++
+.Default
+`true`
+
+
+[[hbase.hregion.max.filesize]]
+*`hbase.hregion.max.filesize`*::
++
+.Description
+
+ Maximum HFile size. If the sum of the sizes of a region's HFiles has grown to exceed this
+ value, the region is split in two.
++
+.Default
+`10737418240`
+
+
+[[hbase.hregion.majorcompaction]]
+*`hbase.hregion.majorcompaction`*::
++
+.Description
+Time between major compactions, expressed in milliseconds. Set to 0 to disable
+ time-based automatic major compactions. User-requested and size-based major compactions will
+ still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause
+ compaction to start at a somewhat-random time during a given window of time. The default value
+ is 7 days, expressed in milliseconds. If major compactions are causing disruption in your
+ environment, you can configure them to run at off-peak times for your deployment, or disable
+ time-based major compactions by setting this parameter to 0, and run major compactions in a
+ cron job or by another external mechanism.
++
+.Default
+`604800000`
+
+
+[[hbase.hregion.majorcompaction.jitter]]
+*`hbase.hregion.majorcompaction.jitter`*::
++
+.Description
+A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur
+ a given amount of time either side of hbase.hregion.majorcompaction. The smaller the number,
+ the closer the compactions will happen to the hbase.hregion.majorcompaction
+ interval.
++
+.Default
+`0.50`
+
+
+[[hbase.hstore.compactionThreshold]]
+*`hbase.hstore.compactionThreshold`*::
++
+.Description
+ If more than this number of StoreFiles exist in any one Store
+ (one StoreFile is written per flush of MemStore), a compaction is run to rewrite all
+ StoreFiles into a single StoreFile. Larger values delay compaction, but when compaction does
+ occur, it takes longer to complete.
++
+.Default
+`3`
+
+
+[[hbase.hstore.flusher.count]]
+*`hbase.hstore.flusher.count`*::
++
+.Description
+ The number of flush threads. With fewer threads, the MemStore flushes will be
+ queued. With more threads, the flushes will be executed in parallel, increasing the load on
+ HDFS, and potentially causing more compactions.
++
+.Default
+`2`
+
+
+[[hbase.hstore.blockingStoreFiles]]
+*`hbase.hstore.blockingStoreFiles`*::
++
+.Description
+ If more than this number of StoreFiles exist in any one Store (one StoreFile
+ is written per flush of MemStore), updates are blocked for this region until a compaction is
+ completed, or until hbase.hstore.blockingWaitTime has been exceeded.
++
+.Default
+`10`
+
+
+[[hbase.hstore.blockingWaitTime]]
+*`hbase.hstore.blockingWaitTime`*::
++
+.Description
+ The time for which a region will block updates after reaching the StoreFile limit
+ defined by hbase.hstore.blockingStoreFiles. After this time has elapsed, the region will stop
+ blocking updates even if a compaction has not been completed.
++
+.Default
+`90000`
+
+
+[[hbase.hstore.compaction.min]]
+*`hbase.hstore.compaction.min`*::
++
+.Description
+The minimum number of StoreFiles which must be eligible for compaction before
+ compaction can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with
+ too many tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction
+ each time you have two StoreFiles in a Store, and this is probably not appropriate. If you
+ set this value too high, all the other values will need to be adjusted accordingly. For most
+ cases, the default value is appropriate. In previous versions of HBase, the parameter
+ hbase.hstore.compaction.min was named hbase.hstore.compactionThreshold.
++
+.Default
+`3`
+
+
+[[hbase.hstore.compaction.max]]
+*`hbase.hstore.compaction.max`*::
++
+.Description
+The maximum number of StoreFiles which will be selected for a single minor
+ compaction, regardless of the number of eligible StoreFiles. Effectively, the value of
+ hbase.hstore.compaction.max controls the length of time it takes a single compaction to
+ complete. Setting it larger means that more StoreFiles are included in a compaction. For most
+ cases, the default value is appropriate.
++
+.Default
+`10`
+
+
+[[hbase.hstore.compaction.min.size]]
+*`hbase.hstore.compaction.min.size`*::
++
+.Description
+A StoreFile smaller than this size will always be eligible for minor compaction.
+ HFiles this size or larger are evaluated by hbase.hstore.compaction.ratio to determine if
+ they are eligible. Because this limit represents the "automatic include"limit for all
+ StoreFiles smaller than this value, this value may need to be reduced in write-heavy
+ environments where many StoreFiles in the 1-2 MB range are being flushed, because every
+ StoreFile will be targeted for compaction and the resulting StoreFiles may still be under the
+ minimum size and require further compaction. If this parameter is lowered, the ratio check is
+ triggered more quickly. This addressed some issues seen in earlier versions of HBase but
+ changing this parameter is no longer necessary in most situations. Default: 128 MB expressed
+ in bytes.
++
+.Default
+`134217728`
+
+
+[[hbase.hstore.compaction.max.size]]
+*`hbase.hstore.compaction.max.size`*::
++
+.Description
+A StoreFile larger than this size will be excluded from compaction. The effect of
+ raising hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get
+ compacted often. If you feel that compaction is happening too often without much benefit, you
+ can try raising this value. Default: the value of LONG.MAX_VALUE, expressed in bytes.
++
+.Default
+`9223372036854775807`
+
+
+[[hbase.hstore.compaction.ratio]]
+*`hbase.hstore.compaction.ratio`*::
++
+.Description
+For minor compaction, this ratio is used to determine whether a given StoreFile
+ which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its
+ effect is to limit compaction of large StoreFiles. The value of hbase.hstore.compaction.ratio
+ is expressed as a floating-point decimal. A large ratio, such as 10, will produce a single
+ giant StoreFile. Conversely, a low value, such as .25, will produce behavior similar to the
+ BigTable compaction algorithm, producing four StoreFiles. A moderate value of between 1.0 and
+ 1.4 is recommended. When tuning this value, you are balancing write costs with read costs.
+ Raising the value (to something like 1.4) will have more write costs, because you will
+ compact larger StoreFiles. However, during reads, HBase will need to seek through fewer
+ StoreFiles to accomplish the read. Consider this approach if you cannot take advantage of
+ Bloom filters. Otherwise, you can lower this value to something like 1.0 to reduce the
+ background cost of writes, and use Bloom filters to control the number of StoreFiles touched
+ during reads. For most cases, the default value is appropriate.
++
+.Default
+`1.2F`
+
+
+[[hbase.hstore.compaction.ratio.offpeak]]
+*`hbase.hstore.compaction.ratio.offpeak`*::
++
+.Description
+Allows you to set a different (by default, more aggressive) ratio for determining
+ whether larger StoreFiles are included in compactions during off-peak hours. Works in the
+ same way as hbase.hstore.compaction.ratio. Only applies if hbase.offpeak.start.hour and
+ hbase.offpeak.end.hour are also enabled.
++
+.Default
+`5.0F`
+
+
+[[hbase.hstore.time.to.purge.deletes]]
+*`hbase.hstore.time.to.purge.deletes`*::
++
+.Description
+The amount of time to delay purging of delete markers with future timestamps. If
+ unset, or set to 0, all delete markers, including those with future timestamps, are purged
+ during the next major compaction. Otherwise, a delete marker is kept until the major compaction
+ which occurs after the marker's timestamp plus the value of this setting, in milliseconds.
+
++
+.Default
+`0`
+
+
+[[hbase.offpeak.start.hour]]
+*`hbase.offpeak.start.hour`*::
++
+.Description
+The start of off-peak hours, expressed as an integer between 0 and 23, inclusive.
+ Set to -1 to disable off-peak.
++
+.Default
+`-1`
+
+
+[[hbase.offpeak.end.hour]]
+*`hbase.offpeak.end.hour`*::
++
+.Description
+The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set
+ to -1 to disable off-peak.
++
+.Default
+`-1`
+
+
+[[hbase.regionserver.thread.compaction.throttle]]
+*`hbase.regionserver.thread.compaction.throttle`*::
++
+.Description
+There are two different thread pools for compactions, one for large compactions and
+ the other for small compactions. This helps to keep compaction of lean tables (such as
+ hbase:meta) fast. If a compaction is larger than this threshold, it
+ goes into the large compaction pool. In most cases, the default value is appropriate. Default:
+ 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults to 128MB).
+ The value field assumes that the value of hbase.hregion.memstore.flush.size is unchanged from
+ the default.
++
+.Default
+`2684354560`
+
+
+[[hbase.hstore.compaction.kv.max]]
+*`hbase.hstore.compaction.kv.max`*::
++
+.Description
+The maximum number of KeyValues to read and then write in a batch when flushing or
+ compacting. Set this lower if you have big KeyValues and problems with Out Of Memory
+ Exceptions Set this higher if you have wide, small rows.
++
+.Default
+`10`
+
+
+[[hbase.storescanner.parallel.seek.enable]]
+*`hbase.storescanner.parallel.seek.enable`*::
++
+.Description
+
+ Enables StoreFileScanner parallel-seeking in StoreScanner,
+ a feature which can reduce response latency under special conditions.
++
+.Default
+`false`
+
+
+[[hbase.storescanner.parallel.seek.threads]]
+*`hbase.storescanner.parallel.seek.threads`*::
++
+.Description
+
+ The default thread pool size if parallel-seeking feature enabled.
++
+.Default
+`10`
+
+
+[[hfile.block.cache.size]]
+*`hfile.block.cache.size`*::
++
+.Description
+Percentage of maximum heap (-Xmx setting) to allocate to block cache
+ used by a StoreFile. Default of 0.4 means allocate 40%.
+ Set to 0 to disable but it's not recommended; you need at least
+ enough cache to hold the storefile indices.
++
+.Default
+`0.4`
+
+
+[[hfile.block.index.cacheonwrite]]
+*`hfile.block.index.cacheonwrite`*::
++
+.Description
+This allows to put non-root multi-level index blocks into the block
+ cache at the time the index is being written.
++
+.Default
+`false`
+
+
+[[hfile.index.block.max.size]]
+*`hfile.index.block.max.size`*::
++
+.Description
+When the size of a leaf-level, intermediate-level, or root-level
+ index block in a multi-level block index grows to this size, the
+ block is written out and a new block is started.
++
+.Default
+`131072`
+
+
+[[hbase.bucketcache.ioengine]]
+*`hbase.bucketcache.ioengine`*::
++
+.Description
+Where to store the contents of the bucketcache. One of: onheap,
+ offheap, or file. If a file, set it to file:PATH_TO_FILE. See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html for more information.
+
++
+.Default
+``
+
+
+[[hbase.bucketcache.combinedcache.enabled]]
+*`hbase.bucketcache.combinedcache.enabled`*::
++
+.Description
+Whether or not the bucketcache is used in league with the LRU
+ on-heap block cache. In this mode, indices and blooms are kept in the LRU
+ blockcache and the data blocks are kept in the bucketcache.
++
+.Default
+`true`
+
+
+[[hbase.bucketcache.size]]
+*`hbase.bucketcache.size`*::
++
+.Description
+Used along with bucket cache, this is a float that EITHER represents a percentage of total heap
+ memory size to give to the cache (if < 1.0) OR, it is the capacity in megabytes of the cache.
++
+.Default
+`0` when specified as a float
+
+
+[[hbase.bucketcache.sizes]]
+*`hbase.bucketcache.sizes`*::
++
+.Description
+A comma-separated list of sizes for buckets for the bucketcache
+ if you use multiple sizes. Should be a list of block sizes in order from smallest
+ to largest. The sizes you use will depend on your data access patterns.
++
+.Default
+``
+
+
+[[hfile.format.version]]
+*`hfile.format.version`*::
++
+.Description
+The HFile format version to use for new files.
+ Version 3 adds support for tags in hfiles (See http://hbase.apache.org/book.html#hbase.tags).
+ Distributed Log Replay requires that tags are enabled. Also see the configuration
+ 'hbase.replication.rpc.codec'.
+
++
+.Default
+`3`
+
+
+[[hfile.block.bloom.cacheonwrite]]
+*`hfile.block.bloom.cacheonwrite`*::
++
+.Description
+Enables cache-on-write for inline blocks of a compound Bloom filter.
++
+.Default
+`false`
+
+
+[[io.storefile.bloom.block.size]]
+*`io.storefile.bloom.block.size`*::
++
+.Description
+The size in bytes of a single block ("chunk") of a compound Bloom
+ filter. This size is approximate, because Bloom blocks can only be
+ inserted at data block boundaries, and the number of keys per data
+ block varies.
++
+.Default
+`131072`
+
+
+[[hbase.rs.cacheblocksonwrite]]
+*`hbase.rs.cacheblocksonwrite`*::
++
+.Description
+Whether an HFile block should be added to the block cache when the
+ block is finished.
++
+.Default
+`false`
+
+
+[[hbase.rpc.timeout]]
+*`hbase.rpc.timeout`*::
++
+.Description
+This is for the RPC layer to define how long HBase client applications
+ take for a remote call to time out. It uses pings to check connections
+ but will eventually throw a TimeoutException.
++
+.Default
+`60000`
+
+
+[[hbase.rpc.shortoperation.timeout]]
+*`hbase.rpc.shortoperation.timeout`*::
++
+.Description
+This is another version of "hbase.rpc.timeout". For those RPC operation
+ within cluster, we rely on this configuration to set a short timeout limitation
+ for short operation. For example, short rpc timeout for region server's trying
+ to report to active master can benefit quicker master failover process.
++
+.Default
+`10000`
+
+
+[[hbase.ipc.client.tcpnodelay]]
+*`hbase.ipc.client.tcpnodelay`*::
++
+.Description
+Set no delay on rpc socket connections. See
+ http://docs.oracle.com/javase/1.5.0/docs/api/java/net/Socket.html#getTcpNoDelay()
++
+.Default
+`true`
+
+
+[[hbase.master.keytab.file]]
+*`hbase.master.keytab.file`*::
++
+.Description
+Full path to the kerberos keytab file to use for logging in
+ the configured HMaster server principal.
++
+.Default
+``
+
+
+[[hbase.master.kerberos.principal]]
+*`hbase.master.kerberos.principal`*::
++
+.Description
+Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name
+ that should be used to run the HMaster process. The principal name should
+ be in the form: user/hostname@DOMAIN. If "_HOST" is used as the hostname
+ portion, it will be replaced with the actual hostname of the running
+ instance.
++
+.Default
+``
+
+
+[[hbase.regionserver.keytab.file]]
+*`hbase.regionserver.keytab.file`*::
++
+.Description
+Full path to the kerberos keytab file to use for logging in
+ the configured HRegionServer server principal.
++
+.Default
+``
+
+
+[[hbase.regionserver.kerberos.principal]]
+*`hbase.regionserver.kerberos.principal`*::
++
+.Description
+Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name
+ that should be used to run the HRegionServer process. The principal name
+ should be in the form: user/hostname@DOMAIN. If "_HOST" is used as the
+ hostname portion, it will be replaced with the actual hostname of the
+ running instance. An entry for this principal must exist in the file
+ specified in hbase.regionserver.keytab.file
++
+.Default
+``
+
+
+[[hadoop.policy.file]]
+*`hadoop.policy.file`*::
++
+.Description
+The policy configuration file used by RPC servers to make
+ authorization decisions on client requests. Only used when HBase
+ security is enabled.
++
+.Default
+`hbase-policy.xml`
+
+
+[[hbase.superuser]]
+*`hbase.superuser`*::
++
+.Description
+List of users or groups (comma-separated), who are allowed
+ full privileges, regardless of stored ACLs, across the cluster.
+ Only used when HBase security is enabled.
++
+.Default
+``
+
+
+[[hbase.auth.key.update.interval]]
+*`hbase.auth.key.update.interval`*::
++
+.Description
+The update interval for master key for authentication tokens
+ in servers in milliseconds. Only used when HBase security is enabled.
++
+.Default
+`86400000`
+
+
+[[hbase.auth.token.max.lifetime]]
+*`hbase.auth.token.max.lifetime`*::
++
+.Description
+The maximum lifetime in milliseconds after which an
+ authentication token expires. Only used when HBase security is enabled.
++
+.Default
+`604800000`
+
+
+[[hbase.ipc.client.fallback-to-simple-auth-allowed]]
+*`hbase.ipc.client.fallback-to-simple-auth-allowed`*::
++
+.Description
+When a client is configured to attempt a secure connection, but attempts to
+ connect to an insecure server, that server may instruct the client to
+ switch to SASL SIMPLE (unsecure) authentication. This setting controls
+ whether or not the client will accept this instruction from the server.
+ When false (the default), the client will not allow the fallback to SIMPLE
+ authentication, and will abort the connection.
++
+.Default
+`false`
+
+
+[[hbase.display.keys]]
+*`hbase.display.keys`*::
++
+.Description
+When this is set to true the webUI and such will display all start/end keys
+ as part of the table details, region names, etc. When this is set to false,
+ the keys are hidden.
++
+.Default
+`true`
+
+
+[[hbase.coprocessor.region.classes]]
+*`hbase.coprocessor.region.classes`*::
++
+.Description
+A comma-separated list of Coprocessors that are loaded by
+ default on all tables. For any override coprocessor method, these classes
+ will be called in order. After implementing your own Coprocessor, just put
+ it in HBase's classpath and add the fully qualified class name here.
+ A coprocessor can also be loaded on demand by setting HTableDescriptor.
++
+.Default
+``
+
+
+[[hbase.rest.port]]
+*`hbase.rest.port`*::
++
+.Description
+The port for the HBase REST server.
++
+.Default
+`8080`
+
+
+[[hbase.rest.readonly]]
+*`hbase.rest.readonly`*::
++
+.Description
+Defines the mode the REST server will be started in. Possible values are:
+ false: All HTTP methods are permitted - GET/PUT/POST/DELETE.
+ true: Only the GET method is permitted.
++
+.Default
+`false`
+
+
+[[hbase.rest.threads.max]]
+*`hbase.rest.threads.max`*::
++
+.Description
+The maximum number of threads of the REST server thread pool.
+ Threads in the pool are reused to process REST requests. This
+ controls the maximum number of requests processed concurrently.
+ It may help to control the memory used by the REST server to
+ avoid OOM issues. If the thread pool is full, incoming requests
+ will be queued up and wait for some free threads.
++
+.Default
+`100`
+
+
+[[hbase.rest.threads.min]]
+*`hbase.rest.threads.min`*::
++
+.Description
+The minimum number of threads of the REST server thread pool.
+ The thread pool always has at least these number of threads so
+ the REST server is ready to serve incoming requests.
++
+.Default
+`2`
+
+
+[[hbase.rest.support.proxyuser]]
+*`hbase.rest.support.proxyuser`*::
++
+.Description
+Enables running the REST server to support proxy-user mode.
++
+.Default
+`false`
+
+
+[[hbase.defaults.for.version.skip]]
+*`hbase.defaults.for.version.skip`*::
++
+.Description
+Set to true to skip the 'hbase.defaults.for.version' check.
+ Setting this to true can be useful in contexts other than
+ the other side of a maven generation; i.e. running in an
+ ide. You'll want to set this boolean to true to avoid
+ seeing the RuntimException complaint: "hbase-default.xml file
+ seems to be for and old version of HBase (\${hbase.version}), this
+ version is X.X.X-SNAPSHOT"
++
+.Default
+`false`
+
+
+[[hbase.coprocessor.master.classes]]
+*`hbase.coprocessor.master.classes`*::
++
+.Description
+A comma-separated list of
+ org.apache.hadoop.hbase.coprocessor.MasterObserver coprocessors that are
+ loaded by default on the active HMaster process. For any implemented
+ coprocessor methods, the listed classes will be called in order. After
+ implementing your own MasterObserver, just put it in HBase's classpath
+ and add the fully qualified class name here.
++
+.Default
+``
+
+
+[[hbase.coprocessor.abortonerror]]
+*`hbase.coprocessor.abortonerror`*::
++
+.Description
+Set to true to cause the hosting server (master or regionserver)
+ to abort if a coprocessor fails to load, fails to initialize, or throws an
+ unexpected Throwable object. Setting this to false will allow the server to
+ continue execution but the system wide state of the coprocessor in question
+ will become inconsistent as it will be properly executing in only a subset
+ of servers, so this is most useful for debugging only.
++
+.Default
+`true`
+
+
+[[hbase.online.schema.update.enable]]
+*`hbase.online.schema.update.enable`*::
++
+.Description
+Set true to enable online schema changes.
++
+.Default
+`true`
+
+
+[[hbase.table.lock.enable]]
+*`hbase.table.lock.enable`*::
++
+.Description
+Set to true to enable locking the table in zookeeper for schema change operations.
+ Table locking from master prevents concurrent schema modifications to corrupt table
+ state.
++
+.Default
+`true`
+
+
+[[hbase.table.max.rowsize]]
+*`hbase.table.max.rowsize`*::
++
+.Description
+
+ Maximum size of single row in bytes (default is 1 Gb) for Get'ting
+ or Scan'ning without in-row scan flag set. If row size exceeds this limit
+ RowTooBigException is thrown to client.
+
++
+.Default
+`1073741824`
+
+
+[[hbase.thrift.minWorkerThreads]]
+*`hbase.thrift.minWorkerThreads`*::
++
+.Description
+The "core size" of the thread pool. New threads are created on every
+ connection until this many threads are created.
++
+.Default
+`16`
+
+
+[[hbase.thrift.maxWorkerThreads]]
+*`hbase.thrift.maxWorkerThreads`*::
++
+.Description
+The maximum size of the thread pool. When the pending request queue
+ overflows, new threads are created until their number reaches this number.
+ After that, the server starts dropping connections.
++
+.Default
+`1000`
+
+
+[[hbase.thrift.maxQueuedRequests]]
+*`hbase.thrift.maxQueuedRequests`*::
++
+.Description
+The maximum number of pending Thrift connections waiting in the queue. If
+ there are no idle threads in the pool, the server queues requests. Only
+ when the queue overflows, new threads are added, up to
+ hbase.thrift.maxQueuedRequests threads.
++
+.Default
+`1000`
+
+
+[[hbase.thrift.htablepool.size.max]]
+*`hbase.thrift.htablepool.size.max`*::
++
+.Description
+The upper bound for the table pool used in the Thrift gateways server.
+ Since this is per table name, we assume a single table and so with 1000 default
+ worker threads max this is set to a matching number. For other workloads this number
+ can be adjusted as needed.
+
++
+.Default
+`1000`
+
+
+[[hbase.regionserver.thrift.framed]]
+*`hbase.regionserver.thrift.framed`*::
++
+.Description
+Use Thrift TFramedTransport on the server side.
+ This is the recommended transport for thrift servers and requires a similar setting
+ on the client side. Changing this to false will select the default transport,
+ vulnerable to DoS when malformed requests are issued due to THRIFT-601.
+
++
+.Default
+`false`
+
+
+[[hbase.regionserver.thrift.framed.max_frame_size_in_mb]]
+*`hbase.regionserver.thrift.framed.max_frame_size_in_mb`*::
++
+.Description
+Default frame size when using framed transport
++
+.Default
+`2`
+
+
+[[hbase.regionserver.thrift.compact]]
+*`hbase.regionserver.thrift.compact`*::
++
+.Description
+Use Thrift TCompactProtocol binary serialization protocol.
++
+.Default
+`false`
+
+
+[[hbase.data.umask.enable]]
+*`hbase.data.umask.enable`*::
++
+.Description
+Enable, if true, that file permissions should be assigned
+ to the files written by the regionserver
++
+.Default
+`false`
+
+
+[[hbase.data.umask]]
+*`hbase.data.umask`*::
++
+.Description
+File permissions that should be used to write data
+ files when hbase.data.umask.enable is true
++
+.Default
+`000`
+
+
+[[hbase.metrics.showTableName]]
+*`hbase.metrics.showTableName`*::
++
+.Description
+Whether to include the prefix "tbl.tablename" in per-column family metrics.
+ If true, for each metric M, per-cf metrics will be reported for tbl.T.cf.CF.M, if false,
+ per-cf metrics will be aggregated by column-family across tables, and reported for cf.CF.M.
+ In both cases, the aggregated metric M across tables and cfs will be reported.
++
+.Default
+`true`
+
+
+[[hbase.metrics.exposeOperationTimes]]
+*`hbase.metrics.exposeOperationTimes`*::
++
+.Description
+Whether to report metrics about time taken performing an
+ operation on the region server. Get, Put, Delete, Increment, and Append can all
+ have their times exposed through Hadoop metrics per CF and per region.
++
+.Default
+`true`
+
+
+[[hbase.snapshot.enabled]]
+*`hbase.snapshot.enabled`*::
++
+.Description
+Set to true to allow snapshots to be taken / restored / cloned.
++
+.Default
+`true`
+
+
+[[hbase.snapshot.restore.take.failsafe.snapshot]]
+*`hbase.snapshot.restore.take.failsafe.snapshot`*::
++
+.Description
+Set to true to take a snapshot before the restore operation.
+ The snapshot taken will be used in case of failure, to restore the previous state.
+ At the end of the restore operation this snapshot will be deleted
++
+.Default
+`true`
+
+
+[[hbase.snapshot.restore.failsafe.name]]
+*`hbase.snapshot.restore.failsafe.name`*::
++
+.Description
+Name of the failsafe snapshot taken by the restore operation.
+ You can use the {snapshot.name}, {table.name} and {restore.timestamp} variables
+ to create a name based on what you are restoring.
++
+.Default
+`hbase-failsafe-{snapshot.name}-{restore.timestamp}`
+
+
+[[hbase.server.compactchecker.interval.multiplier]]
+*`hbase.server.compactchecker.interval.multiplier`*::
++
+.Description
+The number that determines how often we scan to see if compaction is necessary.
+ Normally, compactions are done after some events (such as memstore flush), but if
+ region didn't receive a lot of writes for some time, or due to different compaction
+ policies, it may be necessary to check it periodically. The interval between checks is
+ hbase.server.compactchecker.interval.multiplier multiplied by
+ hbase.server.thread.wakefrequency.
++
+.Default
+`1000`
+
+
+[[hbase.lease.recovery.timeout]]
+*`hbase.lease.recovery.timeout`*::
++
+.Description
+How long we wait on dfs lease recovery in total before giving up.
++
+.Default
+`900000`
+
+
+[[hbase.lease.recovery.dfs.timeout]]
+*`hbase.lease.recovery.dfs.timeout`*::
++
+.Description
+How long between dfs recover lease invocations. Should be larger than the sum of
+ the time it takes for the namenode to issue a block recovery command as part of
+ datanode; dfs.heartbeat.interval and the time it takes for the primary
+ datanode, performing block recovery to timeout on a dead datanode; usually
+ dfs.client.socket-timeout. See the end of HBASE-8389 for more.
++
+.Default
+`64000`
+
+
+[[hbase.column.max.version]]
+*`hbase.column.max.version`*::
++
+.Description
+New column family descriptors will use this value as the default number of versions
+ to keep.
++
+.Default
+`1`
+
+
+[[hbase.dfs.client.read.shortcircuit.buffer.size]]
+*`hbase.dfs.client.read.shortcircuit.buffer.size`*::
++
+.Description
+If the DFSClient configuration
+ dfs.client.read.shortcircuit.buffer.size is unset, we will
+ use what is configured here as the short circuit read default
+ direct byte buffer size. DFSClient native default is 1MB; HBase
+ keeps its HDFS files open so number of file blocks * 1MB soon
+ starts to add up and threaten OOME because of a shortage of
+ direct memory. So, we set it down from the default. Make
+ it > the default hbase block size set in the HColumnDescriptor
+ which is usually 64k.
+
++
+.Default
+`131072`
+
+
+[[hbase.regionserver.checksum.verify]]
+*`hbase.regionserver.checksum.verify`*::
++
+.Description
+
+ If set to true (the default), HBase verifies the checksums for hfile
+ blocks. HBase writes checksums inline with the data when it writes out
+ hfiles. HDFS (as of this writing) writes checksums to a separate file
+ than the data file necessitating extra seeks. Setting this flag saves
+ some on i/o. Checksum verification by HDFS will be internally disabled
+ on hfile streams when this flag is set. If the hbase-checksum verification
+ fails, we will switch back to using HDFS checksums (so do not disable HDFS
+ checksums! And besides this feature applies to hfiles only, not to WALs).
+ If this parameter is set to false, then hbase will not verify any checksums,
+ instead it will depend on checksum verification being done in the HDFS client.
+
++
+.Default
+`true`
+
+
+[[hbase.hstore.bytes.per.checksum]]
+*`hbase.hstore.bytes.per.checksum`*::
++
+.Description
+
+ Number of bytes in a newly created checksum chunk for HBase-level
+ checksums in hfile blocks.
+
++
+.Default
+`16384`
+
+
+[[hbase.hstore.checksum.algorithm]]
+*`hbase.hstore.checksum.algorithm`*::
++
+.Description
+
+ Name of an algorithm that is used to compute checksums. Possible values
+ are NULL, CRC32, CRC32C.
+
++
+.Default
+`CRC32`
+
+
+[[hbase.status.published]]
+*`hbase.status.published`*::
++
+.Description
+
+ This setting activates the publication by the master of the status of the region server.
+ When a region server dies and its recovery starts, the master will push this information
+ to the client application, to let them cut the connection immediately instead of waiting
+ for a timeout.
+
++
+.Default
+`false`
+
+
+[[hbase.status.publisher.class]]
+*`hbase.status.publisher.class`*::
++
+.Description
+
+ Implementation of the status publication with a multicast message.
+
++
+.Default
+`org.apache.hadoop.hbase.master.ClusterStatusPublisher$MulticastPublisher`
+
+
+[[hbase.status.listener.class]]
+*`hbase.status.listener.class`*::
++
+.Description
+
+ Implementation of the status listener with a multicast message.
+
++
+.Default
+`org.apache.hadoop.hbase.client.ClusterStatusListener$MulticastListener`
+
+
+[[hbase.status.multicast.address.ip]]
+*`hbase.status.multicast.address.ip`*::
++
+.Description
+
+ Multicast address to use for the status publication by multicast.
+
++
+.Default
+`226.1.1.3`
+
+
+[[hbase.status.multicast.address.port]]
+*`hbase.status.multicast.address.port`*::
++
+.Description
+
+ Multicast port to use for the status publication by multicast.
+
++
+.Default
+`16100`
+
+
+[[hbase.dynamic.jars.dir]]
+*`hbase.dynamic.jars.dir`*::
++
+.Description
+
+ The directory from which the custom filter/co-processor jars can be loaded
+ dynamically by the region server without the need to restart. However,
+ an already loaded filter/co-processor class would not be un-loaded. See
+ HBASE-1936 for more details.
+
++
+.Default
+`${hbase.rootdir}/lib`
+
+
+[[hbase.security.authentication]]
+*`hbase.security.authentication`*::
++
+.Description
+
+ Controls whether or not secure authentication is enabled for HBase.
+ Possible values are 'simple' (no authentication), and 'kerberos'.
+
++
+.Default
+`simple`
+
+
+[[hbase.rest.filter.classes]]
+*`hbase.rest.filter.classes`*::
++
+.Description
+
+ Servlet filters for REST service.
+
++
+.Default
+`org.apache.hadoop.hbase.rest.filter.GzipFilter`
+
+
+[[hbase.master.loadbalancer.class]]
+*`hbase.master.loadbalancer.class`*::
++
+.Description
+
+ Class used to execute the regions balancing when the period occurs.
+ See the class comment for more on how it works
+ http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/balancer/StochasticLoadBalancer.html
+ It replaces the DefaultLoadBalancer as the default (since renamed
+ as the SimpleLoadBalancer).
+
++
+.Default
+`org.apache.hadoop.hbase.master.balancer.StochasticLoadBalancer`
+
+
+[[hbase.security.exec.permission.checks]]
+*`hbase.security.exec.permission.checks`*::
++
+.Description
+
+ If this setting is enabled and ACL based access control is active (the
+ AccessController coprocessor is installed either as a system coprocessor
+ or on a table as a table coprocessor) then you must grant all relevant
+ users EXEC privilege if they require the ability to execute coprocessor
+ endpoint calls. EXEC privilege, like any other permission, can be
+ granted globally to a user, or to a user on a per table or per namespace
+ basis. For more information on coprocessor endpoints, see the coprocessor
+ section of the HBase online manual. For more information on granting or
+ revoking permissions using the AccessController, see the security
+ section of the HBase online manual.
+
++
+.Default
+`false`
+
+
+[[hbase.procedure.regionserver.classes]]
+*`hbase.procedure.regionserver.classes`*::
++
+.Description
+A comma-separated list of
+ org.apache.hadoop.hbase.procedure.RegionServerProcedureManager procedure managers that are
+ loaded by default on the active HRegionServer process. The lifecycle methods (init/start/stop)
+ will be called by the active HRegionServer process to perform the specific globally barriered
+ procedure. After implementing your own RegionServerProcedureManager, just put it in
+ HBase's classpath and add the fully qualified class name here.
+
++
+.Default
+``
+
+
+[[hbase.procedure.master.classes]]
+*`hbase.procedure.master.classes`*::
++
+.Description
+A comma-separated list of
+ org.apache.hadoop.hbase.procedure.MasterProcedureManager procedure managers that are
+ loaded by default on the active HMaster process. A procedure is identified by its signature and
+ users can use the signature and an instant name to trigger an execution of a globally barriered
+ procedure. After implementing your own MasterProcedureManager, just put it in HBase's classpath
+ and add the fully qualified class name here.
++
+.Default
+``
+
+
+[[hbase.coordinated.state.manager.class]]
+*`hbase.coordinated.state.manager.class`*::
++
+.Description
+Fully qualified name of class implementing coordinated state manager.
++
+.Default
+`org.apache.hadoop.hbase.coordination.ZkCoordinatedStateManager`
+
+
+[[hbase.regionserver.storefile.refresh.period]]
+*`hbase.regionserver.storefile.refresh.period`*::
++
+.Description
+
+ The period (in milliseconds) for refreshing the store files for the secondary regions. 0
+ means this feature is disabled. Secondary regions sees new files (from flushes and
+ compactions) from primary once the secondary region refreshes the list of files in the
+ region (there is no notification mechanism). But too frequent refreshes might cause
+ extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL
+ (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger
+ value is also recommended with this setting.
+
++
+.Default
+`0`
+
+
+[[hbase.region.replica.replication.enabled]]
+*`hbase.region.replica.replication.enabled`*::
++
+.Description
+
+ Whether asynchronous WAL replication to the secondary region replicas is enabled or not.
+ If this is enabled, a replication peer named "region_replica_replication" will be created
+ which will tail the logs and replicate the mutatations to region replicas for tables that
+ have region replication > 1. If this is enabled once, disabling this replication also
+ requires disabling the replication peer using shell or ReplicationAdmin java class.
+ Replication to secondary region replicas works over standard inter-cluster replication.
+ So replication, if disabled explicitly, also has to be enabled by setting "hbase.replication"
+ to true for this feature to work.
+
++
+.Default
+`false`
+
+
+[[hbase.http.filter.initializers]]
+*`hbase.http.filter.initializers`*::
++
+.Description
+
+ A comma separated list of class names. Each class in the list must extend
+ org.apache.hadoop.hbase.http.FilterInitializer. The corresponding Filter will
+ be initialized. Then, the Filter will be applied to all user facing jsp
+ and servlet web pages.
+ The ordering of the list defines the ordering of the filters.
+ The default StaticUserWebFilter add a user principal as defined by the
+ hbase.http.staticuser.user property.
+
++
+.Default
+`org.apache.hadoop.hbase.http.lib.StaticUserWebFilter`
+
+
+[[hbase.security.visibility.mutations.checkauths]]
+*`hbase.security.visibility.mutations.checkauths`*::
++
+.Description
+
+ This property if enabled, will check whether the labels in the visibility expression are associated
+ with the user issuing the mutation
+
++
+.Default
+`false`
+
+
+[[hbase.http.max.threads]]
+*`hbase.http.max.threads`*::
++
+.Description
+
+ The maximum number of threads that the HTTP Server will create in its
+ ThreadPool.
+
++
+.Default
+`10`
+
+
+[[hbase.replication.rpc.codec]]
+*`hbase.replication.rpc.codec`*::
++
+.Description
+
+ The codec that is to be used when replication is enabled so that
+ the tags are also replicated. This is used along with HFileV3 which
+ supports tags in them. If tags are not used or if the hfile version used
+ is HFileV2 then KeyValueCodec can be used as the replication codec. Note that
+ using KeyValueCodecWithTags for replication when there are no tags causes no harm.
+
++
+.Default
+`org.apache.hadoop.hbase.codec.KeyValueCodecWithTags`
+
+
+[[hbase.http.staticuser.user]]
+*`hbase.http.staticuser.user`*::
++
+.Description
+
+ The user name to filter as, on static web filters
+ while rendering content. An example use is the HDFS
+ web UI (user to be used for browsing files).
+
++
+.Default
+`dr.stack`
+
+
+[[hbase.regionserver.handler.abort.on.error.percent]]
+*`hbase.regionserver.handler.abort.on.error.percent`*::
++
+.Description
+The percent of region server RPC threads failed to abort RS.
+ -1 Disable aborting; 0 Abort if even a single handler has died;
+ 0.x Abort only when this percent of handlers have died;
+ 1 Abort only all of the handers have died.
++
+.Default
+`0.5`
+
+
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/hbase_apis.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/hbase_apis.adoc b/src/main/asciidoc/_chapters/hbase_apis.adoc
new file mode 100644
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+++ b/src/main/asciidoc/_chapters/hbase_apis.adoc
@@ -0,0 +1,135 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[hbase_apis]]
+= Apache HBase APIs
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter provides information about performing operations using HBase native APIs.
+This information is not exhaustive, and provides a quick reference in addition to the link:http://hbase.apache.org/apidocs/index.html[User API Reference].
+The examples here are not comprehensive or complete, and should be used for purposes of illustration only.
+
+Apache HBase also works with multiple external APIs.
+See <<external_apis>> for more information.
+
+== Examples
+
+.Create, modify and delete a Table Using Java
+====
+
+[source,java]
+----
+package com.example.hbase.admin;
+
+package util;
+
+import java.io.IOException;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.hbase.HBaseConfiguration;
+import org.apache.hadoop.hbase.HColumnDescriptor;
+import org.apache.hadoop.hbase.HConstants;
+import org.apache.hadoop.hbase.HTableDescriptor;
+import org.apache.hadoop.hbase.TableName;
+import org.apache.hadoop.hbase.client.Admin;
+import org.apache.hadoop.hbase.client.Connection;
+import org.apache.hadoop.hbase.client.ConnectionFactory;
+import org.apache.hadoop.hbase.io.compress.Compression.Algorithm;
+
+public class Example {
+
+ private static final String TABLE_NAME = "MY_TABLE_NAME_TOO";
+ private static final String CF_DEFAULT = "DEFAULT_COLUMN_FAMILY";
+
+ public static void createOrOverwrite(Admin admin, HTableDescriptor table) throws IOException {
+ if (admin.tableExists(table.getTableName())) {
+ admin.disableTable(table.getTableName());
+ admin.deleteTable(table.getTableName());
+ }
+ admin.createTable(table);
+ }
+
+ public static void createSchemaTables(Configuration config) throws IOException {
+ try (Connection connection = ConnectionFactory.createConnection(config);
+ Admin admin = connection.getAdmin()) {
+
+ HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME));
+ table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY));
+
+ System.out.print("Creating table. ");
+ createOrOverwrite(admin, table);
+ System.out.println(" Done.");
+ }
+ }
+
+ public static void modifySchema (Configuration config) throws IOException {
+ try (Connection connection = ConnectionFactory.createConnection(config);
+ Admin admin = connection.getAdmin()) {
+
+ TableName tableName = TableName.valueOf(TABLE_NAME);
+ if (admin.tableExists(tableName)) {
+ System.out.println("Table does not exist.");
+ System.exit(-1);
+ }
+
+ HTableDescriptor table = new HTableDescriptor(tableName);
+
+ // Update existing table
+ HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF");
+ newColumn.setCompactionCompressionType(Algorithm.GZ);
+ newColumn.setMaxVersions(HConstants.ALL_VERSIONS);
+ admin.addColumn(tableName, newColumn);
+
+ // Update existing column family
+ HColumnDescriptor existingColumn = new HColumnDescriptor(CF_DEFAULT);
+ existingColumn.setCompactionCompressionType(Algorithm.GZ);
+ existingColumn.setMaxVersions(HConstants.ALL_VERSIONS);
+ table.modifyFamily(existingColumn);
+ admin.modifyTable(tableName, table);
+
+ // Disable an existing table
+ admin.disableTable(tableName);
+
+ // Delete an existing column family
+ admin.deleteColumn(tableName, CF_DEFAULT.getBytes("UTF-8"));
+
+ // Delete a table (Need to be disabled first)
+ admin.deleteTable(tableName);
+ }
+ }
+
+ public static void main(String... args) throws IOException {
+ Configuration config = HBaseConfiguration.create();
+
+ //Add any necessary configuration files (hbase-site.xml, core-site.xml)
+ config.addResource(new Path(System.getenv("HBASE_CONF_DIR"), "hbase-site.xml"));
+ config.addResource(new Path(System.getenv("HADOOP_CONF_DIR"), "core-site.xml"));
+ createSchemaTables(config);
+ modifySchema(config);
+ }
+}
+----
+====
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/hbase_history.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/hbase_history.adoc b/src/main/asciidoc/_chapters/hbase_history.adoc
new file mode 100644
index 0000000..de4aff5
--- /dev/null
+++ b/src/main/asciidoc/_chapters/hbase_history.adoc
@@ -0,0 +1,37 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[hbase.history]]
+== HBase History
+
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+* 2006: link:http://research.google.com/archive/bigtable.html[BigTable] paper published by Google.
+* 2006 (end of year): HBase development starts.
+* 2008: HBase becomes Hadoop sub-project.
+* 2010: HBase becomes Apache top-level project.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/hbck_in_depth.adoc
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diff --git a/src/main/asciidoc/_chapters/hbck_in_depth.adoc b/src/main/asciidoc/_chapters/hbck_in_depth.adoc
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@@ -0,0 +1,212 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[hbck.in.depth]]
+== hbck In Depth
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+HBaseFsck (hbck) is a tool for checking for region consistency and table integrity problems and repairing a corrupted HBase.
+It works in two basic modes -- a read-only inconsistency identifying mode and a multi-phase read-write repair mode.
+
+=== Running hbck to identify inconsistencies
+
+To check to see if your HBase cluster has corruptions, run hbck against your HBase cluster:
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck
+----
+
+At the end of the commands output it prints OK or tells you the number of INCONSISTENCIES present.
+You may also want to run run hbck a few times because some inconsistencies can be transient (e.g.
+cluster is starting up or a region is splitting). Operationally you may want to run hbck regularly and setup alert (e.g.
+via nagios) if it repeatedly reports inconsistencies . A run of hbck will report a list of inconsistencies along with a brief description of the regions and tables affected.
+The using the `-details` option will report more details including a representative listing of all the splits present in all the tables.
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck -details
+----
+
+If you just want to know if some tables are corrupted, you can limit hbck to identify inconsistencies in only specific tables.
+For example the following command would only attempt to check table TableFoo and TableBar.
+The benefit is that hbck will run in less time.
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck TableFoo TableBar
+----
+
+=== Inconsistencies
+
+If after several runs, inconsistencies continue to be reported, you may have encountered a corruption.
+These should be rare, but in the event they occur newer versions of HBase include the hbck tool enabled with automatic repair options.
+
+There are two invariants that when violated create inconsistencies in HBase:
+
+* HBase's region consistency invariant is satisfied if every region is assigned and deployed on exactly one region server, and all places where this state kept is in accordance.
+* HBase's table integrity invariant is satisfied if for each table, every possible row key resolves to exactly one region.
+
+Repairs generally work in three phases -- a read-only information gathering phase that identifies inconsistencies, a table integrity repair phase that restores the table integrity invariant, and then finally a region consistency repair phase that restores the region consistency invariant.
+Starting from version 0.90.0, hbck could detect region consistency problems report on a subset of possible table integrity problems.
+It also included the ability to automatically fix the most common inconsistency, region assignment and deployment consistency problems.
+This repair could be done by using the `-fix` command line option.
+These problems close regions if they are open on the wrong server or on multiple region servers and also assigns regions to region servers if they are not open.
+
+Starting from HBase versions 0.90.7, 0.92.2 and 0.94.0, several new command line options are introduced to aid repairing a corrupted HBase.
+This hbck sometimes goes by the nickname ``uberhbck''. Each particular version of uber hbck is compatible with the HBase's of the same major version (0.90.7 uberhbck can repair a 0.90.4). However, versions <=0.90.6 and versions <=0.92.1 may require restarting the master or failing over to a backup master.
+
+=== Localized repairs
+
+When repairing a corrupted HBase, it is best to repair the lowest risk inconsistencies first.
+These are generally region consistency repairs -- localized single region repairs, that only modify in-memory data, ephemeral zookeeper data, or patch holes in the META table.
+Region consistency requires that the HBase instance has the state of the region's data in HDFS (.regioninfo files), the region's row in the hbase:meta table., and region's deployment/assignments on region servers and the master in accordance.
+Options for repairing region consistency include:
+
+* `-fixAssignments` (equivalent to the 0.90 `-fix` option) repairs unassigned, incorrectly assigned or multiply assigned regions.
+* `-fixMeta` which removes meta rows when corresponding regions are not present in HDFS and adds new meta rows if they regions are present in HDFS while not in META. To fix deployment and assignment problems you can run this command:
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck -fixAssignments
+----
+
+To fix deployment and assignment problems as well as repairing incorrect meta rows you can run this command:
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck -fixAssignments -fixMeta
+----
+
+There are a few classes of table integrity problems that are low risk repairs.
+The first two are degenerate (startkey == endkey) regions and backwards regions (startkey > endkey). These are automatically handled by sidelining the data to a temporary directory (/hbck/xxxx). The third low-risk class is hdfs region holes.
+This can be repaired by using the:
+
+* `-fixHdfsHoles` option for fabricating new empty regions on the file system.
+ If holes are detected you can use -fixHdfsHoles and should include -fixMeta and -fixAssignments to make the new region consistent.
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck -fixAssignments -fixMeta -fixHdfsHoles
+----
+
+Since this is a common operation, we've added a the `-repairHoles` flag that is equivalent to the previous command:
+
+[source,bourne]
+----
+
+$ ./bin/hbase hbck -repairHoles
+----
+
+If inconsistencies still remain after these steps, you most likely have table integrity problems related to orphaned or overlapping regions.
+
+=== Region Overlap Repairs
+
+Table integrity problems can require repairs that deal with overlaps.
+This is a riskier operation because it requires modifications to the file system, requires some decision making, and may require some manual steps.
+For these repairs it is best to analyze the output of a `hbck -details` run so that you isolate repairs attempts only upon problems the checks identify.
+Because this is riskier, there are safeguard that should be used to limit the scope of the repairs.
+WARNING: This is a relatively new and have only been tested on online but idle HBase instances (no reads/writes). Use at your own risk in an active production environment! The options for repairing table integrity violations include:
+
+* `-fixHdfsOrphans` option for ``adopting'' a region directory that is missing a region metadata file (the .regioninfo file).
+* `-fixHdfsOverlaps` ability for fixing overlapping regions
+
+When repairing overlapping regions, a region's data can be modified on the file system in two ways: 1) by merging regions into a larger region or 2) by sidelining regions by moving data to ``sideline'' directory where data could be restored later.
+Merging a large number of regions is technically correct but could result in an extremely large region that requires series of costly compactions and splitting operations.
+In these cases, it is probably better to sideline the regions that overlap with the most other regions (likely the largest ranges) so that merges can happen on a more reasonable scale.
+Since these sidelined regions are already laid out in HBase's native directory and HFile format, they can be restored by using HBase's bulk load mechanism.
+The default safeguard thresholds are conservative.
+These options let you override the default thresholds and to enable the large region sidelining feature.
+
+* `-maxMerge <n>` maximum number of overlapping regions to merge
+* `-sidelineBigOverlaps` if more than maxMerge regions are overlapping, sideline attempt to sideline the regions overlapping with the most other regions.
+* `-maxOverlapsToSideline <n>` if sidelining large overlapping regions, sideline at most n regions.
+
+Since often times you would just want to get the tables repaired, you can use this option to turn on all repair options:
+
+* `-repair` includes all the region consistency options and only the hole repairing table integrity options.
+
+Finally, there are safeguards to limit repairs to only specific tables.
+For example the following command would only attempt to check and repair table TableFoo and TableBar.
+
+----
+
+$ ./bin/hbase hbck -repair TableFoo TableBar
+----
+
+==== Special cases: Meta is not properly assigned
+
+There are a few special cases that hbck can handle as well.
+Sometimes the meta table's only region is inconsistently assigned or deployed.
+In this case there is a special `-fixMetaOnly` option that can try to fix meta assignments.
+
+----
+
+$ ./bin/hbase hbck -fixMetaOnly -fixAssignments
+----
+
+==== Special cases: HBase version file is missing
+
+HBase's data on the file system requires a version file in order to start.
+If this flie is missing, you can use the `-fixVersionFile` option to fabricating a new HBase version file.
+This assumes that the version of hbck you are running is the appropriate version for the HBase cluster.
+
+==== Special case: Root and META are corrupt.
+
+The most drastic corruption scenario is the case where the ROOT or META is corrupted and HBase will not start.
+In this case you can use the OfflineMetaRepair tool create new ROOT and META regions and tables.
+This tool assumes that HBase is offline.
+It then marches through the existing HBase home directory, loads as much information from region metadata files (.regioninfo files) as possible from the file system.
+If the region metadata has proper table integrity, it sidelines the original root and meta table directories, and builds new ones with pointers to the region directories and their data.
+
+----
+
+$ ./bin/hbase org.apache.hadoop.hbase.util.hbck.OfflineMetaRepair
+----
+
+NOTE: This tool is not as clever as uberhbck but can be used to bootstrap repairs that uberhbck can complete.
+If the tool succeeds you should be able to start hbase and run online repairs if necessary.
+
+==== Special cases: Offline split parent
+
+Once a region is split, the offline parent will be cleaned up automatically.
+Sometimes, daughter regions are split again before their parents are cleaned up.
+HBase can clean up parents in the right order.
+However, there could be some lingering offline split parents sometimes.
+They are in META, in HDFS, and not deployed.
+But HBase can't clean them up.
+In this case, you can use the `-fixSplitParents` option to reset them in META to be online and not split.
+Therefore, hbck can merge them with other regions if fixing overlapping regions option is used.
+
+This option should not normally be used, and it is not in `-fixAll`.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/images
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diff --git a/src/main/asciidoc/_chapters/images b/src/main/asciidoc/_chapters/images
new file mode 120000
index 0000000..1e0c6c1
--- /dev/null
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@@ -0,0 +1 @@
+../../site/resources/images
\ No newline at end of file
[05/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/sql.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/sql.adoc b/src/main/asciidoc/_chapters/sql.adoc
new file mode 100644
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--- /dev/null
+++ b/src/main/asciidoc/_chapters/sql.adoc
@@ -0,0 +1,42 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[sql]]
+== SQL over HBase
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+The following projects offer some support for SQL over HBase.
+
+[[phoenix]]
+=== Apache Phoenix
+
+link:http://phoenix.apache.org[Apache Phoenix]
+
+=== Trafodion
+
+link:https://wiki.trafodion.org/[Trafodion: Transactional SQL-on-HBase]
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/thrift_filter_language.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/thrift_filter_language.adoc b/src/main/asciidoc/_chapters/thrift_filter_language.adoc
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+++ b/src/main/asciidoc/_chapters/thrift_filter_language.adoc
@@ -0,0 +1,279 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[thrift]]
+= Thrift API and Filter Language
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+
+Apache link:http://thrift.apache.org/[Thrift] is a cross-platform, cross-language development framework.
+HBase includes a Thrift API and filter language.
+The Thrift API relies on client and server processes.
+Documentation about the HBase Thrift API is located at http://wiki.apache.org/hadoop/Hbase/ThriftApi.
+
+You can configure Thrift for secure authentication at the server and client side, by following the procedures in <<security.client.thrift>> and <<security.gateway.thrift>>.
+
+The rest of this chapter discusses the filter language provided by the Thrift API.
+
+[[thrift.filter_language]]
+== Filter Language
+
+Thrift Filter Language was introduced in HBase 0.92.
+It allows you to perform server-side filtering when accessing HBase over Thrift or in the HBase shell.
+You can find out more about shell integration by using the `scan help` command in the shell.
+
+You specify a filter as a string, which is parsed on the server to construct the filter.
+
+[[general_syntax]]
+=== General Filter String Syntax
+
+A simple filter expression is expressed as a string:
+
+----
+“FilterName (argument, argument,... , argument)”
+----
+
+Keep the following syntax guidelines in mind.
+
+* Specify the name of the filter followed by the comma-separated argument list in parentheses.
+* If the argument represents a string, it should be enclosed in single quotes (`'`).
+* Arguments which represent a boolean, an integer, or a comparison operator (such as <, >, or !=), should not be enclosed in quotes
+* The filter name must be a single word.
+ All ASCII characters are allowed except for whitespace, single quotes and parentheses.
+* The filter's arguments can contain any ASCII character.
+ If single quotes are present in the argument, they must be escaped by an additional preceding single quote.
+
+=== Compound Filters and Operators
+
+.Binary Operators
+`AND`::
+ If the `AND` operator is used, the key-value must satisfy both filters.
+
+`OR`::
+ If the `OR` operator is used, the key-value must satisfy at least one of the filters.
+
+.Unary Operators
+`SKIP`::
+ For a particular row, if any of the key-values fail the filter condition, the entire row is skipped.
+
+`WHILE`::
+ For a particular row, key-values will be emitted until a key-value is reached that fails the filter condition.
+
+.Compound Operators
+====
+You can combine multiple operators to create a hierarchy of filters, such as the following example:
+[source]
+----
+(Filter1 AND Filter2) OR (Filter3 AND Filter4)
+----
+====
+
+=== Order of Evaluation
+
+. Parentheses have the highest precedence.
+. The unary operators `SKIP` and `WHILE` are next, and have the same precedence.
+. The binary operators follow. `AND` has highest precedence, followed by `OR`.
+
+.Precedence Example
+====
+[source]
+----
+Filter1 AND Filter2 OR Filter
+is evaluated as
+(Filter1 AND Filter2) OR Filter3
+----
+
+[source]
+----
+Filter1 AND SKIP Filter2 OR Filter3
+is evaluated as
+(Filter1 AND (SKIP Filter2)) OR Filter3
+----
+====
+
+You can use parentheses to explicitly control the order of evaluation.
+
+=== Compare Operator
+
+The following compare operators are provided:
+
+. LESS (<)
+. LESS_OR_EQUAL (<=)
+. EQUAL (=)
+. NOT_EQUAL (!=)
+. GREATER_OR_EQUAL (>=)
+. GREATER (>)
+. NO_OP (no operation)
+
+The client should use the symbols (<, <=, =, !=, >, >=) to express compare operators.
+
+=== Comparator
+
+A comparator can be any of the following:
+
+. _BinaryComparator_ - This lexicographically compares against the specified byte array using Bytes.compareTo(byte[], byte[])
+. _BinaryPrefixComparator_ - This lexicographically compares against a specified byte array.
+ It only compares up to the length of this byte array.
+. _RegexStringComparator_ - This compares against the specified byte array using the given regular expression.
+ Only EQUAL and NOT_EQUAL comparisons are valid with this comparator
+. _SubStringComparator_ - This tests if the given substring appears in a specified byte array.
+ The comparison is case insensitive.
+ Only EQUAL and NOT_EQUAL comparisons are valid with this comparator
+
+The general syntax of a comparator is: `ComparatorType:ComparatorValue`
+
+The ComparatorType for the various comparators is as follows:
+
+. _BinaryComparator_ - binary
+. _BinaryPrefixComparator_ - binaryprefix
+. _RegexStringComparator_ - regexstring
+. _SubStringComparator_ - substring
+
+The ComparatorValue can be any value.
+
+.Example ComparatorValues
+. `binary:abc` will match everything that is lexicographically greater than "abc"
+. `binaryprefix:abc` will match everything whose first 3 characters are lexicographically equal to "abc"
+. `regexstring:ab*yz` will match everything that doesn't begin with "ab" and ends with "yz"
+. `substring:abc123` will match everything that begins with the substring "abc123"
+
+[[examplephpclientprogram]]
+=== Example PHP Client Program that uses the Filter Language
+
+[source,php]
+----
+<?
+ $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
+ require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
+ flib_init(FLIB_CONTEXT_SCRIPT);
+ require_module('storage/hbase');
+ $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift server is running>);
+ $hbase->open();
+ $client = $hbase->getClient();
+ $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
+ $to_print = $client->scannerGetList($result,1);
+ while ($to_print) {
+ print_r($to_print);
+ $to_print = $client->scannerGetList($result,1);
+ }
+ $client->scannerClose($result);
+?>
+----
+
+=== Example Filter Strings
+
+* `"PrefixFilter ('Row') AND PageFilter (1) AND FirstKeyOnlyFilter ()"` will return all key-value pairs that match the following conditions:
++
+. The row containing the key-value should have prefix _Row_
+. The key-value must be located in the first row of the table
+. The key-value pair must be the first key-value in the row
++
+* `"(RowFilter (=, 'binary:Row 1') AND TimeStampsFilter (74689, 89734)) OR ColumnRangeFilter ('abc', true, 'xyz', false))"` will return all key-value pairs that match both the following conditions:
+** The key-value is in a row having row key _Row 1_
+** The key-value must have a timestamp of either 74689 or 89734.
+** Or it must match the following condition:
+*** The key-value pair must be in a column that is lexicographically >= abc and < xyz
++
+* `"SKIP ValueFilter (0)"` will skip the entire row if any of the values in the row is not 0
+
+[[individualfiltersyntax]]
+=== Individual Filter Syntax
+
+KeyOnlyFilter::
+ This filter doesn't take any arguments.
+ It returns only the key component of each key-value.
+
+FirstKeyOnlyFilter::
+ This filter doesn't take any arguments.
+ It returns only the first key-value from each row.
+
+PrefixFilter::
+ This filter takes one argument – a prefix of a row key.
+ It returns only those key-values present in a row that starts with the specified row prefix
+
+ColumnPrefixFilter::
+ This filter takes one argument – a column prefix.
+ It returns only those key-values present in a column that starts with the specified column prefix.
+ The column prefix must be of the form: `“qualifier”`.
+
+MultipleColumnPrefixFilter::
+ This filter takes a list of column prefixes.
+ It returns key-values that are present in a column that starts with any of the specified column prefixes.
+ Each of the column prefixes must be of the form: `“qualifier”`.
+
+ColumnCountGetFilter::
+ This filter takes one argument – a limit.
+ It returns the first limit number of columns in the table.
+
+PageFilter::
+ This filter takes one argument – a page size.
+ It returns page size number of rows from the table.
+
+ColumnPaginationFilter::
+ This filter takes two arguments – a limit and offset.
+ It returns limit number of columns after offset number of columns.
+ It does this for all the rows.
+
+InclusiveStopFilter::
+ This filter takes one argument – a row key on which to stop scanning.
+ It returns all key-values present in rows up to and including the specified row.
+
+TimeStampsFilter::
+ This filter takes a list of timestamps.
+ It returns those key-values whose timestamps matches any of the specified timestamps.
+
+RowFilter::
+ This filter takes a compare operator and a comparator.
+ It compares each row key with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that row.
+
+Family Filter::
+ This filter takes a compare operator and a comparator.
+ It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column.
+
+QualifierFilter::
+ This filter takes a compare operator and a comparator.
+ It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column.
+
+ValueFilter::
+ This filter takes a compare operator and a comparator.
+ It compares each value with the comparator using the compare operator and if the comparison returns true, it returns that key-value.
+
+DependentColumnFilter::
+ This filter takes two arguments – a family and a qualifier.
+ It tries to locate this column in each row and returns all key-values in that row that have the same timestamp.
+ If the row doesn't contain the specified column – none of the key-values in that row will be returned.
+
+SingleColumnValueFilter::
+ This filter takes a column family, a qualifier, a compare operator and a comparator.
+ If the specified column is not found – all the columns of that row will be emitted.
+ If the column is found and the comparison with the comparator returns true, all the columns of the row will be emitted.
+ If the condition fails, the row will not be emitted.
+
+SingleColumnValueExcludeFilter::
+ This filter takes the same arguments and behaves same as SingleColumnValueFilter – however, if the column is found and the condition passes, all the columns of the row will be emitted except for the tested column value.
+
+ColumnRangeFilter::
+ This filter is used for selecting only those keys with columns that are between minColumn and maxColumn.
+ It also takes two boolean variables to indicate whether to include the minColumn and maxColumn or not.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/tracing.adoc
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diff --git a/src/main/asciidoc/_chapters/tracing.adoc b/src/main/asciidoc/_chapters/tracing.adoc
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@@ -0,0 +1,194 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+
+[appendix]
+[[tracing]]
+== Enabling Dapper-like Tracing in HBase
+
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+link:https://issues.apache.org/jira/browse/HBASE-6449[HBASE-6449] added support for tracing requests through HBase, using the open source tracing library, link:http://github.com/cloudera/htrace[HTrace].
+Setting up tracing is quite simple, however it currently requires some very minor changes to your client code (it would not be very difficult to remove this requirement).
+
+[[tracing.spanreceivers]]
+=== SpanReceivers
+
+The tracing system works by collecting information in structs called 'Spans'. It is up to you to choose how you want to receive this information by implementing the `SpanReceiver` interface, which defines one method:
+
+[source]
+----
+
+public void receiveSpan(Span span);
+----
+
+This method serves as a callback whenever a span is completed.
+HTrace allows you to use as many SpanReceivers as you want so you can easily send trace information to multiple destinations.
+
+Configure what SpanReceivers you'd like to us by putting a comma separated list of the fully-qualified class name of classes implementing `SpanReceiver` in _hbase-site.xml_ property: `hbase.trace.spanreceiver.classes`.
+
+HTrace includes a `LocalFileSpanReceiver` that writes all span information to local files in a JSON-based format.
+The `LocalFileSpanReceiver` looks in _hbase-site.xml_ for a `hbase.local-file-span-receiver.path` property with a value describing the name of the file to which nodes should write their span information.
+
+[source]
+----
+
+<property>
+ <name>hbase.trace.spanreceiver.classes</name>
+ <value>org.htrace.impl.LocalFileSpanReceiver</value>
+</property>
+<property>
+ <name>hbase.local-file-span-receiver.path</name>
+ <value>/var/log/hbase/htrace.out</value>
+</property>
+----
+
+HTrace also provides `ZipkinSpanReceiver` which converts spans to link:http://github.com/twitter/zipkin[Zipkin] span format and send them to Zipkin server.
+In order to use this span receiver, you need to install the jar of htrace-zipkin to your HBase's classpath on all of the nodes in your cluster.
+
+_htrace-zipkin_ is published to the maven central repository.
+You could get the latest version from there or just build it locally and then copy it out to all nodes, change your config to use zipkin receiver, distribute the new configuration and then (rolling) restart.
+
+Here is the example of manual setup procedure.
+
+----
+
+$ git clone https://github.com/cloudera/htrace
+$ cd htrace/htrace-zipkin
+$ mvn compile assembly:single
+$ cp target/htrace-zipkin-*-jar-with-dependencies.jar $HBASE_HOME/lib/
+ # copy jar to all nodes...
+----
+
+The `ZipkinSpanReceiver` looks in _hbase-site.xml_ for a `hbase.zipkin.collector-hostname` and `hbase.zipkin.collector-port` property with a value describing the Zipkin collector server to which span information are sent.
+
+[source,xml]
+----
+
+<property>
+ <name>hbase.trace.spanreceiver.classes</name>
+ <value>org.htrace.impl.ZipkinSpanReceiver</value>
+</property>
+<property>
+ <name>hbase.zipkin.collector-hostname</name>
+ <value>localhost</value>
+</property>
+<property>
+ <name>hbase.zipkin.collector-port</name>
+ <value>9410</value>
+</property>
+----
+
+If you do not want to use the included span receivers, you are encouraged to write your own receiver (take a look at `LocalFileSpanReceiver` for an example). If you think others would benefit from your receiver, file a JIRA or send a pull request to link:http://github.com/cloudera/htrace[HTrace].
+
+[[tracing.client.modifications]]
+== Client Modifications
+
+In order to turn on tracing in your client code, you must initialize the module sending spans to receiver once per client process.
+
+[source,java]
+----
+
+private SpanReceiverHost spanReceiverHost;
+
+...
+
+ Configuration conf = HBaseConfiguration.create();
+ SpanReceiverHost spanReceiverHost = SpanReceiverHost.getInstance(conf);
+----
+
+Then you simply start tracing span before requests you think are interesting, and close it when the request is done.
+For example, if you wanted to trace all of your get operations, you change this:
+
+[source,java]
+----
+Configuration config = HBaseConfiguration.create();
+Connection connection = ConnectionFactory.createConnection(config);
+Table table = connection.getTable(TableName.valueOf("t1"));
+Get get = new Get(Bytes.toBytes("r1"));
+Result res = table.get(get);
+----
+
+into:
+
+[source,java]
+----
+
+TraceScope ts = Trace.startSpan("Gets", Sampler.ALWAYS);
+try {
+ Table table = connection.getTable(TableName.valueOf("t1"));
+ Get get = new Get(Bytes.toBytes("r1"));
+ Result res = table.get(get);
+} finally {
+ ts.close();
+}
+----
+
+If you wanted to trace half of your 'get' operations, you would pass in:
+
+[source,java]
+----
+
+new ProbabilitySampler(0.5)
+----
+
+in lieu of `Sampler.ALWAYS` to `Trace.startSpan()`.
+See the HTrace _README_ for more information on Samplers.
+
+[[tracing.client.shell]]
+== Tracing from HBase Shell
+
+You can use +trace+ command for tracing requests from HBase Shell. +trace 'start'+ command turns on tracing and +trace
+ 'stop'+ command turns off tracing.
+
+[source]
+----
+
+hbase(main):001:0> trace 'start'
+hbase(main):002:0> put 'test', 'row1', 'f:', 'val1' # traced commands
+hbase(main):003:0> trace 'stop'
+----
+
++trace 'start'+ and +trace 'stop'+ always returns boolean value representing if or not there is ongoing tracing.
+As a result, +trace
+ 'stop'+ returns false on suceess. +trace 'status'+ just returns if or not tracing is turned on.
+
+[source]
+----
+
+hbase(main):001:0> trace 'start'
+=> true
+
+hbase(main):002:0> trace 'status'
+=> true
+
+hbase(main):003:0> trace 'stop'
+=> false
+
+hbase(main):004:0> trace 'status'
+=> false
+----
+
+:numbered:
[07/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/schema_design.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/schema_design.adoc b/src/main/asciidoc/_chapters/schema_design.adoc
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index 0000000..9319c65
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@@ -0,0 +1,1048 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[schema]]
+= HBase and Schema Design
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+A good general introduction on the strength and weaknesses modelling on the various non-rdbms datastores is Ian Varley's Master thesis, link:http://ianvarley.com/UT/MR/Varley_MastersReport_Full_2009-08-07.pdf[No Relation: The Mixed Blessings of Non-Relational Databases]. Also, read <<keyvalue,keyvalue>> for how HBase stores data internally, and the section on <<schema.casestudies,schema.casestudies>>.
+
+[[schema.creation]]
+== Schema Creation
+
+HBase schemas can be created or updated using the <<shell>> or by using link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html[Admin] in the Java API.
+
+Tables must be disabled when making ColumnFamily modifications, for example:
+
+[source,java]
+----
+
+Configuration config = HBaseConfiguration.create();
+Admin admin = new Admin(conf);
+String table = "myTable";
+
+admin.disableTable(table);
+
+HColumnDescriptor cf1 = ...;
+admin.addColumn(table, cf1); // adding new ColumnFamily
+HColumnDescriptor cf2 = ...;
+admin.modifyColumn(table, cf2); // modifying existing ColumnFamily
+
+admin.enableTable(table);
+----
+
+See <<client_dependencies,client dependencies>> for more information about configuring client connections.
+
+NOTE: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase requires the table to be disabled.
+
+[[schema.updates]]
+=== Schema Updates
+
+When changes are made to either Tables or ColumnFamilies (e.g. region size, block size), these changes take effect the next time there is a major compaction and the StoreFiles get re-written.
+
+See <<store,store>> for more information on StoreFiles.
+
+[[number.of.cfs]]
+== On the number of column families
+
+HBase currently does not do well with anything above two or three column families so keep the number of column families in your schema low.
+Currently, flushing and compactions are done on a per Region basis so if one column family is carrying the bulk of the data bringing on flushes, the adjacent families will also be flushed even though the amount of data they carry is small.
+When many column families exist the flushing and compaction interaction can make for a bunch of needless i/o (To be addressed by changing flushing and compaction to work on a per column family basis). For more information on compactions, see <<compaction>>.
+
+Try to make do with one column family if you can in your schemas.
+Only introduce a second and third column family in the case where data access is usually column scoped; i.e.
+you query one column family or the other but usually not both at the one time.
+
+[[number.of.cfs.card]]
+=== Cardinality of ColumnFamilies
+
+Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA's data will likely be spread across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient.
+
+[[rowkey.design]]
+== Rowkey Design
+
+=== Hotspotting
+
+Rows in HBase are sorted lexicographically by row key.
+This design optimizes for scans, allowing you to store related rows, or rows that will be read together, near each other.
+However, poorly designed row keys are a common source of [firstterm]_hotspotting_.
+Hotspotting occurs when a large amount of client traffic is directed at one node, or only a few nodes, of a cluster.
+This traffic may represent reads, writes, or other operations.
+The traffic overwhelms the single machine responsible for hosting that region, causing performance degradation and potentially leading to region unavailability.
+This can also have adverse effects on other regions hosted by the same region server as that host is unable to service the requested load.
+It is important to design data access patterns such that the cluster is fully and evenly utilized.
+
+To prevent hotspotting on writes, design your row keys such that rows that truly do need to be in the same region are, but in the bigger picture, data is being written to multiple regions across the cluster, rather than one at a time.
+Some common techniques for avoiding hotspotting are described below, along with some of their advantages and drawbacks.
+
+.Salting
+Salting in this sense has nothing to do with cryptography, but refers to adding random data to the start of a row key.
+In this case, salting refers to adding a randomly-assigned prefix to the row key to cause it to sort differently than it otherwise would.
+The number of possible prefixes correspond to the number of regions you want to spread the data across.
+Salting can be helpful if you have a few "hot" row key patterns which come up over and over amongst other more evenly-distributed rows.
+Consider the following example, which shows that salting can spread write load across multiple RegionServers, and illustrates some of the negative implications for reads.
+
+.Salting Example
+====
+Suppose you have the following list of row keys, and your table is split such that there is one region for each letter of the alphabet.
+Prefix 'a' is one region, prefix 'b' is another.
+In this table, all rows starting with 'f' are in the same region.
+This example focuses on rows with keys like the following:
+
+----
+
+foo0001
+foo0002
+foo0003
+foo0004
+----
+
+Now, imagine that you would like to spread these across four different regions.
+You decide to use four different salts: `a`, `b`, `c`, and `d`.
+In this scenario, each of these letter prefixes will be on a different region.
+After applying the salts, you have the following rowkeys instead.
+Since you can now write to four separate regions, you theoretically have four times the throughput when writing that you would have if all the writes were going to the same region.
+
+----
+
+a-foo0003
+b-foo0001
+c-foo0004
+d-foo0002
+----
+
+Then, if you add another row, it will randomly be assigned one of the four possible salt values and end up near one of the existing rows.
+
+----
+
+a-foo0003
+b-foo0001
+c-foo0003
+c-foo0004
+d-foo0002
+----
+
+Since this assignment will be random, you will need to do more work if you want to retrieve the rows in lexicographic order.
+In this way, salting attempts to increase throughput on writes, but has a cost during reads.
+====
+
+
+
+.Hashing
+Instead of a random assignment, you could use a one-way [firstterm]_hash_ that would cause a given row to always be "salted" with the same prefix, in a way that would spread the load across the RegionServers, but allow for predictability during reads.
+Using a deterministic hash allows the client to reconstruct the complete rowkey and use a Get operation to retrieve that row as normal.
+
+.Hashing Example
+[example]
+Given the same situation in the salting example above, you could instead apply a one-way hash that would cause the row with key `foo0003` to always, and predictably, receive the `a` prefix.
+Then, to retrieve that row, you would already know the key.
+You could also optimize things so that certain pairs of keys were always in the same region, for instance.
+
+.Reversing the Key
+A third common trick for preventing hotspotting is to reverse a fixed-width or numeric row key so that the part that changes the most often (the least significant digit) is first.
+This effectively randomizes row keys, but sacrifices row ordering properties.
+
+See https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-on-designing-hbase-tables, and link:http://phoenix.apache.org/salted.html[article on Salted Tables] from the Phoenix project, and the discussion in the comments of link:https://issues.apache.org/jira/browse/HBASE-11682[HBASE-11682] for more information about avoiding hotspotting.
+
+[[timeseries]]
+=== Monotonically Increasing Row Keys/Timeseries Data
+
+In the HBase chapter of Tom White's book link:http://oreilly.com/catalog/9780596521981[Hadoop: The Definitive Guide] (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table's regions (and thus, a single node), then moving onto the next region, etc.
+With monotonically increasing row-keys (i.e., using a timestamp), this will happen.
+See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores: link:http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/[monotonically increasing values are bad].
+The pile-up on a single region brought on by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general it's best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key.
+
+If you do need to upload time series data into HBase, you should study link:http://opentsdb.net/[OpenTSDB] as a successful example.
+It has a page describing the link: http://opentsdb.net/schema.html[schema] it uses in HBase.
+The key format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at first glance to contradict the previous advice about not using a timestamp as the key.
+However, the difference is that the timestamp is not in the _lead_ position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types.
+Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table.
+
+See <<schema.casestudies,schema.casestudies>> for some rowkey design examples.
+
+[[keysize]]
+=== Try to minimize row and column sizes
+
+In HBase, values are always freighted with their coordinates; as a cell value passes through the system, it'll be accompanied by its row, column name, and timestamp - always.
+If your rows and column names are large, especially compared to the size of the cell value, then you may run up against some interesting scenarios.
+One such is the case described by Marc Limotte at the tail of link:https://issues.apache.org/jira/browse/HBASE-3551?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13005272#comment-13005272[HBASE-3551] (recommended!). Therein, the indices that are kept on HBase storefiles (<<hfile>>) to facilitate random access may end up occupying large chunks of the HBase allotted RAM because the cell value coordinates are large.
+Mark in the above cited comment suggests upping the block size so entries in the store file index happen at a larger interval or modify the table schema so it makes for smaller rows and column names.
+Compression will also make for larger indices.
+See the thread link:http://search-hadoop.com/m/hemBv1LiN4Q1/a+question+storefileIndexSize&subj=a+question+storefileIndexSize[a question storefileIndexSize] up on the user mailing list.
+
+Most of the time small inefficiencies don't matter all that much. Unfortunately, this is a case where they do.
+Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated several billion times in your data.
+
+See <<keyvalue,keyvalue>> for more information on HBase stores data internally to see why this is important.
+
+[[keysize.cf]]
+==== Column Families
+
+Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default).
+
+See <<keyvalue>> for more information on HBase stores data internally to see why this is important.
+
+[[keysize.attributes]]
+==== Attributes
+
+Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via") to store in HBase.
+
+See <<keyvalue,keyvalue>> for more information on HBase stores data internally to see why this is important.
+
+[[keysize.row]]
+==== Rowkey Length
+
+Keep them as short as is reasonable such that they can still be useful for required data access (e.g. Get vs.
+Scan). A short key that is useless for data access is not better than a longer key with better get/scan properties.
+Expect tradeoffs when designing rowkeys.
+
+[[keysize.patterns]]
+==== Byte Patterns
+
+A long is 8 bytes.
+You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes.
+If you stored this number as a String -- presuming a byte per character -- you need nearly 3x the bytes.
+
+Not convinced? Below is some sample code that you can run on your own.
+
+[source,java]
+----
+
+// long
+//
+long l = 1234567890L;
+byte[] lb = Bytes.toBytes(l);
+System.out.println("long bytes length: " + lb.length); // returns 8
+
+String s = String.valueOf(l);
+byte[] sb = Bytes.toBytes(s);
+System.out.println("long as string length: " + sb.length); // returns 10
+
+// hash
+//
+MessageDigest md = MessageDigest.getInstance("MD5");
+byte[] digest = md.digest(Bytes.toBytes(s));
+System.out.println("md5 digest bytes length: " + digest.length); // returns 16
+
+String sDigest = new String(digest);
+byte[] sbDigest = Bytes.toBytes(sDigest);
+System.out.println("md5 digest as string length: " + sbDigest.length); // returns 26
+----
+
+Unfortunately, using a binary representation of a type will make your data harder to read outside of your code.
+For example, this is what you will see in the shell when you increment a value:
+
+[source]
+----
+
+hbase(main):001:0> incr 't', 'r', 'f:q', 1
+COUNTER VALUE = 1
+
+hbase(main):002:0> get 't', 'r'
+COLUMN CELL
+ f:q timestamp=1369163040570, value=\x00\x00\x00\x00\x00\x00\x00\x01
+1 row(s) in 0.0310 seconds
+----
+
+The shell makes a best effort to print a string, and it this case it decided to just print the hex.
+The same will happen to your row keys inside the region names.
+It can be okay if you know what's being stored, but it might also be unreadable if arbitrary data can be put in the same cells.
+This is the main trade-off.
+
+[[reverse.timestamp]]
+=== Reverse Timestamps
+
+.Reverse Scan API
+[NOTE]
+====
+link:https://issues.apache.org/jira/browse/HBASE-4811[HBASE-4811] implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning.
+This feature is available in HBase 0.98 and later.
+See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setReversed%28boolean for more information.
+====
+
+A common problem in database processing is quickly finding the most recent version of a value.
+A technique using reverse timestamps as a part of the key can help greatly with a special case of this problem.
+Also found in the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly), the technique involves appending (`Long.MAX_VALUE - timestamp`) to the end of any key, e.g. [key][reverse_timestamp].
+
+The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record.
+Since HBase keys are in sorted order, this key sorts before any older row-keys for [key] and thus is first.
+
+This technique would be used instead of using <<schema.versions>> where the intent is to hold onto all versions "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique.
+
+[[rowkey.scope]]
+=== Rowkeys and ColumnFamilies
+
+Rowkeys are scoped to ColumnFamilies.
+Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision.
+
+[[changing.rowkeys]]
+=== Immutability of Rowkeys
+
+Rowkeys cannot be changed.
+The only way they can be "changed" in a table is if the row is deleted and then re-inserted.
+This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you've inserted a lot of data).
+
+[[rowkey.regionsplits]]
+=== Relationship Between RowKeys and Region Splits
+
+If you pre-split your table, it is _critical_ to understand how your rowkey will be distributed across the region boundaries.
+As an example of why this is important, consider the example of using displayable hex characters as the lead position of the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those key ranges through `Bytes.split` (which is the split strategy used when creating regions in `Admin.createTable(byte[] startKey, byte[] endKey, numRegions)` for 10 regions will generate the following splits...
+
+----
+
+48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 // 0
+54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 // 6
+61 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -68 // =
+68 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -126 // D
+75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 72 // K
+82 18 18 18 18 18 18 18 18 18 18 18 18 18 18 14 // R
+88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44 // X
+95 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -102 // _
+102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 // f
+----
+
+(note: the lead byte is listed to the right as a comment.) Given that the first split is a '0' and the last split is an 'f', everything is great, right? Not so fast.
+
+The problem is that all the data is going to pile up in the first 2 regions and the last region thus creating a "lumpy" (and possibly "hot") region problem.
+To understand why, refer to an link:http://www.asciitable.com[ASCII Table].
+'0' is byte 48, and 'f' is byte 102, but there is a huge gap in byte values (bytes 58 to 96) that will _never appear in this keyspace_ because the only values are [0-9] and [a-f]. Thus, the middle regions regions will never be used.
+To make pre-spliting work with this example keyspace, a custom definition of splits (i.e., and not relying on the built-in split method) is required.
+
+Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split them in such a way that all the regions are accessible in the keyspace.
+While this example demonstrated the problem with a hex-key keyspace, the same problem can happen with _any_ keyspace.
+Know your data.
+
+Lesson #2: While generally not advisable, using hex-keys (and more generally, displayable data) can still work with pre-split tables as long as all the created regions are accessible in the keyspace.
+
+To conclude this example, the following is an example of how appropriate splits can be pre-created for hex-keys:.
+
+[source,java]
+----
+public static boolean createTable(Admin admin, HTableDescriptor table, byte[][] splits)
+throws IOException {
+ try {
+ admin.createTable( table, splits );
+ return true;
+ } catch (TableExistsException e) {
+ logger.info("table " + table.getNameAsString() + " already exists");
+ // the table already exists...
+ return false;
+ }
+}
+
+public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
+ byte[][] splits = new byte[numRegions-1][];
+ BigInteger lowestKey = new BigInteger(startKey, 16);
+ BigInteger highestKey = new BigInteger(endKey, 16);
+ BigInteger range = highestKey.subtract(lowestKey);
+ BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
+ lowestKey = lowestKey.add(regionIncrement);
+ for(int i=0; i < numRegions-1;i++) {
+ BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
+ byte[] b = String.format("%016x", key).getBytes();
+ splits[i] = b;
+ }
+ return splits;
+}
+----
+
+[[schema.versions]]
+== Number of Versions
+
+[[schema.versions.max]]
+=== Maximum Number of Versions
+
+The maximum number of row versions to store is configured per column family via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor].
+The default for max versions is 1.
+This is an important parameter because as described in <<datamodel>> section HBase does _not_ overwrite row values, but rather stores different values per row by time (and qualifier). Excess versions are removed during major compactions.
+The number of max versions may need to be increased or decreased depending on application needs.
+
+It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are very dear to you because this will greatly increase StoreFile size.
+
+[[schema.minversions]]
+=== Minimum Number of Versions
+
+Like maximum number of row versions, the minimum number of row versions to keep is configured per column family via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor].
+The default for min versions is 0, which means the feature is disabled.
+The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the number of row versions parameter to allow configurations such as "keep the last T minutes worth of data, at most N versions, _but keep at least M versions around_" (where M is the value for minimum number of row versions, M<N). This parameter should only be set when time-to-live is enabled for a column family and must be less than the number of row versions.
+
+[[supported.datatypes]]
+== Supported Datatypes
+
+HBase supports a "bytes-in/bytes-out" interface via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Put.html[Put] and link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Result.html[Result], so anything that can be converted to an array of bytes can be stored as a value.
+Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes.
+
+There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask); search the mailling list for conversations on this topic.
+All rows in HBase conform to the <<datamodel>>, and that includes versioning.
+Take that into consideration when making your design, as well as block size for the ColumnFamily.
+
+=== Counters
+
+One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#increment%28org.apache.hadoop.hbase.client.Increment%29[Increment] in `Table`.
+
+Synchronization on counters are done on the RegionServer, not in the client.
+
+[[schema.joins]]
+== Joins
+
+If you have multiple tables, don't forget to factor in the potential for <<joins>> into the schema design.
+
+[[ttl]]
+== Time To Live (TTL)
+
+ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached.
+This applies to _all_ versions of a row - even the current one.
+The TTL time encoded in the HBase for the row is specified in UTC.
+
+Store files which contains only expired rows are deleted on minor compaction.
+Setting `hbase.store.delete.expired.storefile` to `false` disables this feature.
+Setting minimum number of versions to other than 0 also disables this.
+
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor] for more information.
+
+Recent versions of HBase also support setting time to live on a per cell basis.
+See link:https://issues.apache.org/jira/browse/HBASE-10560[HBASE-10560] for more information.
+Cell TTLs are submitted as an attribute on mutation requests (Appends, Increments, Puts, etc.) using Mutation#setTTL.
+If the TTL attribute is set, it will be applied to all cells updated on the server by the operation.
+There are two notable differences between cell TTL handling and ColumnFamily TTLs:
+
+* Cell TTLs are expressed in units of milliseconds instead of seconds.
+* A cell TTLs cannot extend the effective lifetime of a cell beyond a ColumnFamily level TTL setting.
+
+[[cf.keep.deleted]]
+== Keeping Deleted Cells
+
+By default, delete markers extend back to the beginning of time.
+Therefore, link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] or link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] operations will not see a deleted cell (row or column), even when the Get or Scan operation indicates a time range before the delete marker was placed.
+
+ColumnFamilies can optionally keep deleted cells.
+In this case, deleted cells can still be retrieved, as long as these operations specify a time range that ends before the timestamp of any delete that would affect the cells.
+This allows for point-in-time queries even in the presence of deletes.
+
+Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells.
+A new "raw" scan options returns all deleted rows and the delete markers.
+
+.Change the Value of `KEEP_DELETED_CELLS` Using HBase Shell
+====
+----
+hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true
+----
+====
+
+.Change the Value of `KEEP_DELETED_CELLS` Using the API
+====
+[source,java]
+----
+...
+HColumnDescriptor.setKeepDeletedCells(true);
+...
+----
+====
+
+Let us illustrate the basic effect of setting the `KEEP_DELETED_CELLS` attribute on a table.
+
+First, without:
+[source]
+----
+create 'test', {NAME=>'e', VERSIONS=>2147483647}
+put 'test', 'r1', 'e:c1', 'value', 10
+put 'test', 'r1', 'e:c1', 'value', 12
+put 'test', 'r1', 'e:c1', 'value', 14
+delete 'test', 'r1', 'e:c1', 11
+
+hbase(main):017:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+ r1 column=e:c1, timestamp=11, type=DeleteColumn
+ r1 column=e:c1, timestamp=10, value=value
+1 row(s) in 0.0120 seconds
+
+hbase(main):018:0> flush 'test'
+0 row(s) in 0.0350 seconds
+
+hbase(main):019:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+ r1 column=e:c1, timestamp=11, type=DeleteColumn
+1 row(s) in 0.0120 seconds
+
+hbase(main):020:0> major_compact 'test'
+0 row(s) in 0.0260 seconds
+
+hbase(main):021:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+1 row(s) in 0.0120 seconds
+----
+
+Notice how delete cells are let go.
+
+Now lets run the same test only with `KEEP_DELETED_CELLS` set on the table (you can do table or per-column-family):
+
+[source]
+----
+hbase(main):005:0> create 'test', {NAME=>'e', VERSIONS=>2147483647, KEEP_DELETED_CELLS => true}
+0 row(s) in 0.2160 seconds
+
+=> Hbase::Table - test
+hbase(main):006:0> put 'test', 'r1', 'e:c1', 'value', 10
+0 row(s) in 0.1070 seconds
+
+hbase(main):007:0> put 'test', 'r1', 'e:c1', 'value', 12
+0 row(s) in 0.0140 seconds
+
+hbase(main):008:0> put 'test', 'r1', 'e:c1', 'value', 14
+0 row(s) in 0.0160 seconds
+
+hbase(main):009:0> delete 'test', 'r1', 'e:c1', 11
+0 row(s) in 0.0290 seconds
+
+hbase(main):010:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+ r1 column=e:c1, timestamp=11, type=DeleteColumn
+ r1 column=e:c1, timestamp=10, value=value
+1 row(s) in 0.0550 seconds
+
+hbase(main):011:0> flush 'test'
+0 row(s) in 0.2780 seconds
+
+hbase(main):012:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+ r1 column=e:c1, timestamp=11, type=DeleteColumn
+ r1 column=e:c1, timestamp=10, value=value
+1 row(s) in 0.0620 seconds
+
+hbase(main):013:0> major_compact 'test'
+0 row(s) in 0.0530 seconds
+
+hbase(main):014:0> scan 'test', {RAW=>true, VERSIONS=>1000}
+ROW COLUMN+CELL
+ r1 column=e:c1, timestamp=14, value=value
+ r1 column=e:c1, timestamp=12, value=value
+ r1 column=e:c1, timestamp=11, type=DeleteColumn
+ r1 column=e:c1, timestamp=10, value=value
+1 row(s) in 0.0650 seconds
+----
+
+KEEP_DELETED_CELLS is to avoid removing Cells from HBase when the _only_ reason to remove them is the delete marker.
+So with KEEP_DELETED_CELLS enabled deleted cells would get removed if either you write more versions than the configured max, or you have a TTL and Cells are in excess of the configured timeout, etc.
+
+
+[[secondary.indexes]]
+== Secondary Indexes and Alternate Query Paths
+
+This section could also be titled "what if my table rowkey looks like _this_ but I also want to query my table like _that_." A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are reporting requirements on activity across users for certain time ranges.
+Thus, selecting by user is easy because it is in the lead position of the key, but time is not.
+
+There is no single answer on the best way to handle this because it depends on...
+
+* Number of users
+* Data size and data arrival rate
+* Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)
+* Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)
+
+and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution.
+Common techniques are in sub-sections below.
+This is a comprehensive, but not exhaustive, list of approaches.
+
+It should not be a surprise that secondary indexes require additional cluster space and processing.
+This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update.
+RDBMS products are more advanced in this regard to handle alternative index management out of the box.
+However, HBase scales better at larger data volumes, so this is a feature trade-off.
+
+Pay attention to <<performance>> when implementing any of these approaches.
+
+Additionally, see the David Butler response in this dist-list thread link:http://search-hadoop.com/m/nvbiBp2TDP/Stargate%252Bhbase&subj=Stargate+hbase[HBase, mail # user - Stargate+hbase]
+
+[[secondary.indexes.filter]]
+=== Filter Query
+
+Depending on the case, it may be appropriate to use <<client.filter>>.
+In this case, no secondary index is created.
+However, don't try a full-scan on a large table like this from an application (i.e., single-threaded client).
+
+[[secondary.indexes.periodic]]
+=== Periodic-Update Secondary Index
+
+A secondary index could be created in an other table which is periodically updated via a MapReduce job.
+The job could be executed intra-day, but depending on load-strategy it could still potentially be out of sync with the main data table.
+
+See <<mapreduce.example.readwrite,mapreduce.example.readwrite>> for more information.
+
+[[secondary.indexes.dualwrite]]
+=== Dual-Write Secondary Index
+
+Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table). If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see <<secondary.indexes.periodic,secondary.indexes.periodic>>).
+
+[[secondary.indexes.summary]]
+=== Summary Tables
+
+Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach.
+These would be generated with MapReduce jobs into another table.
+
+See <<mapreduce.example.summary,mapreduce.example.summary>> for more information.
+
+[[secondary.indexes.coproc]]
+=== Coprocessor Secondary Index
+
+Coprocessors act like RDBMS triggers. These were added in 0.92.
+For more information, see <<coprocessors,coprocessors>>
+
+== Constraints
+
+HBase currently supports 'constraints' in traditional (SQL) database parlance.
+The advised usage for Constraints is in enforcing business rules for attributes in the table (e.g. make sure values are in the range 1-10). Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled.
+Extensive documentation on using Constraints can be found at: link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/constraint[Constraint] since version 0.94.
+
+[[schema.casestudies]]
+== Schema Design Case Studies
+
+The following will describe some typical data ingestion use-cases with HBase, and how the rowkey design and construction can be approached.
+Note: this is just an illustration of potential approaches, not an exhaustive list.
+Know your data, and know your processing requirements.
+
+It is highly recommended that you read the rest of the <<schema>> first, before reading these case studies.
+
+The following case studies are described:
+
+* Log Data / Timeseries Data
+* Log Data / Timeseries on Steroids
+* Customer/Order
+* Tall/Wide/Middle Schema Design
+* List Data
+
+[[schema.casestudies.log_timeseries]]
+=== Case Study - Log Data and Timeseries Data
+
+Assume that the following data elements are being collected.
+
+* Hostname
+* Timestamp
+* Log event
+* Value/message
+
+We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From these attributes the rowkey will be some combination of hostname, timestamp, and log-event - but what specifically?
+
+[[schema.casestudies.log_timeseries.tslead]]
+==== Timestamp In The Rowkey Lead Position
+
+The rowkey `[timestamp][hostname][log-event]` suffers from the monotonically increasing rowkey problem described in <<timeseries>>.
+
+There is another pattern frequently mentioned in the dist-lists about "bucketing" timestamps, by performing a mod operation on the timestamp.
+If time-oriented scans are important, this could be a useful approach.
+Attention must be paid to the number of buckets, because this will require the same number of scans to return results.
+
+[source,java]
+----
+
+long bucket = timestamp % numBuckets;
+----
+
+to construct:
+
+[source]
+----
+
+[bucket][timestamp][hostname][log-event]
+----
+
+As stated above, to select data for a particular timerange, a Scan will need to be performed for each bucket.
+100 buckets, for example, will provide a wide distribution in the keyspace but it will require 100 Scans to obtain data for a single timestamp, so there are trade-offs.
+
+[[schema.casestudies.log_timeseries.hostlead]]
+==== Host In The Rowkey Lead Position
+
+The rowkey `[hostname][log-event][timestamp]` is a candidate if there is a large-ish number of hosts to spread the writes and reads across the keyspace.
+This approach would be useful if scanning by hostname was a priority.
+
+[[schema.casestudies.log_timeseries.revts]]
+==== Timestamp, or Reverse Timestamp?
+
+If the most important access path is to pull most recent events, then storing the timestamps as reverse-timestamps (e.g., `timestamp = Long.MAX_VALUE – timestamp`) will create the property of being able to do a Scan on `[hostname][log-event]` to obtain the quickly obtain the most recently captured events.
+
+Neither approach is wrong, it just depends on what is most appropriate for the situation.
+
+.Reverse Scan API
+[NOTE]
+====
+link:https://issues.apache.org/jira/browse/HBASE-4811[HBASE-4811] implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning.
+This feature is available in HBase 0.98 and later.
+See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setReversed%28boolean for more information.
+====
+
+[[schema.casestudies.log_timeseries.varkeys]]
+==== Variangle Length or Fixed Length Rowkeys?
+
+It is critical to remember that rowkeys are stamped on every column in HBase.
+If the hostname is `a` and the event type is `e1` then the resulting rowkey would be quite small.
+However, what if the ingested hostname is `myserver1.mycompany.com` and the event type is `com.package1.subpackage2.subsubpackage3.ImportantService`?
+
+It might make sense to use some substitution in the rowkey.
+There are at least two approaches: hashed and numeric.
+In the Hostname In The Rowkey Lead Position example, it might look like this:
+
+Composite Rowkey With Hashes:
+
+* [MD5 hash of hostname] = 16 bytes
+* [MD5 hash of event-type] = 16 bytes
+* [timestamp] = 8 bytes
+
+Composite Rowkey With Numeric Substitution:
+
+For this approach another lookup table would be needed in addition to LOG_DATA, called LOG_TYPES.
+The rowkey of LOG_TYPES would be:
+
+* [type] (e.g., byte indicating hostname vs. event-type)
+* [bytes] variable length bytes for raw hostname or event-type.
+
+A column for this rowkey could be a long with an assigned number, which could be obtained by using an link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#incrementColumnValue%28byte[],%20byte[],%20byte[],%20long%29[HBase counter].
+
+So the resulting composite rowkey would be:
+
+* [substituted long for hostname] = 8 bytes
+* [substituted long for event type] = 8 bytes
+* [timestamp] = 8 bytes
+
+In either the Hash or Numeric substitution approach, the raw values for hostname and event-type can be stored as columns.
+
+[[schema.casestudies.log_steroids]]
+=== Case Study - Log Data and Timeseries Data on Steroids
+
+This effectively is the OpenTSDB approach.
+What OpenTSDB does is re-write data and pack rows into columns for certain time-periods.
+For a detailed explanation, see: link:http://opentsdb.net/schema.html, and link:http://www.cloudera.com/content/cloudera/en/resources/library/hbasecon/video-hbasecon-2012-lessons-learned-from-opentsdb.html[Lessons Learned from OpenTSDB] from HBaseCon2012.
+
+But this is how the general concept works: data is ingested, for example, in this manner...
+
+----
+
+[hostname][log-event][timestamp1]
+[hostname][log-event][timestamp2]
+[hostname][log-event][timestamp3]
+----
+
+with separate rowkeys for each detailed event, but is re-written like this...
+
+----
+[hostname][log-event][timerange]
+----
+
+and each of the above events are converted into columns stored with a time-offset relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very advanced processing technique, but HBase makes this possible.
+
+[[schema.casestudies.custorder]]
+=== Case Study - Customer/Order
+
+Assume that HBase is used to store customer and order information.
+There are two core record-types being ingested: a Customer record type, and Order record type.
+
+The Customer record type would include all the things that you'd typically expect:
+
+* Customer number
+* Customer name
+* Address (e.g., city, state, zip)
+* Phone numbers, etc.
+
+The Order record type would include things like:
+
+* Customer number
+* Order number
+* Sales date
+* A series of nested objects for shipping locations and line-items (see <<schema.casestudies.custorder.obj>> for details)
+
+Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose the rowkey, and specifically a composite key such as:
+
+----
+[customer number][order number]
+----
+
+for a ORDER table.
+However, there are more design decisions to make: are the _raw_ values the best choices for rowkeys?
+
+The same design questions in the Log Data use-case confront us here.
+What is the keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a reasonable spread in the keyspace, similar options appear:
+
+Composite Rowkey With Hashes:
+
+* [MD5 of customer number] = 16 bytes
+* [MD5 of order number] = 16 bytes
+
+Composite Numeric/Hash Combo Rowkey:
+
+* [substituted long for customer number] = 8 bytes
+* [MD5 of order number] = 16 bytes
+
+[[schema.casestudies.custorder.tables]]
+==== Single Table? Multiple Tables?
+
+A traditional design approach would have separate tables for CUSTOMER and SALES.
+Another option is to pack multiple record types into a single table (e.g., CUSTOMER++).
+
+Customer Record Type Rowkey:
+
+* [customer-id]
+* [type] = type indicating `1' for customer record type
+
+Order Record Type Rowkey:
+
+* [customer-id]
+* [type] = type indicating `2' for order record type
+* [order]
+
+The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id (e.g., a single scan could get you everything about that customer). The disadvantage is that it's not as easy to scan for a particular record-type.
+
+[[schema.casestudies.custorder.obj]]
+==== Order Object Design
+
+Now we need to address how to model the Order object.
+Assume that the class structure is as follows:
+
+Order::
+ (an Order can have multiple ShippingLocations
+
+LineItem::
+ (a ShippingLocation can have multiple LineItems
+
+there are multiple options on storing this data.
+
+[[schema.casestudies.custorder.obj.norm]]
+===== Completely Normalized
+
+With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM.
+
+The ORDER table's rowkey was described above: <<schema.casestudies.custorder,schema.casestudies.custorder>>
+
+The SHIPPING_LOCATION's composite rowkey would be something like this:
+
+* [order-rowkey]
+* [shipping location number] (e.g., 1st location, 2nd, etc.)
+
+The LINE_ITEM table's composite rowkey would be something like this:
+
+* [order-rowkey]
+* [shipping location number] (e.g., 1st location, 2nd, etc.)
+* [line item number] (e.g., 1st lineitem, 2nd, etc.)
+
+Such a normalized model is likely to be the approach with an RDBMS, but that's not your only option with HBase.
+The cons of such an approach is that to retrieve information about any Order, you will need:
+
+* Get on the ORDER table for the Order
+* Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances
+* Scan on the LINE_ITEM for each ShippingLocation
+
+granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase you're just more aware of this fact.
+
+[[schema.casestudies.custorder.obj.rectype]]
+===== Single Table With Record Types
+
+With this approach, there would exist a single table ORDER that would contain
+
+The Order rowkey was described above: <<schema.casestudies.custorder,schema.casestudies.custorder>>
+
+* [order-rowkey]
+* [ORDER record type]
+
+The ShippingLocation composite rowkey would be something like this:
+
+* [order-rowkey]
+* [SHIPPING record type]
+* [shipping location number] (e.g., 1st location, 2nd, etc.)
+
+The LineItem composite rowkey would be something like this:
+
+* [order-rowkey]
+* [LINE record type]
+* [shipping location number] (e.g., 1st location, 2nd, etc.)
+* [line item number] (e.g., 1st lineitem, 2nd, etc.)
+
+[[schema.casestudies.custorder.obj.denorm]]
+===== Denormalized
+
+A variant of the Single Table With Record Types approach is to denormalize and flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.
+
+The LineItem composite rowkey would be something like this:
+
+* [order-rowkey]
+* [LINE record type]
+* [line item number] (e.g., 1st lineitem, 2nd, etc., care must be taken that there are unique across the entire order)
+
+and the LineItem columns would be something like this:
+
+* itemNumber
+* quantity
+* price
+* shipToLine1 (denormalized from ShippingLocation)
+* shipToLine2 (denormalized from ShippingLocation)
+* shipToCity (denormalized from ShippingLocation)
+* shipToState (denormalized from ShippingLocation)
+* shipToZip (denormalized from ShippingLocation)
+
+The pros of this approach include a less complex object hierarchy, but one of the cons is that updating gets more complicated in case any of this information changes.
+
+[[schema.casestudies.custorder.obj.singleobj]]
+===== Object BLOB
+
+With this approach, the entire Order object graph is treated, in one way or another, as a BLOB.
+For example, the ORDER table's rowkey was described above: <<schema.casestudies.custorder,schema.casestudies.custorder>>, and a single column called "order" would contain an object that could be deserialized that contained a container Order, ShippingLocations, and LineItems.
+
+There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc.
+All of them are variants of the same approach: encode the object graph to a byte-array.
+Care should be taken with this approach to ensure backward compatibilty in case the object model changes such that older persisted structures can still be read back out of HBase.
+
+Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per Order in this example), but the cons include the aforementioned warning about backward compatiblity of serialization, language dependencies of serialization (e.g., Java Serialization only works with Java clients), the fact that you have to deserialize the entire object to get any piece of information inside the BLOB, and the difficulty in getting frameworks like Hive to work with custom objects like this.
+
+[[schema.smackdown]]
+=== Case Study - "Tall/Wide/Middle" Schema Design Smackdown
+
+This section will describe additional schema design questions that appear on the dist-list, specifically about tall and wide tables.
+These are general guidelines and not laws - each application must consider its own needs.
+
+[[schema.smackdown.rowsversions]]
+==== Rows vs. Versions
+
+A common question is whether one should prefer rows or HBase's built-in-versioning.
+The context is typically where there are "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 1 max versions). The rows-approach would require storing a timestamp in some portion of the rowkey so that they would not overwite with each successive update.
+
+Preference: Rows (generally speaking).
+
+[[schema.smackdown.rowscols]]
+==== Rows vs. Columns
+
+Another common question is whether one should prefer rows or columns.
+The context is typically in extreme cases of wide tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.
+
+Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the standard use-case where one needs to store a few dozen or hundred columns.
+But there is also a middle path between these two options, and that is "Rows as Columns."
+
+[[schema.smackdown.rowsascols]]
+==== Rows as Columns
+
+The middle path between Rows vs.
+Columns is packing data that would be a separate row into columns, for certain rows.
+OpenTSDB is the best example of this case where a single row represents a defined time-range, and then discrete events are treated as columns.
+This approach is often more complex, and may require the additional complexity of re-writing your data, but has the advantage of being I/O efficient.
+For an overview of this approach, see <<schema.casestudies.log_steroids,schema.casestudies.log-steroids>>.
+
+[[casestudies.schema.listdata]]
+=== Case Study - List Data
+
+The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in Apache HBase.
+
+*** QUESTION ***
+
+We're looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense.
+One option is store the majority of the data in a key, so we could have something like:
+
+[source]
+----
+
+<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
+<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
+<FixedWidthUserName><FixedWidthValueId3>:"" (no value)
+----
+
+The other option we had was to do this entirely using:
+
+[source,xml]
+----
+
+<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
+<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
+----
+
+where each row would contain multiple values.
+So in one case reading the first thirty values would be:
+
+[source,java]
+----
+
+scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
+----
+
+And in the second case it would be
+
+[source]
+----
+
+get 'FixedWidthUserName\x00\x00\x00\x00'
+----
+
+The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists.
+Some users would have <= 30 total values in these lists, and some users would have millions (i.e.
+power-law distribution)
+
+The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility.
+Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?
+
+My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we'll always need the same page size.
+I've ended up hearing different people tell me opposite things about performance.
+I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case.
+I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we'd need to update all subsequent rows).
+
+Thanks for help / suggestions / follow-up questions.
+
+*** ANSWER ***
+
+If I understand you correctly, you're ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:
+
+[source]
+----
+
+"user123, firstname, Paul",
+"user234, lastname, Smith"
+----
+
+(But the usernames are fixed width, and the valueids are fixed width).
+
+And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?
+
+The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you're really sure it is needed.
+
+Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done.
+What you're giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn't sound like you need that.
+Doing it this way is generally recommended (see here link:http://hbase.apache.org/book.html#schema.smackdown).
+
+Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row.
+I'm guessing you jumped to the "paginated" version because you're assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you're not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn't be fundamentally worse.
+The client has methods that allow you to get specific slices of columns.
+
+Note that neither case fundamentally uses more disk space than the other; you're just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name.
+(If this is a bit confusing, take an hour and watch Lars George's excellent video about understanding HBase schema design: link:http://www.youtube.com/watch?v=_HLoH_PgrLk).
+
+A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc.
+That seems significantly more complex.
+It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out.
+If you don't have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! :)
+
+[[schema.ops]]
+== Operational and Performance Configuration Options
+
+See the Performance section <<perf.schema,perf.schema>> for more information operational and performance schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.
[08/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
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http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/orca.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[orca]]
+== Apache HBase Orca
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+.Apache HBase Orca
+image::jumping-orca_rotated_25percent.png[]
+
+link:https://issues.apache.org/jira/browse/HBASE-4920[An Orca is the Apache HBase mascot.] See NOTICES.txt.
+Our Orca logo we got here: http://www.vectorfree.com/jumping-orca It is licensed Creative Commons Attribution 3.0.
+See https://creativecommons.org/licenses/by/3.0/us/ We changed the logo by stripping the colored background, inverting it and then rotating it some.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/other_info.adoc
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diff --git a/src/main/asciidoc/_chapters/other_info.adoc b/src/main/asciidoc/_chapters/other_info.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[other.info]]
+== Other Information About HBase
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[other.info.videos]]
+=== HBase Videos
+
+.Introduction to HBase
+* link:http://www.cloudera.com/content/cloudera/en/resources/library/presentation/chicago_data_summit_apache_hbase_an_introduction_todd_lipcon.html[Introduction to HBase] by Todd Lipcon (Chicago Data Summit 2011).
+* link:http://www.cloudera.com/videos/intorduction-hbase-todd-lipcon[Introduction to HBase] by Todd Lipcon (2010).
+link:http://www.cloudera.com/videos/hadoop-world-2011-presentation-video-building-realtime-big-data-services-at-facebook-with-hadoop-and-hbase[Building Real Time Services at Facebook with HBase] by Jonathan Gray (Hadoop World 2011).
+
+link:http://www.cloudera.com/videos/hw10_video_how_stumbleupon_built_and_advertising_platform_using_hbase_and_hadoop[HBase and Hadoop, Mixing Real-Time and Batch Processing at StumbleUpon] by JD Cryans (Hadoop World 2010).
+
+[[other.info.pres]]
+=== HBase Presentations (Slides)
+
+link:http://www.cloudera.com/content/cloudera/en/resources/library/hadoopworld/hadoop-world-2011-presentation-video-advanced-hbase-schema-design.html[Advanced HBase Schema Design] by Lars George (Hadoop World 2011).
+
+link:http://www.slideshare.net/cloudera/chicago-data-summit-apache-hbase-an-introduction[Introduction to HBase] by Todd Lipcon (Chicago Data Summit 2011).
+
+link:http://www.slideshare.net/cloudera/hw09-practical-h-base-getting-the-most-from-your-h-base-install[Getting The Most From Your HBase Install] by Ryan Rawson, Jonathan Gray (Hadoop World 2009).
+
+[[other.info.papers]]
+=== HBase Papers
+
+link:http://research.google.com/archive/bigtable.html[BigTable] by Google (2006).
+
+link:http://www.larsgeorge.com/2010/05/hbase-file-locality-in-hdfs.html[HBase and HDFS Locality] by Lars George (2010).
+
+link:http://ianvarley.com/UT/MR/Varley_MastersReport_Full_2009-08-07.pdf[No Relation: The Mixed Blessings of Non-Relational Databases] by Ian Varley (2009).
+
+[[other.info.sites]]
+=== HBase Sites
+
+link:http://www.cloudera.com/blog/category/hbase/[Cloudera's HBase Blog] has a lot of links to useful HBase information.
+
+* link:http://www.cloudera.com/blog/2010/04/cap-confusion-problems-with-partition-tolerance/[CAP Confusion] is a relevant entry for background information on distributed storage systems.
+
+link:http://wiki.apache.org/hadoop/HBase/HBasePresentations[HBase Wiki] has a page with a number of presentations.
+
+link:http://refcardz.dzone.com/refcardz/hbase[HBase RefCard] from DZone.
+
+[[other.info.books]]
+=== HBase Books
+
+link:http://shop.oreilly.com/product/0636920014348.do[HBase: The Definitive Guide] by Lars George.
+
+[[other.info.books.hadoop]]
+=== Hadoop Books
+
+link:http://shop.oreilly.com/product/9780596521981.do[Hadoop: The Definitive Guide] by Tom White.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/performance.adoc
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diff --git a/src/main/asciidoc/_chapters/performance.adoc b/src/main/asciidoc/_chapters/performance.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+
+[[performance]]
+= Apache HBase Performance Tuning
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[perf.os]]
+== Operating System
+
+[[perf.os.ram]]
+=== Memory
+
+RAM, RAM, RAM.
+Don't starve HBase.
+
+[[perf.os.64]]
+=== 64-bit
+
+Use a 64-bit platform (and 64-bit JVM).
+
+[[perf.os.swap]]
+=== Swapping
+
+Watch out for swapping.
+Set `swappiness` to 0.
+
+[[perf.network]]
+== Network
+
+Perhaps the most important factor in avoiding network issues degrading Hadoop and HBase performance is the switching hardware that is used, decisions made early in the scope of the project can cause major problems when you double or triple the size of your cluster (or more).
+
+Important items to consider:
+
+* Switching capacity of the device
+* Number of systems connected
+* Uplink capacity
+
+[[perf.network.1switch]]
+=== Single Switch
+
+The single most important factor in this configuration is that the switching capacity of the hardware is capable of handling the traffic which can be generated by all systems connected to the switch.
+Some lower priced commodity hardware can have a slower switching capacity than could be utilized by a full switch.
+
+[[perf.network.2switch]]
+=== Multiple Switches
+
+Multiple switches are a potential pitfall in the architecture.
+The most common configuration of lower priced hardware is a simple 1Gbps uplink from one switch to another.
+This often overlooked pinch point can easily become a bottleneck for cluster communication.
+Especially with MapReduce jobs that are both reading and writing a lot of data the communication across this uplink could be saturated.
+
+Mitigation of this issue is fairly simple and can be accomplished in multiple ways:
+
+* Use appropriate hardware for the scale of the cluster which you're attempting to build.
+* Use larger single switch configurations i.e.
+ single 48 port as opposed to 2x 24 port
+* Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch bandwidth.
+
+[[perf.network.multirack]]
+=== Multiple Racks
+
+Multiple rack configurations carry the same potential issues as multiple switches, and can suffer performance degradation from two main areas:
+
+* Poor switch capacity performance
+* Insufficient uplink to another rack
+
+If the the switches in your rack have appropriate switching capacity to handle all the hosts at full speed, the next most likely issue will be caused by homing more of your cluster across racks.
+The easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded uplink to other racks.
+The downside of this method however, is in the overhead of ports that could potentially be used.
+An example of this is, creating an 8Gbps port channel from rack A to rack B, using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few however can mean you're not getting the most out of your cluster.
+
+Using 10Gbe links between racks will greatly increase performance, and assuming your switches support a 10Gbe uplink or allow for an expansion card will allow you to save your ports for machines as opposed to uplinks.
+
+[[perf.network.ints]]
+=== Network Interfaces
+
+Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in <<casestudies.slownode>>.
+
+[[perf.network.call_me_maybe]]
+=== Network Consistency and Partition Tolerance
+The link:http://en.wikipedia.org/wiki/CAP_theorem[CAP Theorem] states that a distributed system can maintain two out of the following three charateristics:
+- *C*onsistency -- all nodes see the same data.
+- *A*vailability -- every request receives a response about whether it succeeded or failed.
+- *P*artition tolerance -- the system continues to operate even if some of its components become unavailable to the others.
+
+HBase favors consistency and partition tolerance, where a decision has to be made. Coda Hale explains why partition tolerance is so important, in http://codahale.com/you-cant-sacrifice-partition-tolerance/.
+
+Robert Yokota used an automated testing framework called link:https://aphyr.com/tags/jepsen[Jepson] to test HBase's partition tolerance in the face of network partitions, using techniques modeled after Aphyr's link:https://aphyr.com/posts/281-call-me-maybe-carly-rae-jepsen-and-the-perils-of-network-partitions[Call Me Maybe] series. The results, available as a link:http://eng.yammer.com/call-me-maybe-hbase/[blog post] and an link:http://eng.yammer.com/call-me-maybe-hbase-addendum/[addendum], show that HBase performs correctly.
+
+[[jvm]]
+== Java
+
+[[gc]]
+=== The Garbage Collector and Apache HBase
+
+[[gcpause]]
+==== Long GC pauses
+
+In his presentation, link:http://www.slideshare.net/cloudera/hbase-hug-presentation[Avoiding Full GCs with MemStore-Local Allocation Buffers], Todd Lipcon describes two cases of stop-the-world garbage collections common in HBase, especially during loading; CMS failure modes and old generation heap fragmentation brought.
+
+To address the first, start the CMS earlier than default by adding `-XX:CMSInitiatingOccupancyFraction` and setting it down from defaults.
+Start at 60 or 70 percent (The lower you bring down the threshold, the more GCing is done, the more CPU used). To address the second fragmentation issue, Todd added an experimental facility,
+(MSLAB), that must be explicitly enabled in Apache HBase 0.90.x (It's defaulted to be _on_ in Apache 0.92.x HBase). Set `hbase.hregion.memstore.mslab.enabled` to true in your `Configuration`.
+See the cited slides for background and detail.
+The latest JVMs do better regards fragmentation so make sure you are running a recent release.
+Read down in the message, link:http://osdir.com/ml/hotspot-gc-use/2011-11/msg00002.html[Identifying concurrent mode failures caused by fragmentation].
+Be aware that when enabled, each MemStore instance will occupy at least an MSLAB instance of memory.
+If you have thousands of regions or lots of regions each with many column families, this allocation of MSLAB may be responsible for a good portion of your heap allocation and in an extreme case cause you to OOME.
+Disable MSLAB in this case, or lower the amount of memory it uses or float less regions per server.
+
+If you have a write-heavy workload, check out link:https://issues.apache.org/jira/browse/HBASE-8163[HBASE-8163 MemStoreChunkPool: An improvement for JAVA GC when using MSLAB].
+It describes configurations to lower the amount of young GC during write-heavy loadings.
+If you do not have HBASE-8163 installed, and you are trying to improve your young GC times, one trick to consider -- courtesy of our Liang Xie -- is to set the GC config `-XX:PretenureSizeThreshold` in _hbase-env.sh_ to be just smaller than the size of `hbase.hregion.memstore.mslab.chunksize` so MSLAB allocations happen in the tenured space directly rather than first in the young gen.
+You'd do this because these MSLAB allocations are going to likely make it to the old gen anyways and rather than pay the price of a copies between s0 and s1 in eden space followed by the copy up from young to old gen after the MSLABs have achieved sufficient tenure, save a bit of YGC churn and allocate in the old gen directly.
+
+For more information about GC logs, see <<trouble.log.gc>>.
+
+Consider also enabling the off-heap Block Cache.
+This has been shown to mitigate GC pause times.
+See <<block.cache>>
+
+[[perf.configurations]]
+== HBase Configurations
+
+See <<recommended_configurations>>.
+
+[[perf.compactions.and.splits]]
+=== Managing Compactions
+
+For larger systems, managing link:[compactions and splits] may be something you want to consider.
+
+[[perf.handlers]]
+=== `hbase.regionserver.handler.count`
+
+See <<hbase.regionserver.handler.count>>.
+
+[[perf.hfile.block.cache.size]]
+=== `hfile.block.cache.size`
+
+See <<hfile.block.cache.size>>.
+A memory setting for the RegionServer process.
+
+[[blockcache.prefetch]]
+=== Prefetch Option for Blockcache
+
+link:https://issues.apache.org/jira/browse/HBASE-9857[HBASE-9857] adds a new option to prefetch HFile contents when opening the BlockCache, if a Column family or RegionServer property is set.
+This option is available for HBase 0.98.3 and later.
+The purpose is to warm the BlockCache as rapidly as possible after the cache is opened, using in-memory table data, and not counting the prefetching as cache misses.
+This is great for fast reads, but is not a good idea if the data to be preloaded will not fit into the BlockCache.
+It is useful for tuning the IO impact of prefetching versus the time before all data blocks are in cache.
+
+To enable prefetching on a given column family, you can use HBase Shell or use the API.
+
+.Enable Prefetch Using HBase Shell
+====
+----
+hbase> create 'MyTable', { NAME => 'myCF', PREFETCH_BLOCKS_ON_OPEN => 'true' }
+----
+====
+
+.Enable Prefetch Using the API
+====
+[source,java]
+----
+
+// ...
+HTableDescriptor tableDesc = new HTableDescriptor("myTable");
+HColumnDescriptor cfDesc = new HColumnDescriptor("myCF");
+cfDesc.setPrefetchBlocksOnOpen(true);
+tableDesc.addFamily(cfDesc);
+// ...
+----
+====
+
+See the API documentation for link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html[CacheConfig].
+
+[[perf.rs.memstore.size]]
+=== `hbase.regionserver.global.memstore.size`
+
+See <<hbase.regionserver.global.memstore.size>>.
+This memory setting is often adjusted for the RegionServer process depending on needs.
+
+[[perf.rs.memstore.size.lower.limit]]
+=== `hbase.regionserver.global.memstore.size.lower.limit`
+
+See <<hbase.regionserver.global.memstore.size.lower.limit>>.
+This memory setting is often adjusted for the RegionServer process depending on needs.
+
+[[perf.hstore.blockingstorefiles]]
+=== `hbase.hstore.blockingStoreFiles`
+
+See <<hbase.hstore.blockingstorefiles>>.
+If there is blocking in the RegionServer logs, increasing this can help.
+
+[[perf.hregion.memstore.block.multiplier]]
+=== `hbase.hregion.memstore.block.multiplier`
+
+See <<hbase.hregion.memstore.block.multiplier>>.
+If there is enough RAM, increasing this can help.
+
+[[hbase.regionserver.checksum.verify.performance]]
+=== `hbase.regionserver.checksum.verify`
+
+Have HBase write the checksum into the datablock and save having to do the checksum seek whenever you read.
+
+See <<hbase.regionserver.checksum.verify>>, <<hbase.hstore.bytes.per.checksum>> and <<hbase.hstore.checksum.algorithm>>. For more information see the release note on link:https://issues.apache.org/jira/browse/HBASE-5074[HBASE-5074 support checksums in HBase block cache].
+
+=== Tuning `callQueue` Options
+
+link:https://issues.apache.org/jira/browse/HBASE-11355[HBASE-11355] introduces several callQueue tuning mechanisms which can increase performance.
+See the JIRA for some benchmarking information.
+
+To increase the number of callqueues, set `hbase.ipc.server.num.callqueue` to a value greater than `1`.
+To split the callqueue into separate read and write queues, set `hbase.ipc.server.callqueue.read.ratio` to a value between `0` and `1`.
+This factor weights the queues toward writes (if below .5) or reads (if above .5). Another way to say this is that the factor determines what percentage of the split queues are used for reads.
+The following examples illustrate some of the possibilities.
+Note that you always have at least one write queue, no matter what setting you use.
+
+* The default value of `0` does not split the queue.
+* A value of `.3` uses 30% of the queues for reading and 60% for writing.
+ Given a value of `10` for `hbase.ipc.server.num.callqueue`, 3 queues would be used for reads and 7 for writes.
+* A value of `.5` uses the same number of read queues and write queues.
+ Given a value of `10` for `hbase.ipc.server.num.callqueue`, 5 queues would be used for reads and 5 for writes.
+* A value of `.6` uses 60% of the queues for reading and 30% for reading.
+ Given a value of `10` for `hbase.ipc.server.num.callqueue`, 7 queues would be used for reads and 3 for writes.
+* A value of `1.0` uses one queue to process write requests, and all other queues process read requests.
+ A value higher than `1.0` has the same effect as a value of `1.0`.
+ Given a value of `10` for `hbase.ipc.server.num.callqueue`, 9 queues would be used for reads and 1 for writes.
+
+You can also split the read queues so that separate queues are used for short reads (from Get operations) and long reads (from Scan operations), by setting the `hbase.ipc.server.callqueue.scan.ratio` option.
+This option is a factor between 0 and 1, which determine the ratio of read queues used for Gets and Scans.
+More queues are used for Gets if the value is below `.5` and more are used for scans if the value is above `.5`.
+No matter what setting you use, at least one read queue is used for Get operations.
+
+* A value of `0` does not split the read queue.
+* A value of `.3` uses 60% of the read queues for Gets and 30% for Scans.
+ Given a value of `20` for `hbase.ipc.server.num.callqueue` and a value of `.5` for `hbase.ipc.server.callqueue.read.ratio`, 10 queues would be used for reads, out of those 10, 7 would be used for Gets and 3 for Scans.
+* A value of `.5` uses half the read queues for Gets and half for Scans.
+ Given a value of `20` for `hbase.ipc.server.num.callqueue` and a value of `.5` for `hbase.ipc.server.callqueue.read.ratio`, 10 queues would be used for reads, out of those 10, 5 would be used for Gets and 5 for Scans.
+* A value of `.6` uses 30% of the read queues for Gets and 60% for Scans.
+ Given a value of `20` for `hbase.ipc.server.num.callqueue` and a value of `.5` for `hbase.ipc.server.callqueue.read.ratio`, 10 queues would be used for reads, out of those 10, 3 would be used for Gets and 7 for Scans.
+* A value of `1.0` uses all but one of the read queues for Scans.
+ Given a value of `20` for `hbase.ipc.server.num.callqueue` and a value of`.5` for `hbase.ipc.server.callqueue.read.ratio`, 10 queues would be used for reads, out of those 10, 1 would be used for Gets and 9 for Scans.
+
+You can use the new option `hbase.ipc.server.callqueue.handler.factor` to programmatically tune the number of queues:
+
+* A value of `0` uses a single shared queue between all the handlers.
+* A value of `1` uses a separate queue for each handler.
+* A value between `0` and `1` tunes the number of queues against the number of handlers.
+ For instance, a value of `.5` shares one queue between each two handlers.
++
+Having more queues, such as in a situation where you have one queue per handler, reduces contention when adding a task to a queue or selecting it from a queue.
+The trade-off is that if you have some queues with long-running tasks, a handler may end up waiting to execute from that queue rather than processing another queue which has waiting tasks.
+
+
+For these values to take effect on a given RegionServer, the RegionServer must be restarted.
+These parameters are intended for testing purposes and should be used carefully.
+
+[[perf.zookeeper]]
+== ZooKeeper
+
+See <<zookeeper>> for information on configuring ZooKeeper, and see the part about having a dedicated disk.
+
+[[perf.schema]]
+== Schema Design
+
+[[perf.number.of.cfs]]
+=== Number of Column Families
+
+See <<number.of.cfs>>.
+
+[[perf.schema.keys]]
+=== Key and Attribute Lengths
+
+See <<keysize>>.
+See also <<perf.compression.however>> for compression caveats.
+
+[[schema.regionsize]]
+=== Table RegionSize
+
+The regionsize can be set on a per-table basis via `setFileSize` on link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HTableDescriptor.html[HTableDescriptor] in the event where certain tables require different regionsizes than the configured default regionsize.
+
+See <<ops.capacity.regions>> for more information.
+
+[[schema.bloom]]
+=== Bloom Filters
+
+A Bloom filter, named for its creator, Burton Howard Bloom, is a data structure which is designed to predict whether a given element is a member of a set of data.
+A positive result from a Bloom filter is not always accurate, but a negative result is guaranteed to be accurate.
+Bloom filters are designed to be "accurate enough" for sets of data which are so large that conventional hashing mechanisms would be impractical.
+For more information about Bloom filters in general, refer to http://en.wikipedia.org/wiki/Bloom_filter.
+
+In terms of HBase, Bloom filters provide a lightweight in-memory structure to reduce the number of disk reads for a given Get operation (Bloom filters do not work with Scans) to only the StoreFiles likely to contain the desired Row.
+The potential performance gain increases with the number of parallel reads.
+
+The Bloom filters themselves are stored in the metadata of each HFile and never need to be updated.
+When an HFile is opened because a region is deployed to a RegionServer, the Bloom filter is loaded into memory.
+
+HBase includes some tuning mechanisms for folding the Bloom filter to reduce the size and keep the false positive rate within a desired range.
+
+Bloom filters were introduced in link:https://issues.apache.org/jira/browse/HBASE-1200[HBASE-1200].
+Since HBase 0.96, row-based Bloom filters are enabled by default.
+(link:https://issues.apache.org/jira/browse/HBASE-8450[HBASE-])
+
+For more information on Bloom filters in relation to HBase, see <<blooms>> for more information, or the following Quora discussion: link:http://www.quora.com/How-are-bloom-filters-used-in-HBase[How are bloom filters used in HBase?].
+
+[[bloom.filters.when]]
+==== When To Use Bloom Filters
+
+Since HBase 0.96, row-based Bloom filters are enabled by default.
+You may choose to disable them or to change some tables to use row+column Bloom filters, depending on the characteristics of your data and how it is loaded into HBase.
+
+To determine whether Bloom filters could have a positive impact, check the value of `blockCacheHitRatio` in the RegionServer metrics.
+If Bloom filters are enabled, the value of `blockCacheHitRatio` should increase, because the Bloom filter is filtering out blocks that are definitely not needed.
+
+You can choose to enable Bloom filters for a row or for a row+column combination.
+If you generally scan entire rows, the row+column combination will not provide any benefit.
+A row-based Bloom filter can operate on a row+column Get, but not the other way around.
+However, if you have a large number of column-level Puts, such that a row may be present in every StoreFile, a row-based filter will always return a positive result and provide no benefit.
+Unless you have one column per row, row+column Bloom filters require more space, in order to store more keys.
+Bloom filters work best when the size of each data entry is at least a few kilobytes in size.
+
+Overhead will be reduced when your data is stored in a few larger StoreFiles, to avoid extra disk IO during low-level scans to find a specific row.
+
+Bloom filters need to be rebuilt upon deletion, so may not be appropriate in environments with a large number of deletions.
+
+==== Enabling Bloom Filters
+
+Bloom filters are enabled on a Column Family.
+You can do this by using the setBloomFilterType method of HColumnDescriptor or using the HBase API.
+Valid values are `NONE` (the default), `ROW`, or `ROWCOL`.
+See <<bloom.filters.when>> for more information on `ROW` versus `ROWCOL`.
+See also the API documentation for link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor].
+
+The following example creates a table and enables a ROWCOL Bloom filter on the `colfam1` column family.
+
+----
+
+hbase> create 'mytable',{NAME => 'colfam1', BLOOMFILTER => 'ROWCOL'}
+----
+
+==== Configuring Server-Wide Behavior of Bloom Filters
+
+You can configure the following settings in the _hbase-site.xml_.
+
+[cols="1,1,1", options="header"]
+|===
+| Parameter
+| Default
+| Description
+
+| io.hfile.bloom.enabled
+| yes
+| Set to no to kill bloom filters server-wide if something goes wrong
+
+| io.hfile.bloom.error.rate
+| .01
+| The average false positive rate for bloom filters. Folding is used to
+ maintain the false positive rate. Expressed as a decimal representation of a
+ percentage.
+
+| io.hfile.bloom.max.fold
+| 7
+| The guaranteed maximum fold rate. Changing this setting should not be
+ necessary and is not recommended.
+
+| io.storefile.bloom.max.keys
+| 128000000
+| For default (single-block) Bloom filters, this specifies the maximum number of keys.
+
+| io.storefile.delete.family.bloom.enabled
+| true
+| Master switch to enable Delete Family Bloom filters and store them in the StoreFile.
+
+| io.storefile.bloom.block.size
+| 65536
+| Target Bloom block size. Bloom filter blocks of approximately this size
+ are interleaved with data blocks.
+
+| hfile.block.bloom.cacheonwrite
+| false
+| Enables cache-on-write for inline blocks of a compound Bloom filter.
+|===
+
+[[schema.cf.blocksize]]
+=== ColumnFamily BlockSize
+
+The blocksize can be configured for each ColumnFamily in a table, and defaults to 64k.
+Larger cell values require larger blocksizes.
+There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting indexes should be roughly halved).
+
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor] and <<store>>for more information.
+
+[[cf.in.memory]]
+=== In-Memory ColumnFamilies
+
+ColumnFamilies can optionally be defined as in-memory.
+Data is still persisted to disk, just like any other ColumnFamily.
+In-memory blocks have the highest priority in the <<block.cache>>, but it is not a guarantee that the entire table will be in memory.
+
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html[HColumnDescriptor] for more information.
+
+[[perf.compression]]
+=== Compression
+
+Production systems should use compression with their ColumnFamily definitions.
+See <<compression>> for more information.
+
+[[perf.compression.however]]
+==== However...
+
+Compression deflates data _on disk_.
+When it's in-memory (e.g., in the MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's inflated.
+So while using ColumnFamily compression is a best practice, but it's not going to completely eliminate the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.
+
+See <<keysize>> on for schema design tips, and <<keyvalue>> for more information on HBase stores data internally.
+
+[[perf.general]]
+== HBase General Patterns
+
+[[perf.general.constants]]
+=== Constants
+
+When people get started with HBase they have a tendency to write code that looks like this:
+
+[source,java]
+----
+Get get = new Get(rowkey);
+Result r = table.get(get);
+byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value
+----
+
+But especially when inside loops (and MapReduce jobs), converting the columnFamily and column-names to byte-arrays repeatedly is surprisingly expensive.
+It's better to use constants for the byte-arrays, like this:
+
+[source,java]
+----
+public static final byte[] CF = "cf".getBytes();
+public static final byte[] ATTR = "attr".getBytes();
+...
+Get get = new Get(rowkey);
+Result r = table.get(get);
+byte[] b = r.getValue(CF, ATTR); // returns current version of value
+----
+
+[[perf.writing]]
+== Writing to HBase
+
+[[perf.batch.loading]]
+=== Batch Loading
+
+Use the bulk load tool if you can.
+See <<arch.bulk.load>>.
+Otherwise, pay attention to the below.
+
+[[precreate.regions]]
+=== Table Creation: Pre-Creating Regions
+
+Tables in HBase are initially created with one region by default.
+For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster.
+A useful pattern to speed up the bulk import process is to pre-create empty regions.
+Be somewhat conservative in this, because too-many regions can actually degrade performance.
+
+There are two different approaches to pre-creating splits.
+The first approach is to rely on the default `Admin` strategy (which is implemented in `Bytes.split`)...
+
+[source,java]
+----
+
+byte[] startKey = ...; // your lowest key
+byte[] endKey = ...; // your highest key
+int numberOfRegions = ...; // # of regions to create
+admin.createTable(table, startKey, endKey, numberOfRegions);
+----
+
+And the other approach is to define the splits yourself...
+
+[source,java]
+----
+byte[][] splits = ...; // create your own splits
+admin.createTable(table, splits);
+----
+
+See <<rowkey.regionsplits>> for issues related to understanding your keyspace and pre-creating regions.
+See <<manual_region_splitting_decisions,manual region splitting decisions>> for discussion on manually pre-splitting regions.
+
+[[def.log.flush]]
+=== Table Creation: Deferred Log Flush
+
+The default behavior for Puts using the Write Ahead Log (WAL) is that `WAL` edits will be written immediately.
+If deferred log flush is used, WAL edits are kept in memory until the flush period.
+The benefit is aggregated and asynchronous `WAL`- writes, but the potential downside is that if the RegionServer goes down the yet-to-be-flushed edits are lost.
+This is safer, however, than not using WAL at all with Puts.
+
+Deferred log flush can be configured on tables via link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HTableDescriptor.html[HTableDescriptor].
+The default value of `hbase.regionserver.optionallogflushinterval` is 1000ms.
+
+[[perf.hbase.client.autoflush]]
+=== HBase Client: AutoFlush
+
+When performing a lot of Puts, make sure that setAutoFlush is set to false on your link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table] instance.
+Otherwise, the Puts will be sent one at a time to the RegionServer.
+Puts added via `table.add(Put)` and `table.add( <List> Put)` wind up in the same write buffer.
+If `autoFlush = false`, these messages are not sent until the write-buffer is filled.
+To explicitly flush the messages, call `flushCommits`.
+Calling `close` on the `Table` instance will invoke `flushCommits`.
+
+[[perf.hbase.client.putwal]]
+=== HBase Client: Turn off WAL on Puts
+
+A frequent request is to disable the WAL to increase performance of Puts.
+This is only appropriate for bulk loads, as it puts your data at risk by removing the protection of the WAL in the event of a region server crash.
+Bulk loads can be re-run in the event of a crash, with little risk of data loss.
+
+WARNING: If you disable the WAL for anything other than bulk loads, your data is at risk.
+
+In general, it is best to use WAL for Puts, and where loading throughput is a concern to use bulk loading techniques instead.
+For normal Puts, you are not likely to see a performance improvement which would outweigh the risk.
+To disable the WAL, see <<wal.disable>>.
+
+[[perf.hbase.client.regiongroup]]
+=== HBase Client: Group Puts by RegionServer
+
+In addition to using the writeBuffer, grouping `Put`s by RegionServer can reduce the number of client RPC calls per writeBuffer flush.
+There is a utility `HTableUtil` currently on TRUNK that does this, but you can either copy that or implement your own version for those still on 0.90.x or earlier.
+
+[[perf.hbase.write.mr.reducer]]
+=== MapReduce: Skip The Reducer
+
+When writing a lot of data to an HBase table from a MR job (e.g., with link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableOutputFormat.html[TableOutputFormat]), and specifically where Puts are being emitted from the Mapper, skip the Reducer step.
+When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other Reducers that will most likely be off-node.
+It's far more efficient to just write directly to HBase.
+
+For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result). This is a different processing problem than from the the above case.
+
+[[perf.one.region]]
+=== Anti-Pattern: One Hot Region
+
+If all your data is being written to one region at a time, then re-read the section on processing timeseries data.
+
+Also, if you are pre-splitting regions and all your data is _still_ winding up in a single region even though your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy.
+There are a variety of reasons that regions may appear "well split" but won't work with your data.
+As the HBase client communicates directly with the RegionServers, this can be obtained via link:hhttp://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#getRegionLocation(byte[])[Table.getRegionLocation].
+
+See <<precreate.regions>>, as well as <<perf.configurations>>
+
+[[perf.reading]]
+== Reading from HBase
+
+The mailing list can help if you are having performance issues.
+For example, here is a good general thread on what to look at addressing read-time issues: link:http://search-hadoop.com/m/qOo2yyHtCC1[HBase Random Read latency > 100ms]
+
+[[perf.hbase.client.caching]]
+=== Scan Caching
+
+If HBase is used as an input source for a MapReduce job, for example, make sure that the input link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] instance to the MapReduce job has `setCaching` set to something greater than the default (which is 1). Using the default value means that the map-task will make call back to the region-server for every record processed.
+Setting this value to 500, for example, will transfer 500 rows at a time to the client to be processed.
+There is a cost/benefit to have the cache value be large because it costs more in memory for both client and RegionServer, so bigger isn't always better.
+
+[[perf.hbase.client.caching.mr]]
+==== Scan Caching in MapReduce Jobs
+
+Scan settings in MapReduce jobs deserve special attention.
+Timeouts can result (e.g., UnknownScannerException) in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the next set of data.
+This problem can occur because there is non-trivial processing occurring per row.
+If you process rows quickly, set caching higher.
+If you process rows more slowly (e.g., lots of transformations per row, writes), then set caching lower.
+
+Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the processing that is often performed in MapReduce jobs tends to exacerbate this issue.
+
+[[perf.hbase.client.selection]]
+=== Scan Attribute Selection
+
+Whenever a Scan is used to process large numbers of rows (and especially when used as a MapReduce source), be aware of which attributes are selected.
+If `scan.addFamily` is called then _all_ of the attributes in the specified ColumnFamily will be returned to the client.
+If only a small number of the available attributes are to be processed, then only those attributes should be specified in the input scan because attribute over-selection is a non-trivial performance penalty over large datasets.
+
+[[perf.hbase.client.seek]]
+=== Avoid scan seeks
+
+When columns are selected explicitly with `scan.addColumn`, HBase will schedule seek operations to seek between the selected columns.
+When rows have few columns and each column has only a few versions this can be inefficient.
+A seek operation is generally slower if does not seek at least past 5-10 columns/versions or 512-1024 bytes.
+
+In order to opportunistically look ahead a few columns/versions to see if the next column/version can be found that way before a seek operation is scheduled, a new attribute `Scan.HINT_LOOKAHEAD` can be set the on Scan object.
+The following code instructs the RegionServer to attempt two iterations of next before a seek is scheduled:
+
+[source,java]
+----
+Scan scan = new Scan();
+scan.addColumn(...);
+scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2));
+table.getScanner(scan);
+----
+
+[[perf.hbase.mr.input]]
+=== MapReduce - Input Splits
+
+For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to have the same Input Split (i.e., the RegionServer serving the data), see the Troubleshooting Case Study in <<casestudies.slownode>>.
+
+[[perf.hbase.client.scannerclose]]
+=== Close ResultScanners
+
+This isn't so much about improving performance but rather _avoiding_ performance problems.
+If you forget to close link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/ResultScanner.html[ResultScanners] you can cause problems on the RegionServers.
+Always have ResultScanner processing enclosed in try/catch blocks.
+
+[source,java]
+----
+Scan scan = new Scan();
+// set attrs...
+ResultScanner rs = table.getScanner(scan);
+try {
+ for (Result r = rs.next(); r != null; r = rs.next()) {
+ // process result...
+} finally {
+ rs.close(); // always close the ResultScanner!
+}
+table.close();
+----
+
+[[perf.hbase.client.blockcache]]
+=== Block Cache
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] instances can be set to use the block cache in the RegionServer via the `setCacheBlocks` method.
+For input Scans to MapReduce jobs, this should be `false`.
+For frequently accessed rows, it is advisable to use the block cache.
+
+Cache more data by moving your Block Cache off-heap.
+See <<offheap.blockcache>>
+
+[[perf.hbase.client.rowkeyonly]]
+=== Optimal Loading of Row Keys
+
+When performing a table link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[scan] where only the row keys are needed (no families, qualifiers, values or timestamps), add a FilterList with a `MUST_PASS_ALL` operator to the scanner using `setFilter`.
+The filter list should include both a link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FirstKeyOnlyFilter.html[FirstKeyOnlyFilter] and a link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/KeyOnlyFilter.html[KeyOnlyFilter].
+Using this filter combination will result in a worst case scenario of a RegionServer reading a single value from disk and minimal network traffic to the client for a single row.
+
+[[perf.hbase.read.dist]]
+=== Concurrency: Monitor Data Spread
+
+When performing a high number of concurrent reads, monitor the data spread of the target tables.
+If the target table(s) have too few regions then the reads could likely be served from too few nodes.
+
+See <<precreate.regions>>, as well as <<perf.configurations>>
+
+[[blooms]]
+=== Bloom Filters
+
+Enabling Bloom Filters can save your having to go to disk and can help improve read latencies.
+
+link:http://en.wikipedia.org/wiki/Bloom_filter[Bloom filters] were developed over in link:https://issues.apache.org/jira/browse/HBASE-1200[HBase-1200 Add bloomfilters].
+For description of the development process -- why static blooms rather than dynamic -- and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the _Development Process_ section of the document link:https://issues.apache.org/jira/secure/attachment/12444007/Bloom_Filters_in_HBase.pdf[BloomFilters in HBase] attached to link:https://issues.apache.org/jira/browse/HBASE-1200[HBASE-1200].
+The bloom filters described here are actually version two of blooms in HBase.
+In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the link:http://www.one-lab.org[European Commission One-Lab Project 034819].
+The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile.
+Version 1 of HBase blooms never worked that well.
+Version 2 is a rewrite from scratch though again it starts with the one-lab work.
+
+See also <<schema.bloom>>.
+
+[[bloom_footprint]]
+==== Bloom StoreFile footprint
+
+Bloom filters add an entry to the `StoreFile` general `FileInfo` data structure and then two extra entries to the `StoreFile` metadata section.
+
+===== BloomFilter in the `StoreFile``FileInfo` data structure
+
+`FileInfo` has a `BLOOM_FILTER_TYPE` entry which is set to `NONE`, `ROW` or `ROWCOL.`
+
+===== BloomFilter entries in `StoreFile` metadata
+
+`BLOOM_FILTER_META` holds Bloom Size, Hash Function used, etc.
+It's small in size and is cached on `StoreFile.Reader` load
+
+`BLOOM_FILTER_DATA` is the actual bloomfilter data.
+Obtained on-demand.
+Stored in the LRU cache, if it is enabled (It's enabled by default).
+
+[[config.bloom]]
+==== Bloom Filter Configuration
+
+===== `io.hfile.bloom.enabled` global kill switch
+
+`io.hfile.bloom.enabled` in `Configuration` serves as the kill switch in case something goes wrong.
+Default = `true`.
+
+===== `io.hfile.bloom.error.rate`
+
+`io.hfile.bloom.error.rate` = average false positive rate.
+Default = 1%. Decrease rate by ½ (e.g.
+to .5%) == +1 bit per bloom entry.
+
+===== `io.hfile.bloom.max.fold`
+
+`io.hfile.bloom.max.fold` = guaranteed minimum fold rate.
+Most people should leave this alone.
+Default = 7, or can collapse to at least 1/128th of original size.
+See the _Development Process_ section of the document link:https://issues.apache.org/jira/secure/attachment/12444007/Bloom_Filters_in_HBase.pdf[BloomFilters in HBase] for more on what this option means.
+
+=== Hedged Reads
+
+Hedged reads are a feature of HDFS, introduced in link:https://issues.apache.org/jira/browse/HDFS-5776[HDFS-5776].
+Normally, a single thread is spawned for each read request.
+However, if hedged reads are enabled, the client waits some configurable amount of time, and if the read does not return, the client spawns a second read request, against a different block replica of the same data.
+Whichever read returns first is used, and the other read request is discarded.
+Hedged reads can be helpful for times where a rare slow read is caused by a transient error such as a failing disk or flaky network connection.
+
+Because a HBase RegionServer is a HDFS client, you can enable hedged reads in HBase, by adding the following properties to the RegionServer's hbase-site.xml and tuning the values to suit your environment.
+
+.Configuration for Hedged Reads
+* `dfs.client.hedged.read.threadpool.size` - the number of threads dedicated to servicing hedged reads.
+ If this is set to 0 (the default), hedged reads are disabled.
+* `dfs.client.hedged.read.threshold.millis` - the number of milliseconds to wait before spawning a second read thread.
+
+.Hedged Reads Configuration Example
+====
+[source,xml]
+----
+<property>
+ <name>dfs.client.hedged.read.threadpool.size</name>
+ <value>20</value> <!-- 20 threads -->
+</property>
+<property>
+ <name>dfs.client.hedged.read.threshold.millis</name>
+ <value>10</value> <!-- 10 milliseconds -->
+</property>
+----
+====
+
+Use the following metrics to tune the settings for hedged reads on your cluster.
+See <<hbase_metrics>> for more information.
+
+.Metrics for Hedged Reads
+* hedgedReadOps - the number of times hedged read threads have been triggered.
+ This could indicate that read requests are often slow, or that hedged reads are triggered too quickly.
+* hedgeReadOpsWin - the number of times the hedged read thread was faster than the original thread.
+ This could indicate that a given RegionServer is having trouble servicing requests.
+
+[[perf.deleting]]
+== Deleting from HBase
+
+[[perf.deleting.queue]]
+=== Using HBase Tables as Queues
+
+HBase tables are sometimes used as queues.
+In this case, special care must be taken to regularly perform major compactions on tables used in this manner.
+As is documented in <<datamodel>>, marking rows as deleted creates additional StoreFiles which then need to be processed on reads.
+Tombstones only get cleaned up with major compactions.
+
+See also <<compaction>> and link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html#majorCompact%28java.lang.String%29[Admin.majorCompact].
+
+[[perf.deleting.rpc]]
+=== Delete RPC Behavior
+
+Be aware that `Table.delete(Delete)` doesn't use the writeBuffer.
+It will execute an RegionServer RPC with each invocation.
+For a large number of deletes, consider `Table.delete(List)`.
+
+See http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#delete%28org.apache.hadoop.hbase.client.Delete%29
+
+[[perf.hdfs]]
+== HDFS
+
+Because HBase runs on <<arch.hdfs>> it is important to understand how it works and how it affects HBase.
+
+[[perf.hdfs.curr]]
+=== Current Issues With Low-Latency Reads
+
+The original use-case for HDFS was batch processing.
+As such, there low-latency reads were historically not a priority.
+With the increased adoption of Apache HBase this is changing, and several improvements are already in development.
+See the link:https://issues.apache.org/jira/browse/HDFS-1599[Umbrella Jira Ticket for HDFS Improvements for HBase].
+
+[[perf.hdfs.configs.localread]]
+=== Leveraging local data
+
+Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via link:https://issues.apache.org/jira/browse/HDFS-2246[HDFS-2246], it is possible for the DFSClient to take a "short circuit" and read directly from the disk instead of going through the DataNode when the data is local.
+What this means for HBase is that the RegionServers can read directly off their machine's disks instead of having to open a socket to talk to the DataNode, the former being generally much faster.
+See JD's link:http://files.meetup.com/1350427/hug_ebay_jdcryans.pdf[Performance Talk].
+Also see link:http://search-hadoop.com/m/zV6dKrLCVh1[HBase, mail # dev - read short circuit] thread for more discussion around short circuit reads.
+
+To enable "short circuit" reads, it will depend on your version of Hadoop.
+The original shortcircuit read patch was much improved upon in Hadoop 2 in link:https://issues.apache.org/jira/browse/HDFS-347[HDFS-347].
+See http://blog.cloudera.com/blog/2013/08/how-improved-short-circuit-local-reads-bring-better-performance-and-security-to-hadoop/ for details on the difference between the old and new implementations.
+See link:http://archive.cloudera.com/cdh4/cdh/4/hadoop/hadoop-project-dist/hadoop-hdfs/ShortCircuitLocalReads.html[Hadoop shortcircuit reads configuration page] for how to enable the latter, better version of shortcircuit.
+For example, here is a minimal config.
+enabling short-circuit reads added to _hbase-site.xml_:
+
+[source,xml]
+----
+<property>
+ <name>dfs.client.read.shortcircuit</name>
+ <value>true</value>
+ <description>
+ This configuration parameter turns on short-circuit local reads.
+ </description>
+</property>
+<property>
+ <name>dfs.domain.socket.path</name>
+ <value>/home/stack/sockets/short_circuit_read_socket_PORT</value>
+ <description>
+ Optional. This is a path to a UNIX domain socket that will be used for
+ communication between the DataNode and local HDFS clients.
+ If the string "_PORT" is present in this path, it will be replaced by the
+ TCP port of the DataNode.
+ </description>
+</property>
+----
+
+Be careful about permissions for the directory that hosts the shared domain socket; dfsclient will complain if open to other than the hbase user.
+
+If you are running on an old Hadoop, one that is without link:https://issues.apache.org/jira/browse/HDFS-347[HDFS-347] but that has link:https://issues.apache.org/jira/browse/HDFS-2246[HDFS-2246], you must set two configurations.
+First, the hdfs-site.xml needs to be amended.
+Set the property `dfs.block.local-path-access.user` to be the _only_ user that can use the shortcut.
+This has to be the user that started HBase.
+Then in hbase-site.xml, set `dfs.client.read.shortcircuit` to be `true`
+
+Services -- at least the HBase RegionServers -- will need to be restarted in order to pick up the new configurations.
+
+.dfs.client.read.shortcircuit.buffer.size
+[NOTE]
+====
+The default for this value is too high when running on a highly trafficked HBase.
+In HBase, if this value has not been set, we set it down from the default of 1M to 128k (Since HBase 0.98.0 and 0.96.1). See link:https://issues.apache.org/jira/browse/HBASE-8143[HBASE-8143 HBase on Hadoop 2 with local short circuit reads (ssr) causes OOM]). The Hadoop DFSClient in HBase will allocate a direct byte buffer of this size for _each_ block it has open; given HBase keeps its HDFS files open all the time, this can add up quickly.
+====
+
+[[perf.hdfs.comp]]
+=== Performance Comparisons of HBase vs. HDFS
+
+A fairly common question on the dist-list is why HBase isn't as performant as HDFS files in a batch context (e.g., as a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues, returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this processing context.
+There is room for improvement and this gap will, over time, be reduced, but HDFS will always be faster in this use-case.
+
+[[perf.ec2]]
+== Amazon EC2
+
+Performance questions are common on Amazon EC2 environments because it is a shared environment.
+You will not see the same throughput as a dedicated server.
+In terms of running tests on EC2, run them several times for the same reason (i.e., it's a shared environment and you don't know what else is happening on the server).
+
+If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that because EC2 issues are practically a separate class of performance issues.
+
+[[perf.hbase.mr.cluster]]
+== Collocating HBase and MapReduce
+
+It is often recommended to have different clusters for HBase and MapReduce.
+A better qualification of this is: don't collocate a HBase that serves live requests with a heavy MR workload.
+OLTP and OLAP-optimized systems have conflicting requirements and one will lose to the other, usually the former.
+For example, short latency-sensitive disk reads will have to wait in line behind longer reads that are trying to squeeze out as much throughput as possible.
+MR jobs that write to HBase will also generate flushes and compactions, which will in turn invalidate blocks in the <<block.cache>>.
+
+If you need to process the data from your live HBase cluster in MR, you can ship the deltas with <<copy.table>> or use replication to get the new data in real time on the OLAP cluster.
+In the worst case, if you really need to collocate both, set MR to use less Map and Reduce slots than you'd normally configure, possibly just one.
+
+When HBase is used for OLAP operations, it's preferable to set it up in a hardened way like configuring the ZooKeeper session timeout higher and giving more memory to the MemStores (the argument being that the Block Cache won't be used much since the workloads are usually long scans).
+
+[[perf.casestudy]]
+== Case Studies
+
+For Performance and Troubleshooting Case Studies, see <<casestudies>>.
+
+ifdef::backend-docbook[]
+[index]
+== Index
+// Generated automatically by the DocBook toolchain.
+endif::backend-docbook[]
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[preface]
+= Preface
+:doctype: article
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This is the official reference guide for the link:http://hbase.apache.org/[HBase] version it ships with.
+
+Herein you will find either the definitive documentation on an HBase topic as of its standing when the referenced HBase version shipped, or it will point to the location in link:http://hbase.apache.org/apidocs/index.html[Javadoc], link:https://issues.apache.org/jira/browse/HBASE[JIRA] or link:http://wiki.apache.org/hadoop/Hbase[wiki] where the pertinent information can be found.
+
+.About This Guide
+This reference guide is a work in progress. The source for this guide can be found in the _src/main/asciidoc directory of the HBase source. This reference guide is marked up using link:http://asciidoc.org/[AsciiDoc] from which the finished guide is generated as part of the 'site' build target. Run
+[source,bourne]
+----
+mvn site
+----
+to generate this documentation.
+Amendments and improvements to the documentation are welcomed.
+Click link:https://issues.apache.org/jira/secure/CreateIssueDetails!init.jspa?pid=12310753&issuetype=1&components=12312132&summary=SHORT+DESCRIPTION[this link] to file a new documentation bug against Apache HBase with some values pre-selected.
+
+.Contributing to the Documentation
+For an overview of AsciiDoc and suggestions to get started contributing to the documentation, see the <<appendix_contributing_to_documentation,relevant section later in this documentation>>.
+
+.Heads-up if this is your first foray into the world of distributed computing...
+If this is your first foray into the wonderful world of Distributed Computing, then you are in for some interesting times.
+First off, distributed systems are hard; making a distributed system hum requires a disparate skillset that spans systems (hardware and software) and networking.
+
+Your cluster's operation can hiccup because of any of a myriad set of reasons from bugs in HBase itself through misconfigurations -- misconfiguration of HBase but also operating system misconfigurations -- through to hardware problems whether it be a bug in your network card drivers or an underprovisioned RAM bus (to mention two recent examples of hardware issues that manifested as "HBase is slow"). You will also need to do a recalibration if up to this your computing has been bound to a single box.
+Here is one good starting point: link:http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing[Fallacies of Distributed Computing].
+
+That said, you are welcome. +
+It's a fun place to be. +
+Yours, the HBase Community.
+
+.Reporting Bugs
+
+Please use link:https://issues.apache.org/jira/browse/hbase[JIRA] to report non-security-related bugs.
+
+To protect existing HBase installations from new vulnerabilities, please *do not* use JIRA to report security-related bugs. Instead, send your report to the mailing list private@apache.org, which allows anyone to send messages, but restricts who can read them. Someone on that list will contact you to follow up on your report.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/rpc.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[hbase.rpc]]
+== 0.95 RPC Specification
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+In 0.95, all client/server communication is done with link:https://developers.google.com/protocol-buffers/[protobuf'ed] Messages rather than with link:http://hadoop.apache.org/docs/current/api/org/apache/hadoop/io/Writable.html[Hadoop
+ Writables].
+Our RPC wire format therefore changes.
+This document describes the client/server request/response protocol and our new RPC wire-format.
+
+
+
+For what RPC is like in 0.94 and previous, see Benoît/Tsuna's link:https://github.com/OpenTSDB/asynchbase/blob/master/src/HBaseRpc.java#L164[Unofficial
+ Hadoop / HBase RPC protocol documentation].
+For more background on how we arrived at this spec., see link:https://docs.google.com/document/d/1WCKwgaLDqBw2vpux0jPsAu2WPTRISob7HGCO8YhfDTA/edit#[HBase
+ RPC: WIP]
+
+
+
+=== Goals
+
+
+
+. A wire-format we can evolve
+. A format that does not require our rewriting server core or radically changing its current architecture (for later).
+
+=== TODO
+
+
+
+. List of problems with currently specified format and where we would like to go in a version2, etc.
+ For example, what would we have to change if anything to move server async or to support streaming/chunking?
+. Diagram on how it works
+. A grammar that succinctly describes the wire-format.
+ Currently we have these words and the content of the rpc protobuf idl but a grammar for the back and forth would help with groking rpc.
+ Also, a little state machine on client/server interactions would help with understanding (and ensuring correct implementation).
+
+=== RPC
+
+The client will send setup information on connection establish.
+Thereafter, the client invokes methods against the remote server sending a protobuf Message and receiving a protobuf Message in response.
+Communication is synchronous.
+All back and forth is preceded by an int that has the total length of the request/response.
+Optionally, Cells(KeyValues) can be passed outside of protobufs in follow-behind Cell blocks (because link:https://docs.google.com/document/d/1WEtrq-JTIUhlnlnvA0oYRLp0F8MKpEBeBSCFcQiacdw/edit#[we
+ can't protobuf megabytes of KeyValues] or Cells). These CellBlocks are encoded and optionally compressed.
+
+
+
+For more detail on the protobufs involved, see the link:http://svn.apache.org/viewvc/hbase/trunk/hbase-protocol/src/main/protobuf/RPC.proto?view=markup[RPC.proto] file in trunk.
+
+==== Connection Setup
+
+Client initiates connection.
+
+===== Client
+On connection setup, client sends a preamble followed by a connection header.
+
+.<preamble>
+[source]
+----
+<MAGIC 4 byte integer> <1 byte RPC Format Version> <1 byte auth type>
+----
+
+We need the auth method spec.
+here so the connection header is encoded if auth enabled.
+
+E.g.: HBas0x000x50 -- 4 bytes of MAGIC -- `HBas' -- plus one-byte of version, 0 in this case, and one byte, 0x50 (SIMPLE). of an auth type.
+
+.<Protobuf ConnectionHeader Message>
+Has user info, and ``protocol'', as well as the encoders and compression the client will use sending CellBlocks.
+CellBlock encoders and compressors are for the life of the connection.
+CellBlock encoders implement org.apache.hadoop.hbase.codec.Codec.
+CellBlocks may then also be compressed.
+Compressors implement org.apache.hadoop.io.compress.CompressionCodec.
+This protobuf is written using writeDelimited so is prefaced by a pb varint with its serialized length
+
+===== Server
+
+After client sends preamble and connection header, server does NOT respond if successful connection setup.
+No response means server is READY to accept requests and to give out response.
+If the version or authentication in the preamble is not agreeable or the server has trouble parsing the preamble, it will throw a org.apache.hadoop.hbase.ipc.FatalConnectionException explaining the error and will then disconnect.
+If the client in the connection header -- i.e.
+the protobuf'd Message that comes after the connection preamble -- asks for for a Service the server does not support or a codec the server does not have, again we throw a FatalConnectionException with explanation.
+
+==== Request
+
+After a Connection has been set up, client makes requests.
+Server responds.
+
+A request is made up of a protobuf RequestHeader followed by a protobuf Message parameter.
+The header includes the method name and optionally, metadata on the optional CellBlock that may be following.
+The parameter type suits the method being invoked: i.e.
+if we are doing a getRegionInfo request, the protobuf Message param will be an instance of GetRegionInfoRequest.
+The response will be a GetRegionInfoResponse.
+The CellBlock is optionally used ferrying the bulk of the RPC data: i.e Cells/KeyValues.
+
+===== Request Parts
+
+.<Total Length>
+The request is prefaced by an int that holds the total length of what follows.
+
+.<Protobuf RequestHeader Message>
+Will have call.id, trace.id, and method name, etc.
+including optional Metadata on the Cell block IFF one is following.
+Data is protobuf'd inline in this pb Message or optionally comes in the following CellBlock
+
+.<Protobuf Param Message>
+If the method being invoked is getRegionInfo, if you study the Service descriptor for the client to regionserver protocol, you will find that the request sends a GetRegionInfoRequest protobuf Message param in this position.
+
+.<CellBlock>
+An encoded and optionally compressed Cell block.
+
+==== Response
+
+Same as Request, it is a protobuf ResponseHeader followed by a protobuf Message response where the Message response type suits the method invoked.
+Bulk of the data may come in a following CellBlock.
+
+===== Response Parts
+
+.<Total Length>
+The response is prefaced by an int that holds the total length of what follows.
+
+.<Protobuf ResponseHeader Message>
+Will have call.id, etc.
+Will include exception if failed processing.
+Optionally includes metadata on optional, IFF there is a CellBlock following.
+
+.<Protobuf Response Message>
+
+Return or may be nothing if exception.
+If the method being invoked is getRegionInfo, if you study the Service descriptor for the client to regionserver protocol, you will find that the response sends a GetRegionInfoResponse protobuf Message param in this position.
+
+.<CellBlock>
+
+An encoded and optionally compressed Cell block.
+
+==== Exceptions
+
+There are two distinct types.
+There is the request failed which is encapsulated inside the response header for the response.
+The connection stays open to receive new requests.
+The second type, the FatalConnectionException, kills the connection.
+
+Exceptions can carry extra information.
+See the ExceptionResponse protobuf type.
+It has a flag to indicate do-no-retry as well as other miscellaneous payload to help improve client responsiveness.
+
+==== CellBlocks
+
+These are not versioned.
+Server can do the codec or it cannot.
+If new version of a codec with say, tighter encoding, then give it a new class name.
+Codecs will live on the server for all time so old clients can connect.
+
+=== Notes
+
+.Constraints
+In some part, current wire-format -- i.e.
+all requests and responses preceeded by a length -- has been dictated by current server non-async architecture.
+
+.One fat pb request or header+param
+We went with pb header followed by pb param making a request and a pb header followed by pb response for now.
+Doing header+param rather than a single protobuf Message with both header and param content:
+
+. Is closer to what we currently have
+. Having a single fat pb requires extra copying putting the already pb'd param into the body of the fat request pb (and same making result)
+. We can decide whether to accept the request or not before we read the param; for example, the request might be low priority.
+ As is, we read header+param in one go as server is currently implemented so this is a TODO.
+
+The advantages are minor.
+If later, fat request has clear advantage, can roll out a v2 later.
+
+[[rpc.configs]]
+==== RPC Configurations
+
+.CellBlock Codecs
+To enable a codec other than the default `KeyValueCodec`, set `hbase.client.rpc.codec` to the name of the Codec class to use.
+Codec must implement hbase's `Codec` Interface.
+After connection setup, all passed cellblocks will be sent with this codec.
+The server will return cellblocks using this same codec as long as the codec is on the servers' CLASSPATH (else you will get `UnsupportedCellCodecException`).
+
+To change the default codec, set `hbase.client.default.rpc.codec`.
+
+To disable cellblocks completely and to go pure protobuf, set the default to the empty String and do not specify a codec in your Configuration.
+So, set `hbase.client.default.rpc.codec` to the empty string and do not set `hbase.client.rpc.codec`.
+This will cause the client to connect to the server with no codec specified.
+If a server sees no codec, it will return all responses in pure protobuf.
+Running pure protobuf all the time will be slower than running with cellblocks.
+
+.Compression
+Uses hadoops compression codecs.
+To enable compressing of passed CellBlocks, set `hbase.client.rpc.compressor` to the name of the Compressor to use.
+Compressor must implement Hadoops' CompressionCodec Interface.
+After connection setup, all passed cellblocks will be sent compressed.
+The server will return cellblocks compressed using this same compressor as long as the compressor is on its CLASSPATH (else you will get `UnsupportedCompressionCodecException`).
+
+:numbered:
[16/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/asf.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[asf]]
+== HBase and the Apache Software Foundation
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+:toc: left
+:source-language: java
+
+HBase is a project in the Apache Software Foundation and as such there are responsibilities to the ASF to ensure a healthy project.
+
+[[asf.devprocess]]
+=== ASF Development Process
+
+See the link:http://www.apache.org/dev/#committers[Apache Development Process page] for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing and getting involved, and how open-source works at ASF.
+
+[[asf.reporting]]
+=== ASF Board Reporting
+
+Once a quarter, each project in the ASF portfolio submits a report to the ASF board.
+This is done by the HBase project lead and the committers.
+See link:http://www.apache.org/foundation/board/reporting[ASF board reporting] for more information.
+
+:numbered:
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/case_studies.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[casestudies]]
+= Apache HBase Case Studies
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[casestudies.overview]]
+== Overview
+
+This chapter will describe a variety of performance and troubleshooting case studies that can provide a useful blueprint on diagnosing Apache HBase cluster issues.
+
+For more information on Performance and Troubleshooting, see <<performance>> and <<trouble>>.
+
+[[casestudies.schema]]
+== Schema Design
+
+See the schema design case studies here: <<schema.casestudies>>
+
+[[casestudies.perftroub]]
+== Performance/Troubleshooting
+
+[[casestudies.slownode]]
+=== Case Study #1 (Performance Issue On A Single Node)
+
+==== Scenario
+
+Following a scheduled reboot, one data node began exhibiting unusual behavior.
+Routine MapReduce jobs run against HBase tables which regularly completed in five or six minutes began taking 30 or 40 minutes to finish.
+These jobs were consistently found to be waiting on map and reduce tasks assigned to the troubled data node (e.g., the slow map tasks all had the same Input Split). The situation came to a head during a distributed copy, when the copy was severely prolonged by the lagging node.
+
+==== Hardware
+
+.Datanodes:
+* Two 12-core processors
+* Six Enerprise SATA disks
+* 24GB of RAM
+* Two bonded gigabit NICs
+
+.Network:
+* 10 Gigabit top-of-rack switches
+* 20 Gigabit bonded interconnects between racks.
+
+==== Hypotheses
+
+===== HBase "Hot Spot" Region
+
+We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an HBase table, where uneven key-space distribution can funnel a huge number of requests to a single HBase region, bombarding the RegionServer process and cause slow response time.
+Examination of the HBase Master status page showed that the number of HBase requests to the troubled node was almost zero.
+Further, examination of the HBase logs showed that there were no region splits, compactions, or other region transitions in progress.
+This effectively ruled out a "hot spot" as the root cause of the observed slowness.
+
+===== HBase Region With Non-Local Data
+
+Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was not local to the DataNode, thus forcing HDFS to request data blocks from other servers over the network.
+Examination of the DataNode logs showed that there were very few blocks being requested over the network, indicating that the HBase region was correctly assigned, and that the majority of the necessary data was located on the node.
+This ruled out the possibility of non-local data causing a slowdown.
+
+===== Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk
+
+After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to troubleshooting the DataNode's hardware.
+Java, by design, will periodically scan its entire memory space to do garbage collection.
+If system memory is heavily overcommitted, the Linux kernel may enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to RAM as Java tries to run garbage collection.
+Further, a failing hard disk will often retry reads and/or writes many times before giving up and returning an error.
+This can manifest as high iowait, as running processes wait for reads and writes to complete.
+Finally, a disk nearing the upper edge of its performance envelope will begin to cause iowait as it informs the kernel that it cannot accept any more data, and the kernel queues incoming data into the dirty write pool in memory.
+However, using `vmstat(1)` and `free(1)`, we could see that no swap was being used, and the amount of disk IO was only a few kilobytes per second.
+
+===== Slowness Due To High Processor Usage
+
+Next, we checked to see whether the system was performing slowly simply due to very high computational load. `top(1)` showed that the system load was higher than normal, but `vmstat(1)` and `mpstat(1)` showed that the amount of processor being used for actual computation was low.
+
+===== Network Saturation (The Winner)
+
+Since neither the disks nor the processors were being utilized heavily, we moved on to the performance of the network interfaces.
+The DataNode had two gigabit ethernet adapters, bonded to form an active-standby interface. `ifconfig(8)` showed some unusual anomalies, namely interface errors, overruns, framing errors.
+While not unheard of, these kinds of errors are exceedingly rare on modern hardware which is operating as it should:
+
+----
+
+$ /sbin/ifconfig bond0
+bond0 Link encap:Ethernet HWaddr 00:00:00:00:00:00
+inet addr:10.x.x.x Bcast:10.x.x.255 Mask:255.255.255.0
+UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1
+RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 <--- Look Here! Errors!
+TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0
+collisions:0 txqueuelen:0
+RX bytes:2416328868676 (2.4 TB) TX bytes:3464991094001 (3.4 TB)
+----
+
+These errors immediately lead us to suspect that one or more of the ethernet interfaces might have negotiated the wrong line speed.
+This was confirmed both by running an ICMP ping from an external host and observing round-trip-time in excess of 700ms, and by running `ethtool(8)` on the members of the bond interface and discovering that the active interface was operating at 100Mbs/, full duplex.
+
+----
+
+$ sudo ethtool eth0
+Settings for eth0:
+Supported ports: [ TP ]
+Supported link modes: 10baseT/Half 10baseT/Full
+ 100baseT/Half 100baseT/Full
+ 1000baseT/Full
+Supports auto-negotiation: Yes
+Advertised link modes: 10baseT/Half 10baseT/Full
+ 100baseT/Half 100baseT/Full
+ 1000baseT/Full
+Advertised pause frame use: No
+Advertised auto-negotiation: Yes
+Link partner advertised link modes: Not reported
+Link partner advertised pause frame use: No
+Link partner advertised auto-negotiation: No
+Speed: 100Mb/s <--- Look Here! Should say 1000Mb/s!
+Duplex: Full
+Port: Twisted Pair
+PHYAD: 1
+Transceiver: internal
+Auto-negotiation: on
+MDI-X: Unknown
+Supports Wake-on: umbg
+Wake-on: g
+Current message level: 0x00000003 (3)
+Link detected: yes
+----
+
+In normal operation, the ICMP ping round trip time should be around 20ms, and the interface speed and duplex should read, "1000MB/s", and, "Full", respectively.
+
+==== Resolution
+
+After determining that the active ethernet adapter was at the incorrect speed, we used the `ifenslave(8)` command to make the standby interface the active interface, which yielded an immediate improvement in MapReduce performance, and a 10 times improvement in network throughput:
+
+On the next trip to the datacenter, we determined that the line speed issue was ultimately caused by a bad network cable, which was replaced.
+
+[[casestudies.perf.1]]
+=== Case Study #2 (Performance Research 2012)
+
+Investigation results of a self-described "we're not sure what's wrong, but it seems slow" problem. http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html
+
+[[casestudies.perf.2]]
+=== Case Study #3 (Performance Research 2010))
+
+Investigation results of general cluster performance from 2010.
+Although this research is on an older version of the codebase, this writeup is still very useful in terms of approach. http://hstack.org/hbase-performance-testing/
+
+[[casestudies.max.transfer.threads]]
+=== Case Study #4 (max.transfer.threads Config)
+
+Case study of configuring `max.transfer.threads` (previously known as `xcievers`) and diagnosing errors from misconfigurations. http://www.larsgeorge.com/2012/03/hadoop-hbase-and-xceivers.html
+
+See also <<dfs.datanode.max.transfer.threads>>.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/community.adoc
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[community]]
+= Community
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+== Decisions
+
+.Feature Branches
+
+Feature Branches are easy to make.
+You do not have to be a committer to make one.
+Just request the name of your branch be added to JIRA up on the developer's mailing list and a committer will add it for you.
+Thereafter you can file issues against your feature branch in Apache HBase JIRA.
+Your code you keep elsewhere -- it should be public so it can be observed -- and you can update dev mailing list on progress.
+When the feature is ready for commit, 3 +1s from committers will get your feature merged.
+See link:http://search-hadoop.com/m/asM982C5FkS1[HBase, mail # dev - Thoughts
+ about large feature dev branches]
+
+[[patchplusonepolicy]]
+.Patch +1 Policy
+
+The below policy is something we put in place 09/2012.
+It is a suggested policy rather than a hard requirement.
+We want to try it first to see if it works before we cast it in stone.
+
+Apache HBase is made of link:https://issues.apache.org/jira/browse/HBASE#selectedTab=com.atlassian.jira.plugin.system.project%3Acomponents-panel[components].
+Components have one or more <<owner,OWNER>>s.
+See the 'Description' field on the link:https://issues.apache.org/jira/browse/HBASE#selectedTab=com.atlassian.jira.plugin.system.project%3Acomponents-panel[components] JIRA page for who the current owners are by component.
+
+Patches that fit within the scope of a single Apache HBase component require, at least, a +1 by one of the component's owners before commit.
+If owners are absent -- busy or otherwise -- two +1s by non-owners will suffice.
+
+Patches that span components need at least two +1s before they can be committed, preferably +1s by owners of components touched by the x-component patch (TODO: This needs tightening up but I think fine for first pass).
+
+Any -1 on a patch by anyone vetos a patch; it cannot be committed until the justification for the -1 is addressed.
+
+[[hbase.fix.version.in.jira]]
+.How to set fix version in JIRA on issue resolve
+
+Here is how link:http://search-hadoop.com/m/azemIi5RCJ1[we agreed] to set versions in JIRA when we resolve an issue.
+If trunk is going to be 0.98.0 then:
+
+* Commit only to trunk: Mark with 0.98
+* Commit to 0.95 and trunk : Mark with 0.98, and 0.95.x
+* Commit to 0.94.x and 0.95, and trunk: Mark with 0.98, 0.95.x, and 0.94.x
+* Commit to 89-fb: Mark with 89-fb.
+* Commit site fixes: no version
+
+[[hbase.when.to.close.jira]]
+.Policy on when to set a RESOLVED JIRA as CLOSED
+
+We link:http://search-hadoop.com/m/4cIKs1iwXMS1[agreed] that for issues that list multiple releases in their _Fix Version/s_ field, CLOSE the issue on the release of any of the versions listed; subsequent change to the issue must happen in a new JIRA.
+
+[[no.permanent.state.in.zk]]
+.Only transient state in ZooKeeper!
+
+You should be able to kill the data in zookeeper and hbase should ride over it recreating the zk content as it goes.
+This is an old adage around these parts.
+We just made note of it now.
+We also are currently in violation of this basic tenet -- replication at least keeps permanent state in zk -- but we are working to undo this breaking of a golden rule.
+
+[[community.roles]]
+== Community Roles
+
+[[owner]]
+.Component Owner/Lieutenant
+
+Component owners are listed in the description field on this Apache HBase JIRA link:https://issues.apache.org/jira/browse/HBASE#selectedTab=com.atlassian.jira.plugin.system.project%3Acomponents-panel[components] page.
+The owners are listed in the 'Description' field rather than in the 'Component Lead' field because the latter only allows us list one individual whereas it is encouraged that components have multiple owners.
+
+Owners or component lieutenants are volunteers who are (usually, but not necessarily) expert in their component domain and may have an agenda on how they think their Apache HBase component should evolve.
+
+. Owners will try and review patches that land within their component's scope.
+. If applicable, if an owner has an agenda, they will publish their goals or the design toward which they are driving their component
+
+If you would like to be volunteer as a component owner, just write the dev list and we'll sign you up.
+Owners do not need to be committers.
+
+[[hbase.commit.msg.format]]
+== Commit Message format
+
+We link:http://search-hadoop.com/m/Gwxwl10cFHa1[agreed] to the following SVN commit message format:
+[source]
+----
+HBASE-xxxxx <title>. (<contributor>)
+----
+If the person making the commit is the contributor, leave off the '(<contributor>)' element.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/compression.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/compression.adoc b/src/main/asciidoc/_chapters/compression.adoc
new file mode 100644
index 0000000..42d4de5
--- /dev/null
+++ b/src/main/asciidoc/_chapters/compression.adoc
@@ -0,0 +1,459 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[appendix]
+[[compression]]
+== Compression and Data Block Encoding In HBase(((Compression,Data BlockEncoding)))
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+NOTE: Codecs mentioned in this section are for encoding and decoding data blocks or row keys.
+For information about replication codecs, see <<cluster.replication.preserving.tags,cluster.replication.preserving.tags>>.
+
+Some of the information in this section is pulled from a link:http://search-hadoop.com/m/lL12B1PFVhp1/v=threaded[discussion] on the HBase Development mailing list.
+
+HBase supports several different compression algorithms which can be enabled on a ColumnFamily.
+Data block encoding attempts to limit duplication of information in keys, taking advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys and the schema of a given table.
+Compressors reduce the size of large, opaque byte arrays in cells, and can significantly reduce the storage space needed to store uncompressed data.
+
+Compressors and data block encoding can be used together on the same ColumnFamily.
+
+.Changes Take Effect Upon Compaction
+If you change compression or encoding for a ColumnFamily, the changes take effect during compaction.
+
+Some codecs take advantage of capabilities built into Java, such as GZip compression. Others rely on native libraries. Native libraries may be available as part of Hadoop, such as LZ4. In this case, HBase only needs access to the appropriate shared library.
+
+Other codecs, such as Google Snappy, need to be installed first.
+Some codecs are licensed in ways that conflict with HBase's license and cannot be shipped as part of HBase.
+
+This section discusses common codecs that are used and tested with HBase.
+No matter what codec you use, be sure to test that it is installed correctly and is available on all nodes in your cluster.
+Extra operational steps may be necessary to be sure that codecs are available on newly-deployed nodes.
+You can use the <<compression.test,compression.test>> utility to check that a given codec is correctly installed.
+
+To configure HBase to use a compressor, see <<compressor.install,compressor.install>>.
+To enable a compressor for a ColumnFamily, see <<changing.compression,changing.compression>>.
+To enable data block encoding for a ColumnFamily, see <<data.block.encoding.enable,data.block.encoding.enable>>.
+
+.Block Compressors
+* none
+* Snappy
+* LZO
+* LZ4
+* GZ
+
+.Data Block Encoding Types
+Prefix::
+ Often, keys are very similar. Specifically, keys often share a common prefix and only differ near the end. For instance, one key might be `RowKey:Family:Qualifier0` and the next key might be `RowKey:Family:Qualifier1`.
+ +
+In Prefix encoding, an extra column is added which holds the length of the prefix shared between the current key and the previous key.
+Assuming the first key here is totally different from the key before, its prefix length is 0.
++
+The second key's prefix length is `23`, since they have the first 23 characters in common.
++
+Obviously if the keys tend to have nothing in common, Prefix will not provide much benefit.
++
+The following image shows a hypothetical ColumnFamily with no data block encoding.
++
+.ColumnFamily with No Encoding
+image::data_block_no_encoding.png[]
++
+Here is the same data with prefix data encoding.
++
+.ColumnFamily with Prefix Encoding
+image::data_block_prefix_encoding.png[]
+
+Diff::
+ Diff encoding expands upon Prefix encoding.
+ Instead of considering the key sequentially as a monolithic series of bytes, each key field is split so that each part of the key can be compressed more efficiently.
++
+Two new fields are added: timestamp and type.
++
+If the ColumnFamily is the same as the previous row, it is omitted from the current row.
++
+If the key length, value length or type are the same as the previous row, the field is omitted.
++
+In addition, for increased compression, the timestamp is stored as a Diff from the previous row's timestamp, rather than being stored in full.
+Given the two row keys in the Prefix example, and given an exact match on timestamp and the same type, neither the value length, or type needs to be stored for the second row, and the timestamp value for the second row is just 0, rather than a full timestamp.
++
+Diff encoding is disabled by default because writing and scanning are slower but more data is cached.
++
+This image shows the same ColumnFamily from the previous images, with Diff encoding.
++
+.ColumnFamily with Diff Encoding
+image::data_block_diff_encoding.png[]
+
+Fast Diff::
+ Fast Diff works similar to Diff, but uses a faster implementation. It also adds another field which stores a single bit to track whether the data itself is the same as the previous row. If it is, the data is not stored again.
++
+Fast Diff is the recommended codec to use if you have long keys or many columns.
++
+The data format is nearly identical to Diff encoding, so there is not an image to illustrate it.
+
+
+Prefix Tree::
+ Prefix tree encoding was introduced as an experimental feature in HBase 0.96.
+ It provides similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides faster random access at a cost of slower encoding speed.
++
+Prefix Tree may be appropriate for applications that have high block cache hit ratios. It introduces new 'tree' fields for the row and column.
+The row tree field contains a list of offsets/references corresponding to the cells in that row. This allows for a good deal of compression.
+For more details about Prefix Tree encoding, see link:https://issues.apache.org/jira/browse/HBASE-4676[HBASE-4676].
++
+It is difficult to graphically illustrate a prefix tree, so no image is included. See the Wikipedia article for link:http://en.wikipedia.org/wiki/Trie[Trie] for more general information about this data structure.
+
+=== Which Compressor or Data Block Encoder To Use
+
+The compression or codec type to use depends on the characteristics of your data. Choosing the wrong type could cause your data to take more space rather than less, and can have performance implications.
+
+In general, you need to weigh your options between smaller size and faster compression/decompression. Following are some general guidelines, expanded from a discussion at link:http://search-hadoop.com/m/lL12B1PFVhp1[Documenting Guidance on compression and codecs].
+
+* If you have long keys (compared to the values) or many columns, use a prefix encoder.
+ FAST_DIFF is recommended, as more testing is needed for Prefix Tree encoding.
+* If the values are large (and not precompressed, such as images), use a data block compressor.
+* Use GZIP for [firstterm]_cold data_, which is accessed infrequently.
+ GZIP compression uses more CPU resources than Snappy or LZO, but provides a higher compression ratio.
+* Use Snappy or LZO for [firstterm]_hot data_, which is accessed frequently.
+ Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high of a compression ratio.
+* In most cases, enabling Snappy or LZO by default is a good choice, because they have a low performance overhead and provide space savings.
+* Before Snappy became available by Google in 2011, LZO was the default.
+ Snappy has similar qualities as LZO but has been shown to perform better.
+
+[[hadoop.native.lib]]
+=== Making use of Hadoop Native Libraries in HBase
+
+The Hadoop shared library has a bunch of facility including compression libraries and fast crc'ing. To make this facility available to HBase, do the following. HBase/Hadoop will fall back to use alternatives if it cannot find the native library versions -- or fail outright if you asking for an explicit compressor and there is no alternative available.
+
+If you see the following in your HBase logs, you know that HBase was unable to locate the Hadoop native libraries:
+[source]
+----
+2014-08-07 09:26:20,139 WARN [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+----
+If the libraries loaded successfully, the WARN message does not show.
+
+Lets presume your Hadoop shipped with a native library that suits the platform you are running HBase on.
+To check if the Hadoop native library is available to HBase, run the following tool (available in Hadoop 2.1 and greater):
+[source]
+----
+$ ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
+2014-08-26 13:15:38,717 WARN [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+Native library checking:
+hadoop: false
+zlib: false
+snappy: false
+lz4: false
+bzip2: false
+2014-08-26 13:15:38,863 INFO [main] util.ExitUtil: Exiting with status 1
+----
+Above shows that the native hadoop library is not available in HBase context.
+
+To fix the above, either copy the Hadoop native libraries local or symlink to them if the Hadoop and HBase stalls are adjacent in the filesystem.
+You could also point at their location by setting the `LD_LIBRARY_PATH` environment variable.
+
+Where the JVM looks to find native librarys is "system dependent" (See `java.lang.System#loadLibrary(name)`). On linux, by default, is going to look in _lib/native/PLATFORM_ where `PLATFORM` is the label for the platform your HBase is installed on.
+On a local linux machine, it seems to be the concatenation of the java properties `os.name` and `os.arch` followed by whether 32 or 64 bit.
+HBase on startup prints out all of the java system properties so find the os.name and os.arch in the log.
+For example:
+[source]
+----
+...
+2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.name=Linux
+2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.arch=amd64
+...
+----
+So in this case, the PLATFORM string is `Linux-amd64-64`.
+Copying the Hadoop native libraries or symlinking at _lib/native/Linux-amd64-64_ will ensure they are found.
+Check with the Hadoop _NativeLibraryChecker_.
+
+
+Here is example of how to point at the Hadoop libs with `LD_LIBRARY_PATH` environment variable:
+[source]
+----
+$ LD_LIBRARY_PATH=~/hadoop-2.5.0-SNAPSHOT/lib/native ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
+2014-08-26 13:42:49,332 INFO [main] bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native
+2014-08-26 13:42:49,337 INFO [main] zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
+Native library checking:
+hadoop: true /home/stack/hadoop-2.5.0-SNAPSHOT/lib/native/libhadoop.so.1.0.0
+zlib: true /lib64/libz.so.1
+snappy: true /usr/lib64/libsnappy.so.1
+lz4: true revision:99
+bzip2: true /lib64/libbz2.so.1
+----
+Set in _hbase-env.sh_ the LD_LIBRARY_PATH environment variable when starting your HBase.
+
+=== Compressor Configuration, Installation, and Use
+
+[[compressor.install]]
+==== Configure HBase For Compressors
+
+Before HBase can use a given compressor, its libraries need to be available.
+Due to licensing issues, only GZ compression is available to HBase (via native Java libraries) in a default installation.
+Other compression libraries are available via the shared library bundled with your hadoop.
+The hadoop native library needs to be findable when HBase starts.
+See
+
+.Compressor Support On the Master
+
+A new configuration setting was introduced in HBase 0.95, to check the Master to determine which data block encoders are installed and configured on it, and assume that the entire cluster is configured the same.
+This option, `hbase.master.check.compression`, defaults to `true`.
+This prevents the situation described in link:https://issues.apache.org/jira/browse/HBASE-6370[HBASE-6370], where a table is created or modified to support a codec that a region server does not support, leading to failures that take a long time to occur and are difficult to debug.
+
+If `hbase.master.check.compression` is enabled, libraries for all desired compressors need to be installed and configured on the Master, even if the Master does not run a region server.
+
+.Install GZ Support Via Native Libraries
+
+HBase uses Java's built-in GZip support unless the native Hadoop libraries are available on the CLASSPATH.
+The recommended way to add libraries to the CLASSPATH is to set the environment variable `HBASE_LIBRARY_PATH` for the user running HBase.
+If native libraries are not available and Java's GZIP is used, `Got brand-new compressor` reports will be present in the logs.
+See <<brand.new.compressor,brand.new.compressor>>).
+
+[[lzo.compression]]
+.Install LZO Support
+
+HBase cannot ship with LZO because of incompatibility between HBase, which uses an Apache Software License (ASL) and LZO, which uses a GPL license.
+See the link:http://wiki.apache.org/hadoop/UsingLzoCompression[Using LZO
+ Compression] wiki page for information on configuring LZO support for HBase.
+
+If you depend upon LZO compression, consider configuring your RegionServers to fail to start if LZO is not available.
+See <<hbase.regionserver.codecs,hbase.regionserver.codecs>>.
+
+[[lz4.compression]]
+.Configure LZ4 Support
+
+LZ4 support is bundled with Hadoop.
+Make sure the hadoop shared library (libhadoop.so) is accessible when you start HBase.
+After configuring your platform (see <<hbase.native.platform,hbase.native.platform>>), you can make a symbolic link from HBase to the native Hadoop libraries.
+This assumes the two software installs are colocated.
+For example, if my 'platform' is Linux-amd64-64:
+[source,bourne]
+----
+$ cd $HBASE_HOME
+$ mkdir lib/native
+$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64
+----
+Use the compression tool to check that LZ4 is installed on all nodes.
+Start up (or restart) HBase.
+Afterward, you can create and alter tables to enable LZ4 as a compression codec.:
+----
+hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
+----
+
+[[snappy.compression.installation]]
+.Install Snappy Support
+
+HBase does not ship with Snappy support because of licensing issues.
+You can install Snappy binaries (for instance, by using +yum install snappy+ on CentOS) or build Snappy from source.
+After installing Snappy, search for the shared library, which will be called _libsnappy.so.X_ where X is a number.
+If you built from source, copy the shared library to a known location on your system, such as _/opt/snappy/lib/_.
+
+In addition to the Snappy library, HBase also needs access to the Hadoop shared library, which will be called something like _libhadoop.so.X.Y_, where X and Y are both numbers.
+Make note of the location of the Hadoop library, or copy it to the same location as the Snappy library.
+
+[NOTE]
+====
+The Snappy and Hadoop libraries need to be available on each node of your cluster.
+See <<compression.test,compression.test>> to find out how to test that this is the case.
+
+See <<hbase.regionserver.codecs,hbase.regionserver.codecs>> to configure your RegionServers to fail to start if a given compressor is not available.
+====
+
+Each of these library locations need to be added to the environment variable `HBASE_LIBRARY_PATH` for the operating system user that runs HBase.
+You need to restart the RegionServer for the changes to take effect.
+
+[[compression.test]]
+.CompressionTest
+
+You can use the CompressionTest tool to verify that your compressor is available to HBase:
+
+----
+
+ $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
+----
+
+[[hbase.regionserver.codecs]]
+.Enforce Compression Settings On a RegionServer
+
+You can configure a RegionServer so that it will fail to restart if compression is configured incorrectly, by adding the option hbase.regionserver.codecs to the _hbase-site.xml_, and setting its value to a comma-separated list of codecs that need to be available.
+For example, if you set this property to `lzo,gz`, the RegionServer would fail to start if both compressors were not available.
+This would prevent a new server from being added to the cluster without having codecs configured properly.
+
+[[changing.compression]]
+==== Enable Compression On a ColumnFamily
+
+To enable compression for a ColumnFamily, use an `alter` command.
+You do not need to re-create the table or copy data.
+If you are changing codecs, be sure the old codec is still available until all the old StoreFiles have been compacted.
+
+.Enabling Compression on a ColumnFamily of an Existing Table using HBaseShell
+====
+----
+
+hbase> disable 'test'
+hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'}
+hbase> enable 'test'
+----
+====
+
+.Creating a New Table with Compression On a ColumnFamily
+====
+----
+
+hbase> create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' }
+----
+====
+
+.Verifying a ColumnFamily's Compression Settings
+====
+----
+
+hbase> describe 'test'
+DESCRIPTION ENABLED
+ 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false
+ ', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
+ VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS
+ => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa
+ lse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B
+ LOCKCACHE => 'true'}
+1 row(s) in 0.1070 seconds
+----
+====
+
+==== Testing Compression Performance
+
+HBase includes a tool called LoadTestTool which provides mechanisms to test your compression performance.
+You must specify either `-write` or `-update-read` as your first parameter, and if you do not specify another parameter, usage advice is printed for each option.
+
+.+LoadTestTool+ Usage
+====
+----
+
+$ bin/hbase org.apache.hadoop.hbase.util.LoadTestTool -h
+usage: bin/hbase org.apache.hadoop.hbase.util.LoadTestTool <options>
+Options:
+ -batchupdate Whether to use batch as opposed to separate
+ updates for every column in a row
+ -bloom <arg> Bloom filter type, one of [NONE, ROW, ROWCOL]
+ -compression <arg> Compression type, one of [LZO, GZ, NONE, SNAPPY,
+ LZ4]
+ -data_block_encoding <arg> Encoding algorithm (e.g. prefix compression) to
+ use for data blocks in the test column family, one
+ of [NONE, PREFIX, DIFF, FAST_DIFF, PREFIX_TREE].
+ -encryption <arg> Enables transparent encryption on the test table,
+ one of [AES]
+ -generator <arg> The class which generates load for the tool. Any
+ args for this class can be passed as colon
+ separated after class name
+ -h,--help Show usage
+ -in_memory Tries to keep the HFiles of the CF inmemory as far
+ as possible. Not guaranteed that reads are always
+ served from inmemory
+ -init_only Initialize the test table only, don't do any
+ loading
+ -key_window <arg> The 'key window' to maintain between reads and
+ writes for concurrent write/read workload. The
+ default is 0.
+ -max_read_errors <arg> The maximum number of read errors to tolerate
+ before terminating all reader threads. The default
+ is 10.
+ -multiput Whether to use multi-puts as opposed to separate
+ puts for every column in a row
+ -num_keys <arg> The number of keys to read/write
+ -num_tables <arg> A positive integer number. When a number n is
+ speicfied, load test tool will load n table
+ parallely. -tn parameter value becomes table name
+ prefix. Each table name is in format
+ <tn>_1...<tn>_n
+ -read <arg> <verify_percent>[:<#threads=20>]
+ -regions_per_server <arg> A positive integer number. When a number n is
+ specified, load test tool will create the test
+ table with n regions per server
+ -skip_init Skip the initialization; assume test table already
+ exists
+ -start_key <arg> The first key to read/write (a 0-based index). The
+ default value is 0.
+ -tn <arg> The name of the table to read or write
+ -update <arg> <update_percent>[:<#threads=20>][:<#whether to
+ ignore nonce collisions=0>]
+ -write <arg> <avg_cols_per_key>:<avg_data_size>[:<#threads=20>]
+ -zk <arg> ZK quorum as comma-separated host names without
+ port numbers
+ -zk_root <arg> name of parent znode in zookeeper
+----
+====
+
+.Example Usage of LoadTestTool
+====
+----
+
+$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000
+ -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE
+----
+====
+
+[[data.block.encoding.enable]]
+== Enable Data Block Encoding
+
+Codecs are built into HBase so no extra configuration is needed.
+Codecs are enabled on a table by setting the `DATA_BLOCK_ENCODING` property.
+Disable the table before altering its DATA_BLOCK_ENCODING setting.
+Following is an example using HBase Shell:
+
+.Enable Data Block Encoding On a Table
+====
+----
+
+hbase> disable 'test'
+hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
+Updating all regions with the new schema...
+0/1 regions updated.
+1/1 regions updated.
+Done.
+0 row(s) in 2.2820 seconds
+hbase> enable 'test'
+0 row(s) in 0.1580 seconds
+----
+====
+
+.Verifying a ColumnFamily's Data Block Encoding
+====
+----
+
+hbase> describe 'test'
+DESCRIPTION ENABLED
+ 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true
+ _DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE =>
+ '0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS
+ IONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =
+ > 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals
+ e', BLOCKCACHE => 'true'}
+1 row(s) in 0.0650 seconds
+----
+====
+
+:numbered:
+
+ifdef::backend-docbook[]
+[index]
+== Index
+// Generated automatically by the DocBook toolchain.
+endif::backend-docbook[]
[13/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/developer.adoc
----------------------------------------------------------------------
diff --git a/src/main/asciidoc/_chapters/developer.adoc b/src/main/asciidoc/_chapters/developer.adoc
new file mode 100644
index 0000000..ee03614
--- /dev/null
+++ b/src/main/asciidoc/_chapters/developer.adoc
@@ -0,0 +1,1971 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[developer]]
+= Building and Developing Apache HBase
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+This chapter contains information and guidelines for building and releasing HBase code and documentation.
+Being familiar with these guidelines will help the HBase committers to use your contributions more easily.
+
+[[getting.involved]]
+== Getting Involved
+
+Apache HBase gets better only when people contribute! If you are looking to contribute to Apache HBase, look for link:https://issues.apache.org/jira/issues/?jql=project%20%3D%20HBASE%20AND%20labels%20in%20(beginner)[issues in JIRA tagged with the label 'beginner'].
+These are issues HBase contributors have deemed worthy but not of immediate priority and a good way to ramp on HBase internals.
+See link:http://search-hadoop.com/m/DHED43re96[What label
+ is used for issues that are good on ramps for new contributors?] from the dev mailing list for background.
+
+Before you get started submitting code to HBase, please refer to <<developing,developing>>.
+
+As Apache HBase is an Apache Software Foundation project, see <<asf,asf>> for more information about how the ASF functions.
+
+[[mailing.list]]
+=== Mailing Lists
+
+Sign up for the dev-list and the user-list.
+See the link:http://hbase.apache.org/mail-lists.html[mailing lists] page.
+Posing questions - and helping to answer other people's questions - is encouraged! There are varying levels of experience on both lists so patience and politeness are encouraged (and please stay on topic.)
+
+[[irc]]
+=== Internet Relay Chat (IRC)
+
+For real-time questions and discussions, use the `#hbase` IRC channel on the link:https://freenode.net/[FreeNode] IRC network.
+FreeNode offers a web-based client, but most people prefer a native client, and several clients are available for each operating system.
+
+=== Jira
+
+Check for existing issues in link:https://issues.apache.org/jira/browse/HBASE[Jira].
+If it's either a new feature request, enhancement, or a bug, file a ticket.
+
+To check for existing issues which you can tackle as a beginner, search for link:https://issues.apache.org/jira/issues/?jql=project%20%3D%20HBASE%20AND%20labels%20in%20(beginner)[issues in JIRA tagged with the label 'beginner'].
+
+* .JIRA PrioritiesBlocker: Should only be used if the issue WILL cause data loss or cluster instability reliably.
+* Critical: The issue described can cause data loss or cluster instability in some cases.
+* Major: Important but not tragic issues, like updates to the client API that will add a lot of much-needed functionality or significant bugs that need to be fixed but that don't cause data loss.
+* Minor: Useful enhancements and annoying but not damaging bugs.
+* Trivial: Useful enhancements but generally cosmetic.
+
+.Code Blocks in Jira Comments
+====
+A commonly used macro in Jira is {code}. Everything inside the tags is preformatted, as in this example.
+
+[source]
+----
+
+{code}
+code snippet
+{code}
+----
+====
+
+[[repos]]
+== Apache HBase Repositories
+
+There are two different repositories for Apache HBase: Subversion (SVN) and Git.
+GIT is our repository of record for all but the Apache HBase website.
+We used to be on SVN.
+We migrated.
+See link:https://issues.apache.org/jira/browse/INFRA-7768[Migrate Apache HBase SVN Repos to Git].
+Updating hbase.apache.org still requires use of SVN (See <<hbase.org,hbase.org>>). See link:http://hbase.apache.org/source-repository.html[Source Code
+ Management] page for contributor and committer links or seach for HBase on the link:http://git.apache.org/[Apache Git] page.
+
+== IDEs
+
+=== Eclipse
+
+[[eclipse.code.formatting]]
+==== Code Formatting
+
+Under the _dev-support/_ folder, you will find _hbase_eclipse_formatter.xml_.
+We encourage you to have this formatter in place in eclipse when editing HBase code.
+
+.Procedure: Load the HBase Formatter Into Eclipse
+. Open the menu item.
+. In Preferences, click the menu item.
+. Click btn:[Import] and browse to the location of the _hbase_eclipse_formatter.xml_ file, which is in the _dev-support/_ directory.
+ Click btn:[Apply].
+. Still in Preferences, click .
+ Be sure the following options are selected:
++
+* Perform the selected actions on save
+* Format source code
+* Format edited lines
++
+Click btn:[Apply].
+Close all dialog boxes and return to the main window.
+
+
+In addition to the automatic formatting, make sure you follow the style guidelines explained in <<common.patch.feedback,common.patch.feedback>>
+
+Also, no `@author` tags - that's a rule.
+Quality Javadoc comments are appreciated.
+And include the Apache license.
+
+[[eclipse.git.plugin]]
+==== Eclipse Git Plugin
+
+If you cloned the project via git, download and install the Git plugin (EGit). Attach to your local git repo (via the [label]#Git Repositories# window) and you'll be able to see file revision history, generate patches, etc.
+
+[[eclipse.maven.setup]]
+==== HBase Project Setup in Eclipse using `m2eclipse`
+
+The easiest way is to use the +m2eclipse+ plugin for Eclipse.
+Eclipse Indigo or newer includes +m2eclipse+, or you can download it from link:http://www.eclipse.org/m2e//. It provides Maven integration for Eclipse, and even lets you use the direct Maven commands from within Eclipse to compile and test your project.
+
+To import the project, click and select the HBase root directory. `m2eclipse` locates all the hbase modules for you.
+
+If you install +m2eclipse+ and import HBase in your workspace, do the following to fix your eclipse Build Path.
+
+. Remove _target_ folder
+. Add _target/generated-jamon_ and _target/generated-sources/java_ folders.
+. Remove from your Build Path the exclusions on the _src/main/resources_ and _src/test/resources_ to avoid error message in the console, such as the following:
++
+----
+Failed to execute goal
+org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase:
+'An Ant BuildException has occured: Replace: source file .../target/classes/hbase-default.xml
+doesn't exist
+----
++
+This will also reduce the eclipse build cycles and make your life easier when developing.
+
+
+[[eclipse.commandline]]
+==== HBase Project Setup in Eclipse Using the Command Line
+
+Instead of using `m2eclipse`, you can generate the Eclipse files from the command line.
+
+. First, run the following command, which builds HBase.
+ You only need to do this once.
++
+[source,bourne]
+----
+mvn clean install -DskipTests
+----
+
+. Close Eclipse, and execute the following command from the terminal, in your local HBase project directory, to generate new _.project_ and _.classpath_ files.
++
+[source,bourne]
+----
+mvn eclipse:eclipse
+----
+
+. Reopen Eclipse and import the _.project_ file in the HBase directory to a workspace.
+
+[[eclipse.maven.class]]
+==== Maven Classpath Variable
+
+The `$M2_REPO` classpath variable needs to be set up for the project.
+This needs to be set to your local Maven repository, which is usually _~/.m2/repository_
+
+If this classpath variable is not configured, you will see compile errors in Eclipse like this:
+
+----
+
+Description Resource Path Location Type
+The project cannot be built until build path errors are resolved hbase Unknown Java Problem
+Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase' hbase Build path Build Path Problem
+Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in project 'hbase' hbase Build path Build Path Problem
+Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-java-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem Unbound classpath variable:
+----
+
+[[eclipse.issues]]
+==== Eclipse Known Issues
+
+Eclipse will currently complain about _Bytes.java_.
+It is not possible to turn these errors off.
+
+----
+
+Description Resource Path Location Type
+Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1061 Java Problem
+Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1064 Java Problem
+Access restriction: The method getLong(Object, long) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1111 Java Problem
+----
+
+[[eclipse.more]]
+==== Eclipse - More Information
+
+For additional information on setting up Eclipse for HBase development on Windows, see link:http://michaelmorello.blogspot.com/2011/09/hbase-subversion-eclipse-windows.html[Michael Morello's blog] on the topic.
+
+=== IntelliJ IDEA
+
+You can set up IntelliJ IDEA for similar functinoality as Eclipse.
+Follow these steps.
+
+. Select
+. You do not need to select a profile.
+ Be sure [label]#Maven project
+ required# is selected, and click btn:[Next].
+. Select the location for the JDK.
+
+.Using the HBase Formatter in IntelliJ IDEA
+Using the Eclipse Code Formatter plugin for IntelliJ IDEA, you can import the HBase code formatter described in <<eclipse.code.formatting,eclipse.code.formatting>>.
+
+=== Other IDEs
+
+It would be userful to mirror the <<eclipse,eclipse>> set-up instructions for other IDEs.
+If you would like to assist, please have a look at link:https://issues.apache.org/jira/browse/HBASE-11704[HBASE-11704].
+
+[[build]]
+== Building Apache HBase
+
+[[build.basic]]
+=== Basic Compile
+
+HBase is compiled using Maven.
+You must use Maven 3.x.
+To check your Maven version, run the command +mvn -version+.
+
+.JDK Version Requirements
+[NOTE]
+====
+Starting with HBase 1.0 you must use Java 7 or later to build from source code.
+See <<java,java>> for more complete information about supported JDK versions.
+====
+
+[[maven.build.commands]]
+==== Maven Build Commands
+
+All commands are executed from the local HBase project directory.
+
+===== Package
+
+The simplest command to compile HBase from its java source code is to use the `package` target, which builds JARs with the compiled files.
+
+[source,bourne]
+----
+mvn package -DskipTests
+----
+
+Or, to clean up before compiling:
+
+[source,bourne]
+----
+mvn clean package -DskipTests
+----
+
+With Eclipse set up as explained above in <<eclipse,eclipse>>, you can also use the menu:Build[] command in Eclipse.
+To create the full installable HBase package takes a little bit more work, so read on.
+
+[[maven.build.commands.compile]]
+===== Compile
+
+The `compile` target does not create the JARs with the compiled files.
+
+[source,bourne]
+----
+mvn compile
+----
+
+[source,bourne]
+----
+mvn clean compile
+----
+
+===== Install
+
+To install the JARs in your _~/.m2/_ directory, use the `install` target.
+
+[source,bourne]
+----
+mvn install
+----
+
+[source,bourne]
+----
+mvn clean install
+----
+
+[source,bourne]
+----
+mvn clean install -DskipTests
+----
+
+[[maven.build.commands.unitall]]
+==== Running all or individual Unit Tests
+
+See the <<hbase.unittests.cmds,hbase.unittests.cmds>> section in <<hbase.unittests,hbase.unittests>>
+
+[[maven.build.hadoop]]
+==== Building against various hadoop versions.
+
+As of 0.96, Apache HBase supports building against Apache Hadoop versions: 1.0.3, 2.0.0-alpha and 3.0.0-SNAPSHOT.
+By default, in 0.96 and earlier, we will build with Hadoop-1.0.x.
+As of 0.98, Hadoop 1.x is deprecated and Hadoop 2.x is the default.
+To change the version to build against, add a hadoop.profile property when you invoke +mvn+:
+
+[source,bourne]
+----
+mvn -Dhadoop.profile=1.0 ...
+----
+
+The above will build against whatever explicit hadoop 1.x version we have in our _pom.xml_ as our '1.0' version.
+Tests may not all pass so you may need to pass `-DskipTests` unless you are inclined to fix the failing tests.
+
+.'dependencyManagement.dependencies.dependency.artifactId' fororg.apache.hbase:${compat.module}:test-jar with value '${compat.module}'does not match a valid id pattern
+[NOTE]
+====
+You will see ERRORs like the above title if you pass the _default_ profile; e.g.
+if you pass +hadoop.profile=1.1+ when building 0.96 or +hadoop.profile=2.0+ when building hadoop 0.98; just drop the hadoop.profile stipulation in this case to get your build to run again.
+This seems to be a maven pecularity that is probably fixable but we've not spent the time trying to figure it.
+====
+
+Similarly, for 3.0, you would just replace the profile value.
+Note that Hadoop-3.0.0-SNAPSHOT does not currently have a deployed maven artificat - you will need to build and install your own in your local maven repository if you want to run against this profile.
+
+In earilier versions of Apache HBase, you can build against older versions of Apache Hadoop, notably, Hadoop 0.22.x and 0.23.x.
+If you are running, for example HBase-0.94 and wanted to build against Hadoop 0.23.x, you would run with:
+
+[source,bourne]
+----
+mvn -Dhadoop.profile=22 ...
+----
+
+[[build.protobuf]]
+==== Build Protobuf
+
+You may need to change the protobuf definitions that reside in the _hbase-protocol_ module or other modules.
+
+The protobuf files are located in _hbase-protocol/src/main/protobuf_.
+For the change to be effective, you will need to regenerate the classes.
+You can use maven profile `compile-protobuf` to do this.
+
+[source,bourne]
+----
+mvn compile -Pcompile-protobuf
+----
+
+You may also want to define `protoc.path` for the protoc binary, using the following command:
+
+[source,bourne]
+----
+
+mvn compile -Pcompile-protobuf -Dprotoc.path=/opt/local/bin/protoc
+----
+
+Read the _hbase-protocol/README.txt_ for more details.
+
+[[build.thrift]]
+==== Build Thrift
+
+You may need to change the thrift definitions that reside in the _hbase-thrift_ module or other modules.
+
+The thrift files are located in _hbase-thrift/src/main/resources_.
+For the change to be effective, you will need to regenerate the classes.
+You can use maven profile `compile-thrift` to do this.
+
+[source,bourne]
+----
+mvn compile -Pcompile-thrift
+----
+
+You may also want to define `thrift.path` for the thrift binary, using the following command:
+
+[source,bourne]
+----
+
+ mvn compile -Pcompile-thrift -Dthrift.path=/opt/local/bin/thrift
+----
+
+==== Build a Tarball
+
+You can build a tarball without going through the release process described in <<releasing,releasing>>, by running the following command:
+
+----
+mvn -DskipTests clean install && mvn -DskipTests package assembly:single
+----
+
+The distribution tarball is built in _hbase-assembly/target/hbase-<version>-bin.tar.gz_.
+
+You can install or deploy the tarball by having the assembly:single goal before install or deploy in the maven command:
+
+----
+mvn -DskipTests package assembly:single install
+----
+----
+mvn -DskipTests package assembly:single deploy
+----
+
+
+[[build.gotchas]]
+==== Build Gotchas
+
+If you see `Unable to find resource 'VM_global_library.vm'`, ignore it.
+Its not an error.
+It is link:http://jira.codehaus.org/browse/MSITE-286[officially
+ ugly] though.
+
+[[build.snappy]]
+==== Building in snappy compression support
+
+Pass `-Psnappy` to trigger the `hadoop-snappy` maven profile for building Google Snappy native libraries into HBase.
+See also <<snappy.compression.installation,snappy.compression.installation>>
+
+[[releasing]]
+== Releasing Apache HBase
+
+.Building against HBase 1.x
+[NOTE]
+====
+HBase 1.x requires Java 7 to build.
+See <<java,java>> for Java requirements per HBase release.
+====
+
+=== Building against HBase 0.96-0.98
+
+HBase 0.96.x will run on Hadoop 1.x or Hadoop 2.x.
+HBase 0.98 still runs on both, but HBase 0.98 deprecates use of Hadoop 1.
+HBase 1.x will _not_ run on Hadoop 1.
+In the following procedures, we make a distinction between HBase 1.x builds and the awkward process involved building HBase 0.96/0.98 for either Hadoop 1 or Hadoop 2 targets.
+
+You must choose which Hadoop to build against.
+It is not possible to build a single HBase binary that runs against both Hadoop 1 and Hadoop 2.
+Hadoop is included in the build, because it is needed to run HBase in standalone mode.
+Therefore, the set of modules included in the tarball changes, depending on the build target.
+To determine which HBase you have, look at the HBase version.
+The Hadoop version is embedded within it.
+
+Maven, our build system, natively does not allow a single product to be built against different dependencies.
+Also, Maven cannot change the set of included modules and write out the correct _pom.xml_ files with appropriate dependencies, even using two build targets, one for Hadoop 1 and another for Hadoop 2.
+A prerequisite step is required, which takes as input the current _pom.xml_s and generates Hadoop 1 or Hadoop 2 versions using a script in the _dev-tools/_ directory, called _generate-hadoopX-poms.sh_ where [replaceable]_X_ is either `1` or `2`.
+You then reference these generated poms when you build.
+For now, just be aware of the difference between HBase 1.x builds and those of HBase 0.96-0.98.
+This difference is important to the build instructions.
+
+.Example _~/.m2/settings.xml_ File
+====
+Publishing to maven requires you sign the artifacts you want to upload.
+For the build to sign them for you, you a properly configured _settings.xml_ in your local repository under _.m2_, such as the following.
+
+[source,xml]
+----
+<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+ xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
+ http://maven.apache.org/xsd/settings-1.0.0.xsd">
+ <servers>
+ <!- To publish a snapshot of some part of Maven -->
+ <server>
+ <id>apache.snapshots.https</id>
+ <username>YOUR_APACHE_ID
+ </username>
+ <password>YOUR_APACHE_PASSWORD
+ </password>
+ </server>
+ <!-- To publish a website using Maven -->
+ <!-- To stage a release of some part of Maven -->
+ <server>
+ <id>apache.releases.https</id>
+ <username>YOUR_APACHE_ID
+ </username>
+ <password>YOUR_APACHE_PASSWORD
+ </password>
+ </server>
+ </servers>
+ <profiles>
+ <profile>
+ <id>apache-release</id>
+ <properties>
+ <gpg.keyname>YOUR_KEYNAME</gpg.keyname>
+ <!--Keyname is something like this ... 00A5F21E... do gpg --list-keys to find it-->
+ <gpg.passphrase>YOUR_KEY_PASSWORD
+ </gpg.passphrase>
+ </properties>
+ </profile>
+ </profiles>
+</settings>
+----
+====
+
+[[maven.release]]
+=== Making a Release Candidate
+
+NOTE: These instructions are for building HBase 1.0.x.
+For building earlier versions, the process is different.
+See this section under the respective release documentation folders.
+
+.Point Releases
+If you are making a point release (for example to quickly address a critical incompatability or security problem) off of a release branch instead of a development branch, the tagging instructions are slightly different.
+I'll prefix those special steps with _Point Release Only_.
+
+.Before You Begin
+Before you make a release candidate, do a practice run by deploying a snapshot.
+Before you start, check to be sure recent builds have been passing for the branch from where you are going to take your release.
+You should also have tried recent branch tips out on a cluster under load, perhaps by running the `hbase-it` integration test suite for a few hours to 'burn in' the near-candidate bits.
+
+.Point Release Only
+[NOTE]
+====
+At this point you should tag the previous release branch (ex: 0.96.1) with the new point release tag (e.g.
+0.96.1.1 tag). Any commits with changes for the point release should be appled to the new tag.
+====
+
+The Hadoop link:http://wiki.apache.org/hadoop/HowToRelease[How To
+ Release] wiki page is used as a model for most of the instructions below, and may have more detail on particular sections, so it is worth review.
+
+.Specifying the Heap Space for Maven on OSX
+[NOTE]
+====
+On OSX, you may need to specify the heap space for Maven commands, by setting the `MAVEN_OPTS` variable to `-Xmx3g`.
+You can prefix the variable to the Maven command, as in the following example:
+
+----
+MAVEN_OPTS="-Xmx2g" mvn package
+----
+
+You could also set this in an environment variable or alias in your shell.
+====
+
+
+NOTE: The script _dev-support/make_rc.sh_ automates many of these steps.
+It does not do the modification of the _CHANGES.txt_ for the release, the close of the staging repository in Apache Maven (human intervention is needed here), the checking of the produced artifacts to ensure they are 'good' -- e.g.
+extracting the produced tarballs, verifying that they look right, then starting HBase and checking that everything is running correctly, then the signing and pushing of the tarballs to link:http://people.apache.org[people.apache.org].
+The script handles everything else, and comes in handy.
+
+.Procedure: Release Procedure
+. Update the _CHANGES.txt_ file and the POM files.
++
+Update _CHANGES.txt_ with the changes since the last release.
+Make sure the URL to the JIRA points to the proper location which lists fixes for this release.
+Adjust the version in all the POM files appropriately.
+If you are making a release candidate, you must remove the `-SNAPSHOT` label from all versions.
+If you are running this receipe to publish a snapshot, you must keep the `-SNAPSHOT` suffix on the hbase version.
+The link:http://mojo.codehaus.org/versions-maven-plugin/[Versions
+ Maven Plugin] can be of use here.
+To set a version in all the many poms of the hbase multi-module project, use a command like the following:
++
+[source,bourne]
+----
+
+$ mvn clean org.codehaus.mojo:versions-maven-plugin:1.3.1:set -DnewVersion=0.96.0
+----
++
+Checkin the _CHANGES.txt_ and any version changes.
+
+. Update the documentation.
++
+Update the documentation under _src/main/docbkx_.
+This usually involves copying the latest from trunk and making version-particular adjustments to suit this release candidate version.
+
+. Build the source tarball.
++
+Now, build the source tarball.
+This tarball is Hadoop-version-independent.
+It is just the pure source code and documentation without a particular hadoop taint, etc.
+Add the `-Prelease` profile when building.
+It checks files for licenses and will fail the build if unlicensed files are present.
++
+[source,bourne]
+----
+
+$ mvn clean install -DskipTests assembly:single -Dassembly.file=hbase-assembly/src/main/assembly/src.xml -Prelease
+----
++
+Extract the tarball and make sure it looks good.
+A good test for the src tarball being 'complete' is to see if you can build new tarballs from this source bundle.
+If the source tarball is good, save it off to a _version directory_, a directory somewhere where you are collecting all of the tarballs you will publish as part of the release candidate.
+For example if you were building a hbase-0.96.0 release candidate, you might call the directory _hbase-0.96.0RC0_.
+Later you will publish this directory as our release candidate up on http://people.apache.org/~YOU.
+
+. Build the binary tarball.
++
+Next, build the binary tarball.
+Add the `-Prelease` profile when building.
+It checks files for licenses and will fail the build if unlicensed files are present.
+Do it in two steps.
++
+* First install into the local repository
++
+[source,bourne]
+----
+
+$ mvn clean install -DskipTests -Prelease
+----
+
+* Next, generate documentation and assemble the tarball.
++
+[source,bourne]
+----
+
+$ mvn install -DskipTests site assembly:single -Prelease
+----
+
++
+Otherwise, the build complains that hbase modules are not in the maven repository when you try to do it at once, especially on fresh repository.
+It seems that you need the install goal in both steps.
++
+Extract the generated tarball and check it out.
+Look at the documentation, see if it runs, etc.
+If good, copy the tarball to the above mentioned _version directory_.
+
+. Create a new tag.
++
+.Point Release Only
+[NOTE]
+====
+The following step that creates a new tag can be skipped since you've already created the point release tag
+====
++
+Tag the release at this point since it looks good.
+If you find an issue later, you can delete the tag and start over.
+Release needs to be tagged for the next step.
+
+. Deploy to the Maven Repository.
++
+Next, deploy HBase to the Apache Maven repository, using the `apache-release` profile instead of the `release` profile when running the `mvn deploy` command.
+This profile invokes the Apache pom referenced by our pom files, and also signs your artifacts published to Maven, as long as the _settings.xml_ is configured correctly, as described in <<mvn.settings.file,mvn.settings.file>>.
++
+[source,bourne]
+----
+
+$ mvn deploy -DskipTests -Papache-release
+----
++
+This command copies all artifacts up to a temporary staging Apache mvn repository in an 'open' state.
+More work needs to be done on these maven artifacts to make them generally available.
++
+We do not release HBase tarball to the Apache Maven repository. To avoid deploying the tarball, do not include the `assembly:single` goal in your `mvn deploy` command. Check the deployed artifacts as described in the next section.
+
+. Make the Release Candidate available.
++
+The artifacts are in the maven repository in the staging area in the 'open' state.
+While in this 'open' state you can check out what you've published to make sure all is good.
+To do this, login at link:http://repository.apache.org[repository.apache.org] using your Apache ID.
+Find your artifacts in the staging repository.
+Browse the content.
+Make sure all artifacts made it up and that the poms look generally good.
+If it checks out, 'close' the repo.
+This will make the artifacts publically available.
+You will receive an email with the URL to give out for the temporary staging repository for others to use trying out this new release candidate.
+Include it in the email that announces the release candidate.
+Folks will need to add this repo URL to their local poms or to their local _settings.xml_ file to pull the published release candidate artifacts.
+If the published artifacts are incomplete or have problems, just delete the 'open' staged artifacts.
++
+.hbase-downstreamer
+[NOTE]
+====
+See the link:https://github.com/saintstack/hbase-downstreamer[hbase-downstreamer] test for a simple example of a project that is downstream of HBase an depends on it.
+Check it out and run its simple test to make sure maven artifacts are properly deployed to the maven repository.
+Be sure to edit the pom to point to the proper staging repository.
+Make sure you are pulling from the repository when tests run and that you are not getting from your local repository, by either passing the `-U` flag or deleting your local repo content and check maven is pulling from remote out of the staging repository.
+====
++
+See link:http://www.apache.org/dev/publishing-maven-artifacts.html[Publishing Maven Artifacts] for some pointers on this maven staging process.
++
+NOTE: We no longer publish using the maven release plugin.
+Instead we do +mvn deploy+.
+It seems to give us a backdoor to maven release publishing.
+If there is no _-SNAPSHOT_ on the version string, then we are 'deployed' to the apache maven repository staging directory from which we can publish URLs for candidates and later, if they pass, publish as release (if a _-SNAPSHOT_ on the version string, deploy will put the artifacts up into apache snapshot repos).
++
+If the HBase version ends in `-SNAPSHOT`, the artifacts go elsewhere.
+They are put into the Apache snapshots repository directly and are immediately available.
+Making a SNAPSHOT release, this is what you want to happen.
+
+. If you used the _make_rc.sh_ script instead of doing
+ the above manually, do your sanity checks now.
++
+At this stage, you have two tarballs in your 'version directory' and a set of artifacts in a staging area of the maven repository, in the 'closed' state.
+These are publicly accessible in a temporary staging repository whose URL you should have gotten in an email.
+The above mentioned script, _make_rc.sh_ does all of the above for you minus the check of the artifacts built, the closing of the staging repository up in maven, and the tagging of the release.
+If you run the script, do your checks at this stage verifying the src and bin tarballs and checking what is up in staging using hbase-downstreamer project.
+Tag before you start the build.
+You can always delete it if the build goes haywire.
+
+. Sign, upload, and 'stage' your version directory to link:http://people.apache.org[people.apache.org] (TODO:
+ There is a new location to stage releases using svnpubsub. See
+ (link:https://issues.apache.org/jira/browse/HBASE-10554[HBASE-10554 Please delete old releases from mirroring system]).
++
+If all checks out, next put the _version directory_ up on link:http://people.apache.org[people.apache.org].
+You will need to sign and fingerprint them before you push them up.
+In the _version directory_ run the following commands:
++
+[source,bourne]
+----
+
+$ for i in *.tar.gz; do echo $i; gpg --print-mds $i > $i.mds ; done
+$ for i in *.tar.gz; do echo $i; gpg --armor --output $i.asc --detach-sig $i ; done
+$ cd ..
+# Presuming our 'version directory' is named 0.96.0RC0, now copy it up to people.apache.org.
+$ rsync -av 0.96.0RC0 people.apache.org:public_html
+----
++
+Make sure the link:http://people.apache.org[people.apache.org] directory is showing and that the mvn repo URLs are good.
+Announce the release candidate on the mailing list and call a vote.
+
+
+[[maven.snapshot]]
+=== Publishing a SNAPSHOT to maven
+
+Make sure your _settings.xml_ is set up properly, as in <<mvn.settings.file,mvn.settings.file>>.
+Make sure the hbase version includes `-SNAPSHOT` as a suffix.
+Following is an example of publishing SNAPSHOTS of a release that had an hbase version of 0.96.0 in its poms.
+
+[source,bourne]
+----
+
+ $ mvn clean install -DskipTests javadoc:aggregate site assembly:single -Prelease
+ $ mvn -DskipTests deploy -Papache-release
+----
+
+The _make_rc.sh_ script mentioned above (see <<maven.release,maven.release>>) can help you publish `SNAPSHOTS`.
+Make sure your `hbase.version` has a `-SNAPSHOT` suffix before running the script.
+It will put a snapshot up into the apache snapshot repository for you.
+
+[[hbase.rc.voting]]
+== Voting on Release Candidates
+
+Everyone is encouraged to try and vote on HBase release candidates.
+Only the votes of PMC members are binding.
+PMC members, please read this WIP doc on policy voting for a release candidate, link:https://github.com/rectang/asfrelease/blob/master/release.md[Release
+ Policy]. [quote]_Before casting +1 binding votes, individuals are required to
+ download the signed source code package onto their own hardware, compile it as
+ provided, and test the resulting executable on their own platform, along with also
+ validating cryptographic signatures and verifying that the package meets the
+ requirements of the ASF policy on releases._ Regards the latter, run +mvn apache-rat:check+ to verify all files are suitably licensed.
+See link:http://search-hadoop.com/m/DHED4dhFaU[HBase, mail # dev - On
+ recent discussion clarifying ASF release policy].
+for how we arrived at this process.
+
+[[documentation]]
+== Generating the HBase Reference Guide
+
+The manual is marked up using Asciidoc.
+We then use the link:http://asciidoctor.org/docs/asciidoctor-maven-plugin/[Asciidoctor maven plugin] to transform the markup to html.
+This plugin is run when you specify the +site+ goal as in when you run +mvn site+.
+See <<appendix_contributing_to_documentation,appendix contributing to documentation>> for more information on building the documentation.
+
+[[hbase.org]]
+== Updating link:http://hbase.apache.org[hbase.apache.org]
+
+[[hbase.org.site.contributing]]
+=== Contributing to hbase.apache.org
+
+See <<appendix_contributing_to_documentation,appendix contributing to documentation>> for more information on contributing to the documentation or website.
+
+[[hbase.org.site.publishing]]
+=== Publishing link:http://hbase.apache.org[hbase.apache.org]
+
+As of link:https://issues.apache.org/jira/browse/INFRA-5680[INFRA-5680 Migrate apache hbase website], to publish the website, build it using Maven, and then deploy it over a checkout of _https://svn.apache.org/repos/asf/hbase/hbase.apache.org/trunk_ and check in your changes.
+The script _dev-scripts/publish_hbase_website.sh_ is provided to automate this process and to be sure that stale files are removed from SVN.
+Review the script even if you decide to publish the website manually.
+Use the script as follows:
+
+----
+$ publish_hbase_website.sh -h
+Usage: publish_hbase_website.sh [-i | -a] [-g <dir>] [-s <dir>]
+ -h Show this message
+ -i Prompts the user for input
+ -a Does not prompt the user. Potentially dangerous.
+ -g The local location of the HBase git repository
+ -s The local location of the HBase svn checkout
+ Either --interactive or --silent is required.
+ Edit the script to set default Git and SVN directories.
+----
+
+NOTE: The SVN commit takes a long time.
+
+[[hbase.tests]]
+== Tests
+
+Developers, at a minimum, should familiarize themselves with the unit test detail; unit tests in HBase have a character not usually seen in other projects.
+
+This information is about unit tests for HBase itself.
+For developing unit tests for your HBase applications, see <<unit.tests,unit.tests>>.
+
+[[hbase.moduletests]]
+=== Apache HBase Modules
+
+As of 0.96, Apache HBase is split into multiple modules.
+This creates "interesting" rules for how and where tests are written.
+If you are writing code for `hbase-server`, see <<hbase.unittests,hbase.unittests>> for how to write your tests.
+These tests can spin up a minicluster and will need to be categorized.
+For any other module, for example `hbase-common`, the tests must be strict unit tests and just test the class under test - no use of the HBaseTestingUtility or minicluster is allowed (or even possible given the dependency tree).
+
+[[hbase.moduletest.shell]]
+==== Testing the HBase Shell
+
+The HBase shell and its tests are predominantly written in jruby.
+In order to make these tests run as a part of the standard build, there is a single JUnit test, `TestShell`, that takes care of loading the jruby implemented tests and running them.
+You can run all of these tests from the top level with:
+
+[source,bourne]
+----
+
+ mvn clean test -Dtest=TestShell
+----
+
+Alternatively, you may limit the shell tests that run using the system variable `shell.test`.
+This value should specify the ruby literal equivalent of a particular test case by name.
+For example, the tests that cover the shell commands for altering tables are contained in the test case `AdminAlterTableTest` and you can run them with:
+
+[source,bourne]
+----
+
+ mvn clean test -Dtest=TestShell -Dshell.test=/AdminAlterTableTest/
+----
+
+You may also use a link:http://docs.ruby-doc.com/docs/ProgrammingRuby/html/language.html#UJ[Ruby Regular Expression
+ literal] (in the `/pattern/` style) to select a set of test cases.
+You can run all of the HBase admin related tests, including both the normal administration and the security administration, with the command:
+
+[source,bourne]
+----
+
+ mvn clean test -Dtest=TestShell -Dshell.test=/.*Admin.*Test/
+----
+
+In the event of a test failure, you can see details by examining the XML version of the surefire report results
+
+[source,bourne]
+----
+
+ vim hbase-shell/target/surefire-reports/TEST-org.apache.hadoop.hbase.client.TestShell.xml
+----
+
+[[hbase.moduletest.run]]
+==== Running Tests in other Modules
+
+If the module you are developing in has no other dependencies on other HBase modules, then you can cd into that module and just run:
+
+[source,bourne]
+----
+mvn test
+----
+
+which will just run the tests IN THAT MODULE.
+If there are other dependencies on other modules, then you will have run the command from the ROOT HBASE DIRECTORY.
+This will run the tests in the other modules, unless you specify to skip the tests in that module.
+For instance, to skip the tests in the hbase-server module, you would run:
+
+[source,bourne]
+----
+mvn clean test -PskipServerTests
+----
+
+from the top level directory to run all the tests in modules other than hbase-server.
+Note that you can specify to skip tests in multiple modules as well as just for a single module.
+For example, to skip the tests in `hbase-server` and `hbase-common`, you would run:
+
+[source,bourne]
+----
+mvn clean test -PskipServerTests -PskipCommonTests
+----
+
+Also, keep in mind that if you are running tests in the `hbase-server` module you will need to apply the maven profiles discussed in <<hbase.unittests.cmds,hbase.unittests.cmds>> to get the tests to run properly.
+
+[[hbase.unittests]]
+=== Unit Tests
+
+Apache HBase unit tests are subdivided into four categories: small, medium, large, and integration with corresponding JUnit link:http://www.junit.org/node/581[categories]: `SmallTests`, `MediumTests`, `LargeTests`, `IntegrationTests`.
+JUnit categories are denoted using java annotations and look like this in your unit test code.
+
+[source,java]
+----
+...
+@Category(SmallTests.class)
+public class TestHRegionInfo {
+ @Test
+ public void testCreateHRegionInfoName() throws Exception {
+ // ...
+ }
+}
+----
+
+The above example shows how to mark a unit test as belonging to the `small` category.
+All unit tests in HBase have a categorization.
+
+The first three categories, `small`, `medium`, and `large`, are for tests run when you type `$ mvn test`.
+In other words, these three categorizations are for HBase unit tests.
+The `integration` category is not for unit tests, but for integration tests.
+These are run when you invoke `$ mvn verify`.
+Integration tests are described in <<integration.tests,integration.tests>>.
+
+HBase uses a patched maven surefire plugin and maven profiles to implement its unit test characterizations.
+
+Keep reading to figure which annotation of the set small, medium, and large to put on your new HBase unit test.
+
+.Categorizing Tests
+Small Tests (((SmallTests)))::
+ _Small_ tests are executed in a shared JVM.
+ We put in this category all the tests that can be executed quickly in a shared JVM.
+ The maximum execution time for a small test is 15 seconds, and small tests should not use a (mini)cluster.
+
+Medium Tests (((MediumTests)))::
+ _Medium_ tests represent tests that must be executed before proposing a patch.
+ They are designed to run in less than 30 minutes altogether, and are quite stable in their results.
+ They are designed to last less than 50 seconds individually.
+ They can use a cluster, and each of them is executed in a separate JVM.
+
+Large Tests (((LargeTests)))::
+ _Large_ tests are everything else.
+ They are typically large-scale tests, regression tests for specific bugs, timeout tests, performance tests.
+ They are executed before a commit on the pre-integration machines.
+ They can be run on the developer machine as well.
+
+Integration Tests (((IntegrationTests)))::
+ _Integration_ tests are system level tests.
+ See <<integration.tests,integration.tests>> for more info.
+
+[[hbase.unittests.cmds]]
+=== Running tests
+
+[[hbase.unittests.cmds.test]]
+==== Default: small and medium category tests
+
+Running `mvn test` will execute all small tests in a single JVM (no fork) and then medium tests in a separate JVM for each test instance.
+Medium tests are NOT executed if there is an error in a small test.
+Large tests are NOT executed.
+There is one report for small tests, and one report for medium tests if they are executed.
+
+[[hbase.unittests.cmds.test.runalltests]]
+==== Running all tests
+
+Running `mvn test -P runAllTests` will execute small tests in a single JVM then medium and large tests in a separate JVM for each test.
+Medium and large tests are NOT executed if there is an error in a small test.
+Large tests are NOT executed if there is an error in a small or medium test.
+There is one report for small tests, and one report for medium and large tests if they are executed.
+
+[[hbase.unittests.cmds.test.localtests.mytest]]
+==== Running a single test or all tests in a package
+
+To run an individual test, e.g. `MyTest`, rum `mvn test -Dtest=MyTest` You can also pass multiple, individual tests as a comma-delimited list:
+[source,bash]
+----
+mvn test -Dtest=MyTest1,MyTest2,MyTest3
+----
+You can also pass a package, which will run all tests under the package:
+[source,bash]
+----
+mvn test '-Dtest=org.apache.hadoop.hbase.client.*'
+----
+
+When `-Dtest` is specified, the `localTests` profile will be used.
+It will use the official release of maven surefire, rather than our custom surefire plugin, and the old connector (The HBase build uses a patched version of the maven surefire plugin). Each junit test is executed in a separate JVM (A fork per test class). There is no parallelization when tests are running in this mode.
+You will see a new message at the end of the -report: `"[INFO] Tests are skipped"`.
+It's harmless.
+However, you need to make sure the sum of `Tests run:` in the `Results:` section of test reports matching the number of tests you specified because no error will be reported when a non-existent test case is specified.
+
+[[hbase.unittests.cmds.test.profiles]]
+==== Other test invocation permutations
+
+Running `mvn test -P runSmallTests` will execute "small" tests only, using a single JVM.
+
+Running `mvn test -P runMediumTests` will execute "medium" tests only, launching a new JVM for each test-class.
+
+Running `mvn test -P runLargeTests` will execute "large" tests only, launching a new JVM for each test-class.
+
+For convenience, you can run `mvn test -P runDevTests` to execute both small and medium tests, using a single JVM.
+
+[[hbase.unittests.test.faster]]
+==== Running tests faster
+
+By default, `$ mvn test -P runAllTests` runs 5 tests in parallel.
+It can be increased on a developer's machine.
+Allowing that you can have 2 tests in parallel per core, and you need about 2GB of memory per test (at the extreme), if you have an 8 core, 24GB box, you can have 16 tests in parallel.
+but the memory available limits it to 12 (24/2), To run all tests with 12 tests in parallel, do this: +mvn test -P runAllTests
+ -Dsurefire.secondPartForkCount=12+.
+If using a version earlier than 2.0, do: +mvn test -P runAllTests -Dsurefire.secondPartThreadCount=12
+ +.
+To increase the speed, you can as well use a ramdisk.
+You will need 2GB of memory to run all tests.
+You will also need to delete the files between two test run.
+The typical way to configure a ramdisk on Linux is:
+
+----
+$ sudo mkdir /ram2G
+sudo mount -t tmpfs -o size=2048M tmpfs /ram2G
+----
+
+You can then use it to run all HBase tests on 2.0 with the command:
+
+----
+mvn test
+ -P runAllTests -Dsurefire.secondPartForkCount=12
+ -Dtest.build.data.basedirectory=/ram2G
+----
+
+On earlier versions, use:
+
+----
+mvn test
+ -P runAllTests -Dsurefire.secondPartThreadCount=12
+ -Dtest.build.data.basedirectory=/ram2G
+----
+
+[[hbase.unittests.cmds.test.hbasetests]]
+==== +hbasetests.sh+
+
+It's also possible to use the script +hbasetests.sh+.
+This script runs the medium and large tests in parallel with two maven instances, and provides a single report.
+This script does not use the hbase version of surefire so no parallelization is being done other than the two maven instances the script sets up.
+It must be executed from the directory which contains the _pom.xml_.
+
+For example running +./dev-support/hbasetests.sh+ will execute small and medium tests.
+Running +./dev-support/hbasetests.sh
+ runAllTests+ will execute all tests.
+Running +./dev-support/hbasetests.sh replayFailed+ will rerun the failed tests a second time, in a separate jvm and without parallelisation.
+
+[[hbase.unittests.resource.checker]]
+==== Test Resource Checker(((Test ResourceChecker)))
+
+A custom Maven SureFire plugin listener checks a number of resources before and after each HBase unit test runs and logs its findings at the end of the test output files which can be found in _target/surefire-reports_ per Maven module (Tests write test reports named for the test class into this directory.
+Check the _*-out.txt_ files). The resources counted are the number of threads, the number of file descriptors, etc.
+If the number has increased, it adds a _LEAK?_ comment in the logs.
+As you can have an HBase instance running in the background, some threads can be deleted/created without any specific action in the test.
+However, if the test does not work as expected, or if the test should not impact these resources, it's worth checking these log lines [computeroutput]+...hbase.ResourceChecker(157): before...+ and [computeroutput]+...hbase.ResourceChecker(157): after...+.
+For example:
+
+----
+2012-09-26 09:22:15,315 INFO [pool-1-thread-1]
+hbase.ResourceChecker(157): after:
+regionserver.TestColumnSeeking#testReseeking Thread=65 (was 65),
+OpenFileDescriptor=107 (was 107), MaxFileDescriptor=10240 (was 10240),
+ConnectionCount=1 (was 1)
+----
+
+[[hbase.tests.writing]]
+=== Writing Tests
+
+[[hbase.tests.rules]]
+==== General rules
+
+* As much as possible, tests should be written as category small tests.
+* All tests must be written to support parallel execution on the same machine, hence they should not use shared resources as fixed ports or fixed file names.
+* Tests should not overlog.
+ More than 100 lines/second makes the logs complex to read and use i/o that are hence not available for the other tests.
+* Tests can be written with `HBaseTestingUtility`.
+ This class offers helper functions to create a temp directory and do the cleanup, or to start a cluster.
+
+[[hbase.tests.categories]]
+==== Categories and execution time
+
+* All tests must be categorized, if not they could be skipped.
+* All tests should be written to be as fast as possible.
+* Small category tests should last less than 15 seconds, and must not have any side effect.
+* Medium category tests should last less than 50 seconds.
+* Large category tests should last less than 3 minutes.
+ This should ensure a good parallelization for people using it, and ease the analysis when the test fails.
+
+[[hbase.tests.sleeps]]
+==== Sleeps in tests
+
+Whenever possible, tests should not use [method]+Thread.sleep+, but rather waiting for the real event they need.
+This is faster and clearer for the reader.
+Tests should not do a [method]+Thread.sleep+ without testing an ending condition.
+This allows understanding what the test is waiting for.
+Moreover, the test will work whatever the machine performance is.
+Sleep should be minimal to be as fast as possible.
+Waiting for a variable should be done in a 40ms sleep loop.
+Waiting for a socket operation should be done in a 200 ms sleep loop.
+
+[[hbase.tests.cluster]]
+==== Tests using a cluster
+
+Tests using a HRegion do not have to start a cluster: A region can use the local file system.
+Start/stopping a cluster cost around 10 seconds.
+They should not be started per test method but per test class.
+Started cluster must be shutdown using [method]+HBaseTestingUtility#shutdownMiniCluster+, which cleans the directories.
+As most as possible, tests should use the default settings for the cluster.
+When they don't, they should document it.
+This will allow to share the cluster later.
+
+[[integration.tests]]
+=== Integration Tests
+
+HBase integration/system tests are tests that are beyond HBase unit tests.
+They are generally long-lasting, sizeable (the test can be asked to 1M rows or 1B rows), targetable (they can take configuration that will point them at the ready-made cluster they are to run against; integration tests do not include cluster start/stop code), and verifying success, integration tests rely on public APIs only; they do not attempt to examine server internals asserting success/fail.
+Integration tests are what you would run when you need to more elaborate proofing of a release candidate beyond what unit tests can do.
+They are not generally run on the Apache Continuous Integration build server, however, some sites opt to run integration tests as a part of their continuous testing on an actual cluster.
+
+Integration tests currently live under the _src/test_ directory in the hbase-it submodule and will match the regex: _**/IntegrationTest*.java_.
+All integration tests are also annotated with `@Category(IntegrationTests.class)`.
+
+Integration tests can be run in two modes: using a mini cluster, or against an actual distributed cluster.
+Maven failsafe is used to run the tests using the mini cluster.
+IntegrationTestsDriver class is used for executing the tests against a distributed cluster.
+Integration tests SHOULD NOT assume that they are running against a mini cluster, and SHOULD NOT use private API's to access cluster state.
+To interact with the distributed or mini cluster uniformly, `IntegrationTestingUtility`, and `HBaseCluster` classes, and public client API's can be used.
+
+On a distributed cluster, integration tests that use ChaosMonkey or otherwise manipulate services thru cluster manager (e.g.
+restart regionservers) use SSH to do it.
+To run these, test process should be able to run commands on remote end, so ssh should be configured accordingly (for example, if HBase runs under hbase user in your cluster, you can set up passwordless ssh for that user and run the test also under it). To facilitate that, `hbase.it.clustermanager.ssh.user`, `hbase.it.clustermanager.ssh.opts` and `hbase.it.clustermanager.ssh.cmd` configuration settings can be used.
+"User" is the remote user that cluster manager should use to perform ssh commands.
+"Opts" contains additional options that are passed to SSH (for example, "-i /tmp/my-key"). Finally, if you have some custom environment setup, "cmd" is the override format for the entire tunnel (ssh) command.
+The default string is {`/usr/bin/ssh %1$s %2$s%3$s%4$s "%5$s"`} and is a good starting point.
+This is a standard Java format string with 5 arguments that is used to execute the remote command.
+The argument 1 (%1$s) is SSH options set the via opts setting or via environment variable, 2 is SSH user name, 3 is "@" if username is set or "" otherwise, 4 is the target host name, and 5 is the logical command to execute (that may include single quotes, so don't use them). For example, if you run the tests under non-hbase user and want to ssh as that user and change to hbase on remote machine, you can use:
+[source,bash]
+----
+/usr/bin/ssh %1$s %2$s%3$s%4$s "su hbase - -c \"%5$s\""
+----
+That way, to kill RS (for example) integration tests may run:
+[source,bash]
+----
+{/usr/bin/ssh some-hostname "su hbase - -c \"ps aux | ... | kill ...\""}
+----
+The command is logged in the test logs, so you can verify it is correct for your environment.
+
+To disable the running of Integration Tests, pass the following profile on the command line `-PskipIntegrationTests`.
+For example,
+[source]
+----
+$ mvn clean install test -Dtest=TestZooKeeper -PskipIntegrationTests
+----
+
+[[maven.build.commands.integration.tests.mini]]
+==== Running integration tests against mini cluster
+
+HBase 0.92 added a `verify` maven target.
+Invoking it, for example by doing `mvn verify`, will run all the phases up to and including the verify phase via the maven link:http://maven.apache.org/plugins/maven-failsafe-plugin/[failsafe
+ plugin], running all the above mentioned HBase unit tests as well as tests that are in the HBase integration test group.
+After you have completed +mvn install -DskipTests+ You can run just the integration tests by invoking:
+
+[source,bourne]
+----
+
+cd hbase-it
+mvn verify
+----
+
+If you just want to run the integration tests in top-level, you need to run two commands.
+First: +mvn failsafe:integration-test+ This actually runs ALL the integration tests.
+
+NOTE: This command will always output `BUILD SUCCESS` even if there are test failures.
+
+At this point, you could grep the output by hand looking for failed tests.
+However, maven will do this for us; just use: +mvn
+ failsafe:verify+ The above command basically looks at all the test results (so don't remove the 'target' directory) for test failures and reports the results.
+
+[[maven.build.commands.integration.tests2]]
+===== Running a subset of Integration tests
+
+This is very similar to how you specify running a subset of unit tests (see above), but use the property `it.test` instead of `test`.
+To just run `IntegrationTestClassXYZ.java`, use: +mvn
+ failsafe:integration-test -Dit.test=IntegrationTestClassXYZ+ The next thing you might want to do is run groups of integration tests, say all integration tests that are named IntegrationTestClassX*.java: +mvn failsafe:integration-test -Dit.test=*ClassX*+ This runs everything that is an integration test that matches *ClassX*. This means anything matching: "**/IntegrationTest*ClassX*". You can also run multiple groups of integration tests using comma-delimited lists (similar to unit tests). Using a list of matches still supports full regex matching for each of the groups.This would look something like: +mvn
+ failsafe:integration-test -Dit.test=*ClassX*, *ClassY+
+
+[[maven.build.commands.integration.tests.distributed]]
+==== Running integration tests against distributed cluster
+
+If you have an already-setup HBase cluster, you can launch the integration tests by invoking the class `IntegrationTestsDriver`.
+You may have to run test-compile first.
+The configuration will be picked by the bin/hbase script.
+[source,bourne]
+----
+mvn test-compile
+----
+Then launch the tests with:
+
+[source,bourne]
+----
+bin/hbase [--config config_dir] org.apache.hadoop.hbase.IntegrationTestsDriver
+----
+
+Pass `-h` to get usage on this sweet tool.
+Running the IntegrationTestsDriver without any argument will launch tests found under `hbase-it/src/test`, having `@Category(IntegrationTests.class)` annotation, and a name starting with `IntegrationTests`.
+See the usage, by passing -h, to see how to filter test classes.
+You can pass a regex which is checked against the full class name; so, part of class name can be used.
+IntegrationTestsDriver uses Junit to run the tests.
+Currently there is no support for running integration tests against a distributed cluster using maven (see link:https://issues.apache.org/jira/browse/HBASE-6201[HBASE-6201]).
+
+The tests interact with the distributed cluster by using the methods in the `DistributedHBaseCluster` (implementing `HBaseCluster`) class, which in turn uses a pluggable `ClusterManager`.
+Concrete implementations provide actual functionality for carrying out deployment-specific and environment-dependent tasks (SSH, etc). The default `ClusterManager` is `HBaseClusterManager`, which uses SSH to remotely execute start/stop/kill/signal commands, and assumes some posix commands (ps, etc). Also assumes the user running the test has enough "power" to start/stop servers on the remote machines.
+By default, it picks up `HBASE_SSH_OPTS`, `HBASE_HOME`, `HBASE_CONF_DIR` from the env, and uses `bin/hbase-daemon.sh` to carry out the actions.
+Currently tarball deployments, deployments which uses _hbase-daemons.sh_, and link:http://incubator.apache.org/ambari/[Apache Ambari] deployments are supported.
+_/etc/init.d/_ scripts are not supported for now, but it can be easily added.
+For other deployment options, a ClusterManager can be implemented and plugged in.
+
+[[maven.build.commands.integration.tests.destructive]]
+==== Destructive integration / system tests
+
+In 0.96, a tool named `ChaosMonkey` has been introduced.
+It is modeled after the link:http://techblog.netflix.com/2012/07/chaos-monkey-released-into-wild.html[same-named tool by Netflix].
+Some of the tests use ChaosMonkey to simulate faults in the running cluster in the way of killing random servers, disconnecting servers, etc.
+ChaosMonkey can also be used as a stand-alone tool to run a (misbehaving) policy while you are running other tests.
+
+ChaosMonkey defines Action's and Policy's.
+Actions are sequences of events.
+We have at least the following actions:
+
+* Restart active master (sleep 5 sec)
+* Restart random regionserver (sleep 5 sec)
+* Restart random regionserver (sleep 60 sec)
+* Restart META regionserver (sleep 5 sec)
+* Restart ROOT regionserver (sleep 5 sec)
+* Batch restart of 50% of regionservers (sleep 5 sec)
+* Rolling restart of 100% of regionservers (sleep 5 sec)
+
+Policies on the other hand are responsible for executing the actions based on a strategy.
+The default policy is to execute a random action every minute based on predefined action weights.
+ChaosMonkey executes predefined named policies until it is stopped.
+More than one policy can be active at any time.
+
+To run ChaosMonkey as a standalone tool deploy your HBase cluster as usual.
+ChaosMonkey uses the configuration from the bin/hbase script, thus no extra configuration needs to be done.
+You can invoke the ChaosMonkey by running:
+
+[source,bourne]
+----
+bin/hbase org.apache.hadoop.hbase.util.ChaosMonkey
+----
+
+This will output smt like:
+
+----
+
+12/11/19 23:21:57 INFO util.ChaosMonkey: Using ChaosMonkey Policy: class org.apache.hadoop.hbase.util.ChaosMonkey$PeriodicRandomActionPolicy, period:60000
+12/11/19 23:21:57 INFO util.ChaosMonkey: Sleeping for 26953 to add jitter
+12/11/19 23:22:24 INFO util.ChaosMonkey: Performing action: Restart active master
+12/11/19 23:22:24 INFO util.ChaosMonkey: Killing master:master.example.com,60000,1353367210440
+12/11/19 23:22:24 INFO hbase.HBaseCluster: Aborting Master: master.example.com,60000,1353367210440
+12/11/19 23:22:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:master.example.com
+12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
+12/11/19 23:22:25 INFO hbase.HBaseCluster: Waiting service:master to stop: master.example.com,60000,1353367210440
+12/11/19 23:22:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:master.example.com
+12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
+12/11/19 23:22:25 INFO util.ChaosMonkey: Killed master server:master.example.com,60000,1353367210440
+12/11/19 23:22:25 INFO util.ChaosMonkey: Sleeping for:5000
+12/11/19 23:22:30 INFO util.ChaosMonkey: Starting master:master.example.com
+12/11/19 23:22:30 INFO hbase.HBaseCluster: Starting Master on: master.example.com
+12/11/19 23:22:30 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start master , hostname:master.example.com
+12/11/19 23:22:31 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting master, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-master-master.example.com.out
+....
+12/11/19 23:22:33 INFO util.ChaosMonkey: Started master: master.example.com,60000,1353367210440
+12/11/19 23:22:33 INFO util.ChaosMonkey: Sleeping for:51321
+12/11/19 23:23:24 INFO util.ChaosMonkey: Performing action: Restart random region server
+12/11/19 23:23:24 INFO util.ChaosMonkey: Killing region server:rs3.example.com,60020,1353367027826
+12/11/19 23:23:24 INFO hbase.HBaseCluster: Aborting RS: rs3.example.com,60020,1353367027826
+12/11/19 23:23:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:rs3.example.com
+12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
+12/11/19 23:23:25 INFO hbase.HBaseCluster: Waiting service:regionserver to stop: rs3.example.com,60020,1353367027826
+12/11/19 23:23:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:rs3.example.com
+12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
+12/11/19 23:23:25 INFO util.ChaosMonkey: Killed region server:rs3.example.com,60020,1353367027826. Reported num of rs:6
+12/11/19 23:23:25 INFO util.ChaosMonkey: Sleeping for:60000
+12/11/19 23:24:25 INFO util.ChaosMonkey: Starting region server:rs3.example.com
+12/11/19 23:24:25 INFO hbase.HBaseCluster: Starting RS on: rs3.example.com
+12/11/19 23:24:25 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start regionserver , hostname:rs3.example.com
+12/11/19 23:24:26 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting regionserver, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-regionserver-rs3.example.com.out
+
+12/11/19 23:24:27 INFO util.ChaosMonkey: Started region server:rs3.example.com,60020,1353367027826. Reported num of rs:6
+----
+
+As you can see from the log, ChaosMonkey started the default PeriodicRandomActionPolicy, which is configured with all the available actions, and ran RestartActiveMaster and RestartRandomRs actions.
+ChaosMonkey tool, if run from command line, will keep on running until the process is killed.
+
+[[chaos.monkey.properties]]
+==== Passing individual Chaos Monkey per-test Settings/Properties
+
+Since HBase version 1.0.0 (link:https://issues.apache.org/jira/browse/HBASE-11348[HBASE-11348]), the chaos monkeys is used to run integration tests can be configured per test run.
+Users can create a java properties file and and pass this to the chaos monkey with timing configurations.
+The properties file needs to be in the HBase classpath.
+The various properties that can be configured and their default values can be found listed in the `org.apache.hadoop.hbase.chaos.factories.MonkeyConstants` class.
+If any chaos monkey configuration is missing from the property file, then the default values are assumed.
+For example:
+
+[source,bourne]
+----
+
+$bin/hbase org.apache.hadoop.hbase.IntegrationTestIngest -m slowDeterministic -monkeyProps monkey.properties
+----
+
+The above command will start the integration tests and chaos monkey passing the properties file _monkey.properties_.
+Here is an example chaos monkey file:
+
+[source]
+----
+
+sdm.action1.period=120000
+sdm.action2.period=40000
+move.regions.sleep.time=80000
+move.regions.max.time=1000000
+move.regions.sleep.time=80000
+batch.restart.rs.ratio=0.4f
+----
+
+[[developing]]
+== Developer Guidelines
+
+=== Codelines
+
+Most development is done on the master branch, which is named `master` in the Git repository.
+Previously, HBase used Subversion, in which the master branch was called `TRUNK`.
+Branches exist for minor releases, and important features and bug fixes are often back-ported.
+
+=== Release Managers
+
+Each maintained release branch has a release manager, who volunteers to coordinate new features and bug fixes are backported to that release.
+The release managers are link:https://hbase.apache.org/team-list.html[committers].
+If you would like your feature or bug fix to be included in a given release, communicate with that release manager.
+If this list goes out of date or you can't reach the listed person, reach out to someone else on the list.
+
+NOTE: End-of-life releases are not included in this list.
+
+.Release Managers
+[cols="1,1", options="header"]
+|===
+| Release
+| Release Manager
+| 0.98
+| Andrew Purtell
+
+| 1.0
+| Enis Soztutar
+|===
+
+[[code.standards]]
+=== Code Standards
+
+See <<eclipse.code.formatting,eclipse.code.formatting>> and <<common.patch.feedback,common.patch.feedback>>.
+
+==== Interface Classifications
+
+Interfaces are classified both by audience and by stability level.
+These labels appear at the head of a class.
+The conventions followed by HBase are inherited by its parent project, Hadoop.
+
+The following interface classifications are commonly used:
+
+.InterfaceAudience
+`@InterfaceAudience.Public`::
+ APIs for users and HBase applications.
+ These APIs will be deprecated through major versions of HBase.
+
+`@InterfaceAudience.Private`::
+ APIs for HBase internals developers.
+ No guarantees on compatibility or availability in future versions.
+ Private interfaces do not need an `@InterfaceStability` classification.
+
+`@InterfaceAudience.LimitedPrivate(HBaseInterfaceAudience.COPROC)`::
+ APIs for HBase coprocessor writers.
+ As of HBase 0.92/0.94/0.96/0.98 this api is still unstable.
+ No guarantees on compatibility with future versions.
+
+No `@InterfaceAudience` Classification::
+ Packages without an `@InterfaceAudience` label are considered private.
+ Mark your new packages if publicly accessible.
+
+.Excluding Non-Public Interfaces from API Documentation
+[NOTE]
+====
+Only interfaces classified `@InterfaceAudience.Public` should be included in API documentation (Javadoc). Committers must add new package excludes `ExcludePackageNames` section of the _pom.xml_ for new packages which do not contain public classes.
+====
+
+.@InterfaceStability
+`@InterfaceStability` is important for packages marked `@InterfaceAudience.Public`.
+
+`@InterfaceStability.Stable`::
+ Public packages marked as stable cannot be changed without a deprecation path or a very good reason.
+
+`@InterfaceStability.Unstable`::
+ Public packages marked as unstable can be changed without a deprecation path.
+
+`@InterfaceStability.Evolving`::
+ Public packages marked as evolving may be changed, but it is discouraged.
+
+No `@InterfaceStability` Label::
+ Public classes with no `@InterfaceStability` label are discouraged, and should be considered implicitly unstable.
+
+If you are unclear about how to mark packages, ask on the development list.
+
+[[common.patch.feedback]]
+==== Code Formatting Conventions
+
+Please adhere to the following guidelines so that your patches can be reviewed more quickly.
+These guidelines have been developed based upon common feedback on patches from new contributors.
+
+See the link:http://www.oracle.com/technetwork/java/index-135089.html[Code
+ Conventions for the Java Programming Language] for more information on coding conventions in Java.
+
+[[common.patch.feedback.space.invaders]]
+===== Space Invaders
+
+Do not use extra spaces around brackets.
+Use the second style, rather than the first.
+
+[source,java]
+----
+
+if ( foo.equals( bar ) ) { // don't do this
+----
+
+[source,java]
+----
+
+if (foo.equals(bar)) {
+----
+
+[source,java]
+----
+
+foo = barArray[ i ]; // don't do this
+----
+
+[source,java]
+----
+
+foo = barArray[i];
+----
+
+[[common.patch.feedback.autogen]]
+===== Auto Generated Code
+
+Auto-generated code in Eclipse often uses bad variable names such as `arg0`.
+Use more informative variable names.
+Use code like the second example here.
+
+[source,java]
+----
+
+ public void readFields(DataInput arg0) throws IOException { // don't do this
+ foo = arg0.readUTF(); // don't do this
+----
+
+[source,java]
+----
+
+ public void readFields(DataInput di) throws IOException {
+ foo = di.readUTF();
+----
+
+[[common.patch.feedback.longlines]]
+===== Long Lines
+
+Keep lines less than 100 characters.
+You can configure your IDE to do this automatically.
+
+[source,java]
+----
+
+Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3, argument4, argument5, argument6, argument7, argument8, argument9); // don't do this
+----
+
+[source,java]
+----
+
+Bar bar = foo.veryLongMethodWithManyArguments(
+ argument1, argument2, argument3,argument4, argument5, argument6, argument7, argument8, argument9);
+----
+
+[[common.patch.feedback.trailingspaces]]
+===== Trailing Spaces
+
+Trailing spaces are a common problem.
+Be sure there is a line break after the end of your code, and avoid lines with nothing but whitespace.
+This makes diffs more meaningful.
+You can configure your IDE to help with this.
+
+[source,java]
+----
+
+Bar bar = foo.getBar(); <--- imagine there is an extra space(s) after the semicolon.
+----
+
+[[common.patch.feedback.javadoc]]
+===== API Documentation (Javadoc)
+
+This is also a very common feedback item.
+Don't forget Javadoc!
+
+Javadoc warnings are checked during precommit.
+If the precommit tool gives you a '-1', please fix the javadoc issue.
+Your patch won't be committed if it adds such warnings.
+
+[[common.patch.feedback.findbugs]]
+===== Findbugs
+
+`Findbugs` is used to detect common bugs pattern.
+It is checked during the precommit build by Apache's Jenkins.
+If errors are found, please fix them.
+You can run findbugs locally with +mvn
+ findbugs:findbugs+, which will generate the `findbugs` files locally.
+Sometimes, you may have to write code smarter than `findbugs`.
+You can annotate your code to tell `findbugs` you know what you're doing, by annotating your class with the following annotation:
+
+[source,java]
+----
+@edu.umd.cs.findbugs.annotations.SuppressWarnings(
+value="HE_EQUALS_USE_HASHCODE",
+justification="I know what I'm doing")
+----
+
+It is important to use the Apache-licensed version of the annotations.
+
+[[common.patch.feedback.javadoc.defaults]]
+===== Javadoc - Useless Defaults
+
+Don't just leave the @param arguments the way your IDE generated them.:
+
+[source,java]
+----
+
+ /**
+ *
+ * @param bar <---- don't do this!!!!
+ * @return <---- or this!!!!
+ */
+ public Foo getFoo(Bar bar);
+----
+
+Either add something descriptive to the @`param` and @`return` lines, or just remove them.
+The preference is to add something descriptive and useful.
+
+[[common.patch.feedback.onething]]
+===== One Thing At A Time, Folks
+
+If you submit a patch for one thing, don't do auto-reformatting or unrelated reformatting of code on a completely different area of code.
+
+Likewise, don't add unrelated cleanup or refactorings outside the scope of your Jira.
+
+[[common.patch.feedback.tests]]
+===== Ambigious Unit Tests
+
+Make sure that you're clear about what you are testing in your unit tests and why.
+
+[[common.patch.feedback.writable]]
+===== Implementing Writable
+
+.Applies pre-0.96 only
+[NOTE]
+====
+In 0.96, HBase moved to protocol buffers (protobufs). The below section on Writables applies to 0.94.x and previous, not to 0.96 and beyond.
+====
+
+Every class returned by RegionServers must implement the `Writable` interface.
+If you are creating a new class that needs to implement this interface, do not forget the default constructor.
+
+[[design.invariants]]
+=== Invariants
+
+We don't have many but what we have we list below.
+All are subject to challenge of course but until then, please hold to the rules of the road.
+
+[[design.invariants.zk.data]]
+==== No permanent state in ZooKeeper
+
+ZooKeeper state should transient (treat it like memory). If ZooKeeper state is deleted, hbase should be able to recover and essentially be in the same state.
+
+* .ExceptionsThere are currently a few exceptions that we need to fix around whether a table is enabled or disabled.
+* Replication data is currently stored only in ZooKeeper.
+ Deleting ZooKeeper data related to replication may cause replication to be disabled.
+ Do not delete the replication tree, _/hbase/replication/_.
++
+WARNING: Replication may be disrupted and data loss may occur if you delete the replication tree (_/hbase/replication/_) from ZooKeeper.
+Follow progress on this issue at link:https://issues.apache.org/jira/browse/HBASE-10295[HBASE-10295].
+
+
+[[run.insitu]]
+=== Running In-Situ
+
+If you are developing Apache HBase, frequently it is useful to test your changes against a more-real cluster than what you find in unit tests.
+In this case, HBase can be run directly from the source in local-mode.
+All you need to do is run:
+
+[source,bourne]
+----
+${HBASE_HOME}/bin/start-hbase.sh
+----
+
+This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your machine.
+
+Keep in mind that you will need to have installed HBase into your local maven repository for the in-situ cluster to work properly.
+That is, you will need to run:
+
+[source,bourne]
+----
+mvn clean install -DskipTests
+----
+
+to ensure that maven can find the correct classpath and dependencies.
+Generally, the above command is just a good thing to try running first, if maven is acting oddly.
+
+[[add.metrics]]
+=== Adding Metrics
+
+After adding a new feature a developer might want to add metrics.
+HBase exposes metrics using the Hadoop Metrics 2 system, so adding a new metric involves exposing that metric to the hadoop system.
+Unfortunately the API of metrics2 changed from hadoop 1 to hadoop 2.
+In order to get around this a set of interfaces and implementations have to be loaded at runtime.
+To get an in-depth look at the reasoning and structure of these classes you can read the blog post located link:https://blogs.apache.org/hbase/entry/migration_to_the_new_metrics[here].
+To add a metric to an existing MBean follow the short guide below:
+
+==== Add Metric name and Function to Hadoop Compat Interface.
+
+Inside of the source interface the corresponds to where the metrics are generated (eg MetricsMasterSource for things coming from HMaster) create new static strings for metric name and description.
+Then add a new method that will be called to add new reading.
+
+==== Add the Implementation to Both Hadoop 1 and Hadoop 2 Compat modules.
+
+Inside of the implementation of the source (eg.
+MetricsMasterSourceImpl in the above example) create a new histogram, counter, gauge, or stat in the init method.
+Then in the method that was added to the interface wire up the parameter passed in to the histogram.
+
+Now add tests that make sure the data is correctly exported to the metrics 2 system.
+For this the MetricsAssertHelper is provided.
+
+[[git.best.practices]]
+=== Git Best Practices
+
+Use the correct method to create patches.::
+ See <<submitting.patches,submitting.patches>>.
+Avoid git merges.::
+ Use `git pull --rebase` or `git fetch` followed by `git rebase`.
+Do not use `git push --force`.::
+ If the push does not work, fix the problem or ask for help.
+
+Please contribute to this document if you think of other Git best practices.
+
+==== `rebase_all_git_branches.sh`
+
+The _dev-support/rebase_all_git_branches.sh_ script is provided to help keep your Git repository clean.
+Use the `-h` parameter to get usage instructions.
+The script automatically refreshes your tracking branches, attempts an automatic rebase of each local branch against its remote branch, and gives you the option to delete any branch which represents a closed `HBASE-` JIRA.
+The script has one optional configuration option, the location of your Git directory.
+You can set a default by editing the script.
+Otherwise, you can pass the git directory manually by using the `-d` parameter, followed by an absolute or relative directory name, or even '.' for the current working directory.
+The script checks the directory for sub-directory called _.git/_, before proceeding.
+
+[[submitting.patches]]
+=== Submitting Patches
+
+HBase moved to GIT from SVN.
+Until we develop our own documentation for how to contribute patches in our new GIT context, caveat the fact that we have a different branching model and that we don't currently do the merge practice described in the following, the link:http://accumulo.apache.org/git.html[accumulo doc
+ on how to contribute and develop] after our move to GIT is worth a read.
+See also <<git.best.practices,git.best.practices>>.
+
+If you are new to submitting patches to open source or new to submitting patches to Apache, start by reading the link:http://commons.apache.org/patches.html[On Contributing
+ Patches] page from link:http://commons.apache.org/[Apache
+ Commons Project].
+It provides a nice overview that applies equally to the Apache HBase Project.
+
+[[submitting.patches.create]]
+==== Create Patch
+
+The script _dev-support/make_patch.sh_ has been provided to help you adhere to patch-creation guidelines.
+The script has the following syntax:
+
+----
+$ make_patch.sh [-a] [-p <patch_dir>]
+----
+
+. If you do not pass a `patch_dir`, the script defaults to _~/patches/_.
+ If the `patch_dir` does not exist, it is created.
+. By default, if an existing patch exists with the JIRA ID, the version of the new patch is incremented (_HBASE-XXXX-v3.patch_). If the `-a` option is passed, the version is not incremented, but the suffix `-addendum` is added (_HBASE-XXXX-v2-addendum.patch_). A second addendum to a given version is not supported.
+. Detects whether you have more than one local commit on your branch.
+ If you do, the script offers you the chance to run +git rebase
+ -i+ to squash the changes into a single commit so that it can use +git format-patch+.
+ If you decline, the script uses +git diff+ instead.
+ The patch is saved in a configurable directory and is ready to be attached to your JIRA.
+
+* .Patching WorkflowAlways patch against the master branch first, even if you want to patch in another branch.
+ HBase committers always apply patches first to the master branch, and backport if necessary.
+* Submit one single patch for a fix.
+ If necessary, squash local commits to merge local commits into a single one first.
+ See this link:http://stackoverflow.com/questions/5308816/how-to-use-git-merge-squash[Stack Overflow question] for more information about squashing commits.
+* The patch should have the JIRA ID in the name.
+ If you are generating from a branch, include the target branch in the filename.
+ A common naming scheme for patches is:
++
+----
+HBASE-XXXX.patch
+----
++
+----
+HBASE-XXXX-0.90.patch # to denote that the patch is against branch 0.90
+----
++
+----
+HBASE-XXXX-v3.patch # to denote that this is the third version of the patch
+----
+
+* To submit a patch, first create it using one of the methods in <<patching.methods,patching.methods>>.
+ Next, attach the patch to the JIRA (one patch for the whole fix), using the dialog.
+ Next, click the btn:[Patch
+ Available] button, which triggers the Hudson job which checks the patch for validity.
++
+Please understand that not every patch may get committed, and that feedback will likely be provided on the patch.
+
+* If your patch is longer than a single screen, also attach a Review Board to the case.
+ See <<reviewboard,reviewboard>>.
+* If you need to revise your patch, leave the previous patch file(s) attached to the JIRA, and upload the new one, following the naming conventions in <<submitting.patches.create,submitting.patches.create>>.
+ Cancel the Patch Available flag and then re-trigger it, by toggling the btn:[Patch Available] button in JIRA.
+ JIRA sorts attached files by the time they were attached, and has no problem with multiple attachments with the same name.
+ However, at times it is easier to refer to different version of a patch if you add `-vX`, where the [replaceable]_X_ is the version (starting with 2).
+* If you need to submit your patch against multiple branches, rather than just master, name each version of the patch with the branch it is for, following the naming conventions in <<submitting.patches.create,submitting.patches.create>>.
+
+.Methods to Create PatchesEclipse::
+ Select the menu item.
+
+Git::
+ `git format-patch` is preferred because it preserves commit messages.
+ Use `git rebase -i` first, to combine (squash) smaller commits into a single larger one.
+
+Subversion::
+
+Make sure you review <<eclipse.code.formatting,eclipse.code.formatting>> and <<common.patch.feedback,common.patch.feedback>> for code style.
+If your patch was generated incorrectly or your code does not adhere to the code formatting guidelines, you may be asked to redo some work.
+
+[[submitting.patches.tests]]
+==== Unit Tests
+
+Yes, please.
+Please try to include unit tests with every code patch (and especially new classes and large changes). Make sure unit tests pass locally before submitting the patch.
+
+Also, see <<mockito,mockito>>.
+
+If you are creating a new unit test class, notice how other unit test classes have classification/sizing annotations at the top and a static method on the end.
+Be sure to include these in any new unit test files you generate.
+See <<hbase.tests,hbase.tests>> for more on how the annotations work.
+
+==== Integration Tests
+
+Significant new features should provide an integration test in addition to unit tests, suitable for exercising the new feature at different points in its configuration space.
+
+==== ReviewBoard
+
+Patches larger than one screen, or patches that will be tricky to review, should go through link:http://reviews.apache.org[ReviewBoard].
+
+.Procedure: Use ReviewBoard
+. Register for an account if you don't already have one.
+ It does not use the credentials from link:http://issues.apache.org[issues.apache.org].
+ Log in.
+. Click [label]#New Review Request#.
+. Choose the `hbase-git` repository.
+ Click Choose File to select the diff and optionally a parent diff.
+ Click btn:[Create
+ Review Request].
+. Fill in the fields as required.
+ At the minimum, fill in the [label]#Summary# and choose `hbase` as the [label]#Review Group#.
+ If you fill in the [label]#Bugs# field, the review board links back to the relevant JIRA.
+ The more fields you fill in, the better.
+ Click btn:[Publish] to make your review request public.
+ An email will be sent to everyone in the `hbase` group, to review the patch.
+. Back in your JIRA, click , and paste in the URL of your ReviewBoard request.
+ This attaches the ReviewBoard to the JIRA, for easy access.
+. To cancel the request, click .
+
+For more information on how to use ReviewBoard, see link:http://www.reviewboard.org/docs/manual/1.5/[the ReviewBoard
+ documentation].
+
+==== Guide for HBase Committers
+
+===== New committers
+
+New committers are encouraged to first read Apache's generic committer documentation:
+
+* link:http://www.apache.org/dev/new-committers-guide.html[Apache New Committer Guide]
+* link:http://www.apache.org/dev/committers.html[Apache Committer FAQ]
+
+===== Review
+
+HBase committers should, as often as possible, attempt to review patches submitted by others.
+Ideally every submitted patch will get reviewed by a committer _within a few days_.
+If a committer reviews a patch they have not authored, and believe it to be of sufficient quality, then they can commit the patch, otherwise the patch should be cancelled with a clear explanation for why it was rejected.
+
+The list of submitted patches is in the link:https://issues.apache.org/jira/secure/IssueNavigator.jspa?mode=hide&requestId=12312392[HBase Review Queue], which is ordered by time of last modification.
+Committers should scan the list from top to bottom, looking for patches that they feel qualified to review and possibly commit.
+
+For non-trivial changes, it is required to get another committer to review your own patches before commit.
+Use the btn:[Submit Patch] button in JIRA, just like other contributors, and then wait for a `+1` response from another committer before committing.
+
+===== Reject
+
+Patches which do not adhere to the guidelines in link:https://wiki.apache.org/hadoop/Hbase/HowToCommit/hadoop/Hbase/HowToContribute#[HowToContribute] and to the link:https://wiki.apache.org/hadoop/Hbase/HowToCommit/hadoop/CodeReviewChecklist#[code review checklist] should be rejected.
+Committers should always be polite to contributors and try to instruct and encourage them to contribute better patches.
+If a committer wishes to improve an unacceptable patch, then it should first be rejected, and a new patch should be attached by the committer for review.
+
+[[committing.patches]]
+===== Commit
+
+Committers commit patches to the Apache HBase GIT repository.
+
+.Before you commit!!!!
+[NOTE]
+====
+Make sure your local configuration is correct, especially your identity and email.
+Examine the output of the +$ git config
+ --list+ command and be sure it is correct.
+See this GitHub article, link:https://help.github.com/articles/set-up-git[Set Up Git] if you need pointers.
+====
+
+When you commit a patch, please:
+
+. Include the Jira issue id in the commit message, along with a short description of the change and the name of the contributor if it is not you.
+ Be sure to get the issue ID right, as this causes Jira to link to the change in Git (use the issue's "All" tab to see these).
+. Commit the patch to a new branch based off master or other intended branch.
+ It's a good idea to call this branch by the JIRA ID.
+ Then check out the relevant target branch where you want to commit, make sure your local branch has all remote changes, by doing a +git pull --rebase+ or another similar command, cherry-pick the change into each relevant branch (such as master), and do +git push <remote-server>
+ <remote-branch>+.
++
+WARNING: If you do not have all remote changes, the push will fail.
+If the push fails for any reason, fix the problem or ask for help.
+Do not do a +git push --force+.
++
+Before you can commit a patch, you need to determine how the patch was created.
+The instructions and preferences around the way to create patches have changed, and there will be a transition periond.
++
+* .Determine How a Patch Was CreatedIf the first few lines of the patch look like the headers of an email, with a From, Date, and Subject, it was created using +git format-patch+.
+ This is the preference, because you can reuse the submitter's commit message.
+ If the commit message is not appropriate, you can still use the commit, then run the command +git
+ rebase -i origin/master+, and squash and reword as appropriate.
+* If the first line of the patch looks similar to the following, it was created using +git diff+ without `--no-prefix`.
+ This is acceptable too.
+ Notice the `a` and `b` in front of the file names.
+ This is the indication that the patch was not created with `--no-prefix`.
++
+----
+diff --git a/src/main/docbkx/developer.xml b/src/main/docbkx/developer.xml
+----
+
+* If the first line of th
<TRUNCATED>
[17/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
Posted by nd...@apache.org.
http://git-wip-us.apache.org/repos/asf/hbase/blob/33fe79cf/src/main/asciidoc/_chapters/architecture.adoc
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diff --git a/src/main/asciidoc/_chapters/architecture.adoc b/src/main/asciidoc/_chapters/architecture.adoc
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@@ -0,0 +1,2512 @@
+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+= Architecture
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+:toc: left
+:source-language: java
+
+[[arch.overview]]
+== Overview
+
+[[arch.overview.nosql]]
+=== NoSQL?
+
+HBase is a type of "NoSQL" database.
+"NoSQL" is a general term meaning that the database isn't an RDBMS which supports SQL as its primary access language, but there are many types of NoSQL databases: BerkeleyDB is an example of a local NoSQL database, whereas HBase is very much a distributed database.
+Technically speaking, HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS, such as typed columns, secondary indexes, triggers, and advanced query languages, etc.
+
+However, HBase has many features which supports both linear and modular scaling.
+HBase clusters expand by adding RegionServers that are hosted on commodity class servers.
+If a cluster expands from 10 to 20 RegionServers, for example, it doubles both in terms of storage and as well as processing capacity.
+RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best performance requires specialized hardware and storage devices.
+HBase features of note are:
+
+* Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore.
+ This makes it very suitable for tasks such as high-speed counter aggregation.
+* Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are automatically split and re-distributed as your data grows.
+* Automatic RegionServer failover
+* Hadoop/HDFS Integration: HBase supports HDFS out of the box as its distributed file system.
+* MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both source and sink.
+* Java Client API: HBase supports an easy to use Java API for programmatic access.
+* Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.
+* Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.
+* Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.
+
+[[arch.overview.when]]
+=== When Should I Use HBase?
+
+HBase isn't suitable for every problem.
+
+First, make sure you have enough data.
+If you have hundreds of millions or billions of rows, then HBase is a good candidate.
+If you only have a few thousand/million rows, then using a traditional RDBMS might be a better choice due to the fact that all of your data might wind up on a single node (or two) and the rest of the cluster may be sitting idle.
+
+Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns, secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be "ported" to HBase by simply changing a JDBC driver, for example.
+Consider moving from an RDBMS to HBase as a complete redesign as opposed to a port.
+
+Third, make sure you have enough hardware.
+Even HDFS doesn't do well with anything less than 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.
+
+HBase can run quite well stand-alone on a laptop - but this should be considered a development configuration only.
+
+[[arch.overview.hbasehdfs]]
+=== What Is The Difference Between HBase and Hadoop/HDFS?
+
+link:http://hadoop.apache.org/hdfs/[HDFS] is a distributed file system that is well suited for the storage of large files.
+Its documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files.
+HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables.
+This can sometimes be a point of conceptual confusion.
+HBase internally puts your data in indexed "StoreFiles" that exist on HDFS for high-speed lookups.
+See the <<datamodel>> and the rest of this chapter for more information on how HBase achieves its goals.
+
+[[arch.catalog]]
+== Catalog Tables
+
+The catalog table `hbase:meta` exists as an HBase table and is filtered out of the HBase shell's `list` command, but is in fact a table just like any other.
+
+[[arch.catalog.root]]
+=== -ROOT-
+
+NOTE: The `-ROOT-` table was removed in HBase 0.96.0.
+Information here should be considered historical.
+
+The `-ROOT-` table kept track of the location of the `.META` table (the previous name for the table now called `hbase:meta`) prior to HBase 0.96.
+The `-ROOT-` table structure was as follows:
+
+.Key
+
+* .META.
+ region key (`.META.,,1`)
+
+.Values
+
+* `info:regioninfo` (serialized link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html[HRegionInfo] instance of `hbase:meta`)
+* `info:server` (server:port of the RegionServer holding `hbase:meta`)
+* `info:serverstartcode` (start-time of the RegionServer process holding `hbase:meta`)
+
+[[arch.catalog.meta]]
+=== hbase:meta
+
+The `hbase:meta` table (previously called `.META.`) keeps a list of all regions in the system.
+The location of `hbase:meta` was previously tracked within the `-ROOT-` table, but is now stored in ZooKeeper.
+
+The `hbase:meta` table structure is as follows:
+
+.Key
+
+* Region key of the format (`[table],[region start key],[region id]`)
+
+.Values
+
+* `info:regioninfo` (serialized link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html[HRegionInfo] instance for this region)
+* `info:server` (server:port of the RegionServer containing this region)
+* `info:serverstartcode` (start-time of the RegionServer process containing this region)
+
+When a table is in the process of splitting, two other columns will be created, called `info:splitA` and `info:splitB`.
+These columns represent the two daughter regions.
+The values for these columns are also serialized HRegionInfo instances.
+After the region has been split, eventually this row will be deleted.
+
+.Note on HRegionInfo
+[NOTE]
+====
+The empty key is used to denote table start and table end.
+A region with an empty start key is the first region in a table.
+If a region has both an empty start and an empty end key, it is the only region in the table
+====
+
+In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/Writables.html#getHRegionInfo%28byte[]%29[Writables] utility.
+
+[[arch.catalog.startup]]
+=== Startup Sequencing
+
+First, the location of `hbase:meta` is looked up in ZooKeeper.
+Next, `hbase:meta` is updated with server and startcode values.
+
+For information on region-RegionServer assignment, see <<regions.arch.assignment>>.
+
+[[architecture.client]]
+== Client
+
+The HBase client finds the RegionServers that are serving the particular row range of interest.
+It does this by querying the `hbase:meta` table.
+See <<arch.catalog.meta>> for details.
+After locating the required region(s), the client contacts the RegionServer serving that region, rather than going through the master, and issues the read or write request.
+This information is cached in the client so that subsequent requests need not go through the lookup process.
+Should a region be reassigned either by the master load balancer or because a RegionServer has died, the client will requery the catalog tables to determine the new location of the user region.
+
+See <<master.runtime>> for more information about the impact of the Master on HBase Client communication.
+
+Administrative functions are done via an instance of link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html[Admin]
+
+[[client.connections]]
+=== Cluster Connections
+
+The API changed in HBase 1.0. For connection configuration information, see <<client_dependencies>>.
+
+==== API as of HBase 1.0.0
+
+Its been cleaned up and users are returned Interfaces to work against rather than particular types.
+In HBase 1.0, obtain a `Connection` object from `ConnectionFactory` and thereafter, get from it instances of `Table`, `Admin`, and `RegionLocator` on an as-need basis.
+When done, close the obtained instances.
+Finally, be sure to cleanup your `Connection` instance before exiting.
+`Connections` are heavyweight objects but thread-safe so you can create one for your application and keep the instance around.
+`Table`, `Admin` and `RegionLocator` instances are lightweight.
+Create as you go and then let go as soon as you are done by closing them.
+See the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/package-summary.html[Client Package Javadoc Description] for example usage of the new HBase 1.0 API.
+
+==== API before HBase 1.0.0
+
+Instances of `HTable` are the way to interact with an HBase cluster earlier than 1.0.0. _link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table] instances are not thread-safe_. Only one thread can use an instance of Table at any given time.
+When creating Table instances, it is advisable to use the same link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HBaseConfiguration[HBaseConfiguration] instance.
+This will ensure sharing of ZooKeeper and socket instances to the RegionServers which is usually what you want.
+For example, this is preferred:
+
+[source,java]
+----
+HBaseConfiguration conf = HBaseConfiguration.create();
+HTable table1 = new HTable(conf, "myTable");
+HTable table2 = new HTable(conf, "myTable");
+----
+
+as opposed to this:
+
+[source,java]
+----
+HBaseConfiguration conf1 = HBaseConfiguration.create();
+HTable table1 = new HTable(conf1, "myTable");
+HBaseConfiguration conf2 = HBaseConfiguration.create();
+HTable table2 = new HTable(conf2, "myTable");
+----
+
+For more information about how connections are handled in the HBase client, see link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/ConnectionFactory.html[ConnectionFactory].
+
+[[client.connection.pooling]]
+===== Connection Pooling
+
+For applications which require high-end multithreaded access (e.g., web-servers or application servers that may serve many application threads in a single JVM), you can pre-create a `Connection`, as shown in the following example:
+
+.Pre-Creating a `Connection`
+====
+[source,java]
+----
+// Create a connection to the cluster.
+Configuration conf = HBaseConfiguration.create();
+try (Connection connection = ConnectionFactory.createConnection(conf)) {
+ try (Table table = connection.getTable(TableName.valueOf(tablename)) {
+ // use table as needed, the table returned is lightweight
+ }
+}
+----
+====
+
+Constructing HTableInterface implementation is very lightweight and resources are controlled.
+
+.`HTablePool` is Deprecated
+[WARNING]
+====
+Previous versions of this guide discussed `HTablePool`, which was deprecated in HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by link:https://issues.apache.org/jira/browse/HBASE-6580[HBASE-6500], or `HConnection`, which is deprecated in HBase 1.0 by `Connection`.
+Please use link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Connection.html[Connection] instead.
+====
+
+[[client.writebuffer]]
+=== WriteBuffer and Batch Methods
+
+In HBase 1.0 and later, link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTable.html[HTable] is deprecated in favor of link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html[Table]. `Table` does not use autoflush. To do buffered writes, use the BufferedMutator class.
+
+Before a `Table` or `HTable` instance is discarded, invoke either `close()` or `flushCommits()`, so `Put`s will not be lost.
+
+For additional information on write durability, review the link:../acid-semantics.html[ACID semantics] page.
+
+For fine-grained control of batching of ``Put``s or ``Delete``s, see the link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#batch%28java.util.List%29[batch] methods on Table.
+
+[[client.external]]
+=== External Clients
+
+Information on non-Java clients and custom protocols is covered in <<external_apis>>
+
+[[client.filter]]
+== Client Request Filters
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html[Get] and link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html[Scan] instances can be optionally configured with link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/Filter.html[filters] which are applied on the RegionServer.
+
+Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups of Filter functionality.
+
+[[client.filter.structural]]
+=== Structural
+
+Structural Filters contain other Filters.
+
+[[client.filter.structural.fl]]
+==== FilterList
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FilterList.html[FilterList] represents a list of Filters with a relationship of `FilterList.Operator.MUST_PASS_ALL` or `FilterList.Operator.MUST_PASS_ONE` between the Filters.
+The following example shows an 'or' between two Filters (checking for either 'my value' or 'my other value' on the same attribute).
+
+[source,java]
+----
+FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
+SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my value")
+ );
+list.add(filter1);
+SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my other value")
+ );
+list.add(filter2);
+scan.setFilter(list);
+----
+
+[[client.filter.cv]]
+=== Column Value
+
+[[client.filter.cv.scvf]]
+==== SingleColumnValueFilter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SingleColumnValueFilter.html[SingleColumnValueFilter] can be used to test column values for equivalence (`CompareOp.EQUAL`), inequality (`CompareOp.NOT_EQUAL`), or ranges (e.g., `CompareOp.GREATER`). The following is example of testing equivalence a column to a String value "my value"...
+
+[source,java]
+----
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my value")
+ );
+scan.setFilter(filter);
+----
+
+[[client.filter.cvp]]
+=== Column Value Comparators
+
+There are several Comparator classes in the Filter package that deserve special mention.
+These Comparators are used in concert with other Filters, such as <<client.filter.cv.scvf>>.
+
+[[client.filter.cvp.rcs]]
+==== RegexStringComparator
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RegexStringComparator.html[RegexStringComparator] supports regular expressions for value comparisons.
+
+[source,java]
+----
+RegexStringComparator comp = new RegexStringComparator("my."); // any value that starts with 'my'
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ comp
+ );
+scan.setFilter(filter);
+----
+
+See the Oracle JavaDoc for link:http://download.oracle.com/javase/6/docs/api/java/util/regex/Pattern.html[supported RegEx patterns in Java].
+
+[[client.filter.cvp.substringcomparator]]
+==== SubstringComparator
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SubstringComparator.html[SubstringComparator] can be used to determine if a given substring exists in a value.
+The comparison is case-insensitive.
+
+[source,java]
+----
+
+SubstringComparator comp = new SubstringComparator("y val"); // looking for 'my value'
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ comp
+ );
+scan.setFilter(filter);
+----
+
+[[client.filter.cvp.bfp]]
+==== BinaryPrefixComparator
+
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryPrefixComparator.html[BinaryPrefixComparator].
+
+[[client.filter.cvp.bc]]
+==== BinaryComparator
+
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/BinaryComparator.html[BinaryComparator].
+
+[[client.filter.kvm]]
+=== KeyValue Metadata
+
+As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate the existence of keys (i.e., ColumnFamily:Column qualifiers) for a row, as opposed to values the previous section.
+
+[[client.filter.kvm.ff]]
+==== FamilyFilter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FamilyFilter.html[FamilyFilter] can be used to filter on the ColumnFamily.
+It is generally a better idea to select ColumnFamilies in the Scan than to do it with a Filter.
+
+[[client.filter.kvm.qf]]
+==== QualifierFilter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/QualifierFilter.html[QualifierFilter] can be used to filter based on Column (aka Qualifier) name.
+
+[[client.filter.kvm.cpf]]
+==== ColumnPrefixFilter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnPrefixFilter.html[ColumnPrefixFilter] can be used to filter based on the lead portion of Column (aka Qualifier) names.
+
+A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family.
+It can be used to efficiently get a subset of the columns in very wide rows.
+
+Note: The same column qualifier can be used in different column families.
+This filter returns all matching columns.
+
+Example: Find all columns in a row and family that start with "abc"
+
+[source,java]
+----
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[] prefix = Bytes.toBytes("abc");
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new ColumnPrefixFilter(prefix);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+----
+
+[[client.filter.kvm.mcpf]]
+==== MultipleColumnPrefixFilter
+
+link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/MultipleColumnPrefixFilter.html[MultipleColumnPrefixFilter] behaves like ColumnPrefixFilter but allows specifying multiple prefixes.
+
+Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes.
+It can be used to efficiently get discontinuous sets of columns from very wide rows.
+
+Example: Find all columns in a row and family that start with "abc" or "xyz"
+
+[source,java]
+----
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new MultipleColumnPrefixFilter(prefixes);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+----
+
+[[client.filter.kvm.crf]]
+==== ColumnRangeFilter
+
+A link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ColumnRangeFilter.html[ColumnRangeFilter] allows efficient intra row scanning.
+
+A ColumnRangeFilter can seek ahead to the first matching column for each involved column family.
+It can be used to efficiently get a 'slice' of the columns of a very wide row.
+i.e.
+you have a million columns in a row but you only want to look at columns bbbb-bbdd.
+
+Note: The same column qualifier can be used in different column families.
+This filter returns all matching columns.
+
+Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)
+
+[source,java]
+----
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[] startColumn = Bytes.toBytes("bbbb");
+byte[] endColumn = Bytes.toBytes("bbdd");
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+----
+
+Note: Introduced in HBase 0.92
+
+[[client.filter.row]]
+=== RowKey
+
+[[client.filter.row.rf]]
+==== RowFilter
+
+It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/RowFilter.html[RowFilter] can also be used.
+
+[[client.filter.utility]]
+=== Utility
+
+[[client.filter.utility.fkof]]
+==== FirstKeyOnlyFilter
+
+This is primarily used for rowcount jobs.
+See link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/FirstKeyOnlyFilter.html[FirstKeyOnlyFilter].
+
+== Master
+
+`HMaster` is the implementation of the Master Server.
+The Master server is responsible for monitoring all RegionServer instances in the cluster, and is the interface for all metadata changes.
+In a distributed cluster, the Master typically runs on the <<arch.hdfs.nn>>.
+J Mohamed Zahoor goes into some more detail on the Master Architecture in this blog posting, link:http://blog.zahoor.in/2012/08/hbase-hmaster-architecture/[HBase HMaster Architecture ].
+
+[[master.startup]]
+=== Startup Behavior
+
+If run in a multi-Master environment, all Masters compete to run the cluster.
+If the active Master loses its lease in ZooKeeper (or the Master shuts down), then the remaining Masters jostle to take over the Master role.
+
+[[master.runtime]]
+=== Runtime Impact
+
+A common dist-list question involves what happens to an HBase cluster when the Master goes down.
+Because the HBase client talks directly to the RegionServers, the cluster can still function in a "steady state". Additionally, per <<arch.catalog>>, `hbase:meta` exists as an HBase table and is not resident in the Master.
+However, the Master controls critical functions such as RegionServer failover and completing region splits.
+So while the cluster can still run for a short time without the Master, the Master should be restarted as soon as possible.
+
+[[master.api]]
+=== Interface
+
+The methods exposed by `HMasterInterface` are primarily metadata-oriented methods:
+
+* Table (createTable, modifyTable, removeTable, enable, disable)
+* ColumnFamily (addColumn, modifyColumn, removeColumn)
+* Region (move, assign, unassign) For example, when the `Admin` method `disableTable` is invoked, it is serviced by the Master server.
+
+[[master.processes]]
+=== Processes
+
+The Master runs several background threads:
+
+[[master.processes.loadbalancer]]
+==== LoadBalancer
+
+Periodically, and when there are no regions in transition, a load balancer will run and move regions around to balance the cluster's load.
+See <<balancer_config>> for configuring this property.
+
+See <<regions.arch.assignment>> for more information on region assignment.
+
+[[master.processes.catalog]]
+==== CatalogJanitor
+
+Periodically checks and cleans up the `hbase:meta` table.
+See <arch.catalog.meta>> for more information on the meta table.
+
+[[regionserver.arch]]
+== RegionServer
+
+`HRegionServer` is the RegionServer implementation.
+It is responsible for serving and managing regions.
+In a distributed cluster, a RegionServer runs on a <<arch.hdfs.dn>>.
+
+[[regionserver.arch.api]]
+=== Interface
+
+The methods exposed by `HRegionRegionInterface` contain both data-oriented and region-maintenance methods:
+
+* Data (get, put, delete, next, etc.)
+* Region (splitRegion, compactRegion, etc.) For example, when the `Admin` method `majorCompact` is invoked on a table, the client is actually iterating through all regions for the specified table and requesting a major compaction directly to each region.
+
+[[regionserver.arch.processes]]
+=== Processes
+
+The RegionServer runs a variety of background threads:
+
+[[regionserver.arch.processes.compactsplit]]
+==== CompactSplitThread
+
+Checks for splits and handle minor compactions.
+
+[[regionserver.arch.processes.majorcompact]]
+==== MajorCompactionChecker
+
+Checks for major compactions.
+
+[[regionserver.arch.processes.memstore]]
+==== MemStoreFlusher
+
+Periodically flushes in-memory writes in the MemStore to StoreFiles.
+
+[[regionserver.arch.processes.log]]
+==== LogRoller
+
+Periodically checks the RegionServer's WAL.
+
+=== Coprocessors
+
+Coprocessors were added in 0.92.
+There is a thorough link:https://blogs.apache.org/hbase/entry/coprocessor_introduction[Blog Overview of CoProcessors] posted.
+Documentation will eventually move to this reference guide, but the blog is the most current information available at this time.
+
+[[block.cache]]
+=== Block Cache
+
+HBase provides two different BlockCache implementations: the default on-heap `LruBlockCache` and the `BucketCache`, which is (usually) off-heap.
+This section discusses benefits and drawbacks of each implementation, how to choose the appropriate option, and configuration options for each.
+
+.Block Cache Reporting: UI
+[NOTE]
+====
+See the RegionServer UI for detail on caching deploy.
+Since HBase 0.98.4, the Block Cache detail has been significantly extended showing configurations, sizings, current usage, time-in-the-cache, and even detail on block counts and types.
+====
+
+==== Cache Choices
+
+`LruBlockCache` is the original implementation, and is entirely within the Java heap. `BucketCache` is mainly intended for keeping block cache data off-heap, although `BucketCache` can also keep data on-heap and serve from a file-backed cache.
+
+.BucketCache is production ready as of HBase 0.98.6
+[NOTE]
+====
+To run with BucketCache, you need HBASE-11678.
+This was included in 0.98.6.
+====
+
+Fetching will always be slower when fetching from BucketCache, as compared to the native on-heap LruBlockCache.
+However, latencies tend to be less erratic across time, because there is less garbage collection when you use BucketCache since it is managing BlockCache allocations, not the GC.
+If the BucketCache is deployed in off-heap mode, this memory is not managed by the GC at all.
+This is why you'd use BucketCache, so your latencies are less erratic and to mitigate GCs and heap fragmentation.
+See Nick Dimiduk's link:http://www.n10k.com/blog/blockcache-101/[BlockCache 101] for comparisons running on-heap vs off-heap tests.
+Also see link:http://people.apache.org/~stack/bc/[Comparing BlockCache Deploys] which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC), use BucketCache.
+
+When you enable BucketCache, you are enabling a two tier caching system, an L1 cache which is implemented by an instance of LruBlockCache and an off-heap L2 cache which is implemented by BucketCache.
+Management of these two tiers and the policy that dictates how blocks move between them is done by `CombinedBlockCache`.
+It keeps all DATA blocks in the L2 BucketCache and meta blocks -- INDEX and BLOOM blocks -- on-heap in the L1 `LruBlockCache`.
+See <<offheap.blockcache>> for more detail on going off-heap.
+
+[[cache.configurations]]
+==== General Cache Configurations
+
+Apart from the cache implementation itself, you can set some general configuration options to control how the cache performs.
+See http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html.
+After setting any of these options, restart or rolling restart your cluster for the configuration to take effect.
+Check logs for errors or unexpected behavior.
+
+See also <<blockcache.prefetch>>, which discusses a new option introduced in link:https://issues.apache.org/jira/browse/HBASE-9857[HBASE-9857].
+
+[[block.cache.design]]
+==== LruBlockCache Design
+
+The LruBlockCache is an LRU cache that contains three levels of block priority to allow for scan-resistance and in-memory ColumnFamilies:
+
+* Single access priority: The first time a block is loaded from HDFS it normally has this priority and it will be part of the first group to be considered during evictions.
+ The advantage is that scanned blocks are more likely to get evicted than blocks that are getting more usage.
+* Multi access priority: If a block in the previous priority group is accessed again, it upgrades to this priority.
+ It is thus part of the second group considered during evictions.
+* In-memory access priority: If the block's family was configured to be "in-memory", it will be part of this priority disregarding the number of times it was accessed.
+ Catalog tables are configured like this.
+ This group is the last one considered during evictions.
++
+To mark a column family as in-memory, call
+
+[source,java]
+----
+HColumnDescriptor.setInMemory(true);
+----
+
+if creating a table from java, or set `IN_MEMORY => true` when creating or altering a table in the shell: e.g.
+
+[source]
+----
+hbase(main):003:0> create 't', {NAME => 'f', IN_MEMORY => 'true'}
+----
+
+For more information, see the link:http://hbase.apache.org/xref/org/apache/hadoop/hbase/io/hfile/LruBlockCache.html[LruBlockCache source]
+
+[[block.cache.usage]]
+==== LruBlockCache Usage
+
+Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache.
+This might be good for a large number of use cases, but further tunings are usually required in order to achieve better performance.
+An important concept is the link:http://en.wikipedia.org/wiki/Working_set_size[working set size], or WSS, which is: "the amount of memory needed to compute the answer to a problem". For a website, this would be the data that's needed to answer the queries over a short amount of time.
+
+The way to calculate how much memory is available in HBase for caching is:
+
+[source]
+----
+number of region servers * heap size * hfile.block.cache.size * 0.99
+----
+
+The default value for the block cache is 0.25 which represents 25% of the available heap.
+The last value (99%) is the default acceptable loading factor in the LRU cache after which eviction is started.
+The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would make the process blocking from the point where it loads new blocks.
+Here are some examples:
+
+* One region server with the heap size set to 1 GB and the default block cache size will have 253 MB of block cache available.
+* 20 region servers with the heap size set to 8 GB and a default block cache size will have 39.6 of block cache.
+* 100 region servers with the heap size set to 24 GB and a block cache size of 0.5 will have about 1.16 TB of block cache.
+
+Your data is not the only resident of the block cache.
+Here are others that you may have to take into account:
+
+Catalog Tables::
+ The `-ROOT-` (prior to HBase 0.96, see <<arch.catalog.root,arch.catalog.root>>) and `hbase:meta` tables are forced into the block cache and have the in-memory priority which means that they are harder to evict.
+ The former never uses more than a few hundreds bytes while the latter can occupy a few MBs (depending on the number of regions).
+
+HFiles Indexes::
+ An _HFile_ is the file format that HBase uses to store data in HDFS.
+ It contains a multi-layered index which allows HBase to seek to the data without having to read the whole file.
+ The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing.
+ For big data sets it's not unusual to see numbers around 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren't used.
+
+Keys::
+ The values that are stored are only half the picture, since each value is stored along with its keys (row key, family qualifier, and timestamp). See <<keysize>>.
+
+Bloom Filters::
+ Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.
+
+Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the region server web UI and checkout the relevant metrics.
+For keys, sampling can be done by using the HFile command line tool and look for the average key size metric.
+Since HBase 0.98.3, you can view details on BlockCache stats and metrics in a special Block Cache section in the UI.
+
+It's generally bad to use block caching when the WSS doesn't fit in memory.
+This is the case when you have for example 40GB available across all your region servers' block caches but you need to process 1TB of data.
+One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily.
+Here are two use cases:
+
+* Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close to 0.
+ Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM.
+ For more information on monitoring GC, see <<trouble.log.gc>>.
+* Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there's no need to put them into the block cache.
+ The Scan object has the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access.
+ An example would be counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that's currently in use.
+
+[[data.blocks.in.fscache]]
+===== Caching META blocks only (DATA blocks in fscache)
+
+An interesting setup is one where we cache META blocks only and we read DATA blocks in on each access.
+If the DATA blocks fit inside fscache, this alternative may make sense when access is completely random across a very large dataset.
+To enable this setup, alter your table and for each column family set `BLOCKCACHE => 'false'`.
+You are 'disabling' the BlockCache for this column family only. You can never disable the caching of META blocks.
+Since link:https://issues.apache.org/jira/browse/HBASE-4683[HBASE-4683 Always cache index and bloom blocks], we will cache META blocks even if the BlockCache is disabled.
+
+[[offheap.blockcache]]
+==== Off-heap Block Cache
+
+[[enable.bucketcache]]
+===== How to Enable BucketCache
+
+The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an L1 on-heap cache implemented by LruBlockCache and a second L2 cache implemented with BucketCache.
+The managing class is link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CombinedBlockCache.html[CombinedBlockCache] by default.
+The previous link describes the caching 'policy' implemented by CombinedBlockCache.
+In short, it works by keeping meta blocks -- INDEX and BLOOM in the L1, on-heap LruBlockCache tier -- and DATA blocks are kept in the L2, BucketCache tier.
+It is possible to amend this behavior in HBase since version 1.0 and ask that a column family have both its meta and DATA blocks hosted on-heap in the L1 tier by setting `cacheDataInL1` via `(HColumnDescriptor.setCacheDataInL1(true)` or in the shell, creating or amending column families setting `CACHE_DATA_IN_L1` to true: e.g.
+[source]
+----
+hbase(main):003:0> create 't', {NAME => 't', CONFIGURATION => {CACHE_DATA_IN_L1 => 'true'}}
+----
+
+The BucketCache Block Cache can be deployed on-heap, off-heap, or file based.
+You set which via the `hbase.bucketcache.ioengine` setting.
+Setting it to `heap` will have BucketCache deployed inside the allocated Java heap.
+Setting it to `offheap` will have BucketCache make its allocations off-heap, and an ioengine setting of `file:PATH_TO_FILE` will direct BucketCache to use a file caching (Useful in particular if you have some fast I/O attached to the box such as SSDs).
+
+It is possible to deploy an L1+L2 setup where we bypass the CombinedBlockCache policy and have BucketCache working as a strict L2 cache to the L1 LruBlockCache.
+For such a setup, set `CacheConfig.BUCKET_CACHE_COMBINED_KEY` to `false`.
+In this mode, on eviction from L1, blocks go to L2.
+When a block is cached, it is cached first in L1.
+When we go to look for a cached block, we look first in L1 and if none found, then search L2.
+Let us call this deploy format, _Raw L1+L2_.
+
+Other BucketCache configs include: specifying a location to persist cache to across restarts, how many threads to use writing the cache, etc.
+See the link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html[CacheConfig.html] class for configuration options and descriptions.
+
+
+
+====== BucketCache Example Configuration
+This sample provides a configuration for a 4 GB off-heap BucketCache with a 1 GB on-heap cache.
+
+Configuration is performed on the RegionServer.
+
+Setting `hbase.bucketcache.ioengine` and `hbase.bucketcache.size` > 0 enables `CombinedBlockCache`.
+Let us presume that the RegionServer has been set to run with a 5G heap: i.e. `HBASE_HEAPSIZE=5g`.
+
+
+. First, edit the RegionServer's _hbase-env.sh_ and set `HBASE_OFFHEAPSIZE` to a value greater than the off-heap size wanted, in this case, 4 GB (expressed as 4G). Let's set it to 5G.
+ That'll be 4G for our off-heap cache and 1G for any other uses of off-heap memory (there are other users of off-heap memory other than BlockCache; e.g.
+ DFSClient in RegionServer can make use of off-heap memory). See <<direct.memory>>.
++
+[source]
+----
+HBASE_OFFHEAPSIZE=5G
+----
+
+. Next, add the following configuration to the RegionServer's _hbase-site.xml_.
++
+[source,xml]
+----
+<property>
+ <name>hbase.bucketcache.ioengine</name>
+ <value>offheap</value>
+</property>
+<property>
+ <name>hfile.block.cache.size</name>
+ <value>0.2</value>
+</property>
+<property>
+ <name>hbase.bucketcache.size</name>
+ <value>4196</value>
+</property>
+----
+
+. Restart or rolling restart your cluster, and check the logs for any issues.
+
+
+In the above, we set the BucketCache to be 4G.
+We configured the on-heap LruBlockCache have 20% (0.2) of the RegionServer's heap size (0.2 * 5G = 1G). In other words, you configure the L1 LruBlockCache as you would normally (as if there were no L2 cache present).
+
+link:https://issues.apache.org/jira/browse/HBASE-10641[HBASE-10641] introduced the ability to configure multiple sizes for the buckets of the BucketCache, in HBase 0.98 and newer.
+To configurable multiple bucket sizes, configure the new property `hfile.block.cache.sizes` (instead of `hfile.block.cache.size`) to a comma-separated list of block sizes, ordered from smallest to largest, with no spaces.
+The goal is to optimize the bucket sizes based on your data access patterns.
+The following example configures buckets of size 4096 and 8192.
+
+[source,xml]
+----
+<property>
+ <name>hfile.block.cache.sizes</name>
+ <value>4096,8192</value>
+</property>
+----
+
+[[direct.memory]]
+.Direct Memory Usage In HBase
+[NOTE]
+====
+The default maximum direct memory varies by JVM.
+Traditionally it is 64M or some relation to allocated heap size (-Xmx) or no limit at all (JDK7 apparently). HBase servers use direct memory, in particular short-circuit reading, the hosted DFSClient will allocate direct memory buffers.
+If you do off-heap block caching, you'll be making use of direct memory.
+Starting your JVM, make sure the `-XX:MaxDirectMemorySize` setting in _conf/hbase-env.sh_ is set to some value that is higher than what you have allocated to your off-heap BlockCache (`hbase.bucketcache.size`). It should be larger than your off-heap block cache and then some for DFSClient usage (How much the DFSClient uses is not easy to quantify; it is the number of open HFiles * `hbase.dfs.client.read.shortcircuit.buffer.size` where `hbase.dfs.client.read.shortcircuit.buffer.size` is set to 128k in HBase -- see _hbase-default.xml_ default configurations). Direct memory, which is part of the Java process heap, is separate from the object heap allocated by -Xmx.
+The value allocated by `MaxDirectMemorySize` must not exceed physical RAM, and is likely to be less than the total available RAM due to other memory requirements and system constraints.
+
+You can see how much memory -- on-heap and off-heap/direct -- a RegionServer is configured to use and how much it is using at any one time by looking at the _Server Metrics: Memory_ tab in the UI.
+It can also be gotten via JMX.
+In particular the direct memory currently used by the server can be found on the `java.nio.type=BufferPool,name=direct` bean.
+Terracotta has a link:http://terracotta.org/documentation/4.0/bigmemorygo/configuration/storage-options[good write up] on using off-heap memory in Java.
+It is for their product BigMemory but a lot of the issues noted apply in general to any attempt at going off-heap. Check it out.
+====
+
+.hbase.bucketcache.percentage.in.combinedcache
+[NOTE]
+====
+This is a pre-HBase 1.0 configuration removed because it was confusing.
+It was a float that you would set to some value between 0.0 and 1.0.
+Its default was 0.9.
+If the deploy was using CombinedBlockCache, then the LruBlockCache L1 size was calculated to be `(1 - hbase.bucketcache.percentage.in.combinedcache) * size-of-bucketcache` and the BucketCache size was `hbase.bucketcache.percentage.in.combinedcache * size-of-bucket-cache`.
+where size-of-bucket-cache itself is EITHER the value of the configuration `hbase.bucketcache.size` IF it was specified as Megabytes OR `hbase.bucketcache.size` * `-XX:MaxDirectMemorySize` if `hbase.bucketcache.size` is between 0 and 1.0.
+
+In 1.0, it should be more straight-forward.
+L1 LruBlockCache size is set as a fraction of java heap using `hfile.block.cache.size setting` (not the best name) and L2 is set as above either in absolute Megabytes or as a fraction of allocated maximum direct memory.
+====
+
+==== Compressed BlockCache
+
+link:https://issues.apache.org/jira/browse/HBASE-11331[HBASE-11331] introduced lazy BlockCache decompression, more simply referred to as compressed BlockCache.
+When compressed BlockCache is enabled data and encoded data blocks are cached in the BlockCache in their on-disk format, rather than being decompressed and decrypted before caching.
+
+For a RegionServer hosting more data than can fit into cache, enabling this feature with SNAPPY compression has been shown to result in 50% increase in throughput and 30% improvement in mean latency while, increasing garbage collection by 80% and increasing overall CPU load by 2%. See HBASE-11331 for more details about how performance was measured and achieved.
+For a RegionServer hosting data that can comfortably fit into cache, or if your workload is sensitive to extra CPU or garbage-collection load, you may receive less benefit.
+
+The compressed BlockCache is disabled by default. To enable it, set `hbase.block.data.cachecompressed` to `true` in _hbase-site.xml_ on all RegionServers.
+
+[[regionserver_splitting_implementation]]
+=== RegionServer Splitting Implementation
+
+As write requests are handled by the region server, they accumulate in an in-memory storage system called the _memstore_. Once the memstore fills, its content are written to disk as additional store files. This event is called a _memstore flush_. As store files accumulate, the RegionServer will <<compaction,compact>> them into fewer, larger files. After each flush or compaction finishes, the amount of data stored in the region has changed. The RegionServer consults the region split policy to determine if the region has grown too large or should be split for another policy-specific reason. A region split request is enqueued if the policy recommends it.
+
+Logically, the process of splitting a region is simple. We find a suitable point in the keyspace of the region where we should divide the region in half, then split the region's data into two new regions at that point. The details of the process however are not simple. When a split happens, the newly created _daughter regions_ do not rewrite all the data into new files immediately. Instead, they create small files similar to symbolic link files, named link:http://www.google.com/url?q=http%3A%2F%2Fhbase.apache.org%2Fapidocs%2Forg%2Fapache%2Fhadoop%2Fhbase%2Fio%2FReference.html&sa=D&sntz=1&usg=AFQjCNEkCbADZ3CgKHTtGYI8bJVwp663CA[Reference files], which point to either the top or bottom part of the parent store file according to the split point. The reference file is used just like a regular data file, but only half of the records are considered. The region can only be split if there are no more references to the immutable data files of the parent region. Those reference files are clea
ned gradually by compactions, so that the region will stop referring to its parents files, and can be split further.
+
+Although splitting the region is a local decision made by the RegionServer, the split process itself must coordinate with many actors. The RegionServer notifies the Master before and after the split, updates the `.META.` table so that clients can discover the new daughter regions, and rearranges the directory structure and data files in HDFS. Splitting is a multi-task process. To enable rollback in case of an error, the RegionServer keeps an in-memory journal about the execution state. The steps taken by the RegionServer to execute the split are illustrated in <<regionserver_split_process_image>>. Each step is labeled with its step number. Actions from RegionServers or Master are shown in red, while actions from the clients are show in green.
+
+[[regionserver_split_process_image]]
+.RegionServer Split Process
+image::region_split_process.png[Region Split Process]
+
+. The RegionServer decides locally to split the region, and prepares the split. *THE SPLIT TRANSACTION IS STARTED.* As a first step, the RegionServer acquires a shared read lock on the table to prevent schema modifications during the splitting process. Then it creates a znode in zookeeper under `/hbase/region-in-transition/region-name`, and sets the znode's state to `SPLITTING`.
+. The Master learns about this znode, since it has a watcher for the parent `region-in-transition` znode.
+. The RegionServer creates a sub-directory named `.splits` under the parent’s `region` directory in HDFS.
+. The RegionServer closes the parent region and marks the region as offline in its local data structures. *THE SPLITTING REGION IS NOW OFFLINE.* At this point, client requests coming to the parent region will throw `NotServingRegionException`. The client will retry with some backoff. The closing region is flushed.
+. The RegionServer creates region directories under the `.splits` directory, for daughter regions A and B, and creates necessary data structures. Then it splits the store files, in the sense that it creates two link:http://www.google.com/url?q=http%3A%2F%2Fhbase.apache.org%2Fapidocs%2Forg%2Fapache%2Fhadoop%2Fhbase%2Fio%2FReference.html&sa=D&sntz=1&usg=AFQjCNEkCbADZ3CgKHTtGYI8bJVwp663CA[Reference] files per store file in the parent region. Those reference files will point to the parent regions'files.
+. The RegionServer creates the actual region directory in HDFS, and moves the reference files for each daughter.
+. The RegionServer sends a `Put` request to the `.META.` table, to set the parent as offline in the `.META.` table and add information about daughter regions. At this point, there won’t be individual entries in `.META.` for the daughters. Clients will see that the parent region is split if they scan `.META.`, but won’t know about the daughters until they appear in `.META.`. Also, if this `Put` to `.META`. succeeds, the parent will be effectively split. If the RegionServer fails before this RPC succeeds, Master and the next Region Server opening the region will clean dirty state about the region split. After the `.META.` update, though, the region split will be rolled-forward by Master.
+. The RegionServer opens daughters A and B in parallel.
+. The RegionServer adds the daughters A and B to `.META.`, together with information that it hosts the regions. *THE SPLIT REGIONS (DAUGHTERS WITH REFERENCES TO PARENT) ARE NOW ONLINE.* After this point, clients can discover the new regions and issue requests to them. Clients cache the `.META.` entries locally, but when they make requests to the RegionServer or `.META.`, their caches will be invalidated, and they will learn about the new regions from `.META.`.
+. The RegionServer updates znode `/hbase/region-in-transition/region-name` in ZooKeeper to state `SPLIT`, so that the master can learn about it. The balancer can freely re-assign the daughter regions to other region servers if necessary. *THE SPLIT TRANSACTION IS NOW FINISHED.*
+. After the split, `.META.` and HDFS will still contain references to the parent region. Those references will be removed when compactions in daughter regions rewrite the data files. Garbage collection tasks in the master periodically check whether the daughter regions still refer to the parent region's files. If not, the parent region will be removed.
+
+[[wal]]
+=== Write Ahead Log (WAL)
+
+[[purpose.wal]]
+==== Purpose
+
+The _Write Ahead Log (WAL)_ records all changes to data in HBase, to file-based storage.
+Under normal operations, the WAL is not needed because data changes move from the MemStore to StoreFiles.
+However, if a RegionServer crashes or becomes unavailable before the MemStore is flushed, the WAL ensures that the changes to the data can be replayed.
+If writing to the WAL fails, the entire operation to modify the data fails.
+
+HBase uses an implementation of the link:http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/wal/WAL.html[WAL] interface.
+Usually, there is only one instance of a WAL per RegionServer.
+The RegionServer records Puts and Deletes to it, before recording them to the <<store.memstore>> for the affected <<store>>.
+
+.The HLog
+[NOTE]
+====
+Prior to 2.0, the interface for WALs in HBase was named `HLog`.
+In 0.94, HLog was the name of the implementation of the WAL.
+You will likely find references to the HLog in documentation tailored to these older versions.
+====
+
+The WAL resides in HDFS in the _/hbase/WALs/_ directory (prior to HBase 0.94, they were stored in _/hbase/.logs/_), with subdirectories per region.
+
+For more general information about the concept of write ahead logs, see the Wikipedia link:http://en.wikipedia.org/wiki/Write-ahead_logging[Write-Ahead Log] article.
+
+==== MultiWAL
+With a single WAL per RegionServer, the RegionServer must write to the WAL serially, because HDFS files must be sequential. This causes the WAL to be a performance bottleneck.
+
+HBase 1.0 introduces support MultiWal in link:https://issues.apache.org/jira/browse/HBASE-5699[HBASE-5699]. MultiWAL allows a RegionServer to write multiple WAL streams in parallel, by using multiple pipelines in the underlying HDFS instance, which increases total throughput during writes. This parallelization is done by partitioning incoming edits by their Region. Thus, the current implementation will not help with increasing the throughput to a single Region.
+
+RegionServers using the original WAL implementation and those using the MultiWAL implementation can each handle recovery of either set of WALs, so a zero-downtime configuration update is possible through a rolling restart.
+
+.Configure MultiWAL
+To configure MultiWAL for a RegionServer, set the value of the property `hbase.wal.provider` to `multiwal` by pasting in the following XML:
+
+[source,xml]
+----
+<property>
+ <name>hbase.wal.provider</name>
+ <value>multiwal</value>
+</property>
+----
+
+Restart the RegionServer for the changes to take effect.
+
+To disable MultiWAL for a RegionServer, unset the property and restart the RegionServer.
+
+
+[[wal_flush]]
+==== WAL Flushing
+
+TODO (describe).
+
+==== WAL Splitting
+
+A RegionServer serves many regions.
+All of the regions in a region server share the same active WAL file.
+Each edit in the WAL file includes information about which region it belongs to.
+When a region is opened, the edits in the WAL file which belong to that region need to be replayed.
+Therefore, edits in the WAL file must be grouped by region so that particular sets can be replayed to regenerate the data in a particular region.
+The process of grouping the WAL edits by region is called _log splitting_.
+It is a critical process for recovering data if a region server fails.
+
+Log splitting is done by the HMaster during cluster start-up or by the ServerShutdownHandler as a region server shuts down.
+So that consistency is guaranteed, affected regions are unavailable until data is restored.
+All WAL edits need to be recovered and replayed before a given region can become available again.
+As a result, regions affected by log splitting are unavailable until the process completes.
+
+.Procedure: Log Splitting, Step by Step
+. The _/hbase/WALs/<host>,<port>,<startcode>_ directory is renamed.
++
+Renaming the directory is important because a RegionServer may still be up and accepting requests even if the HMaster thinks it is down.
+If the RegionServer does not respond immediately and does not heartbeat its ZooKeeper session, the HMaster may interpret this as a RegionServer failure.
+Renaming the logs directory ensures that existing, valid WAL files which are still in use by an active but busy RegionServer are not written to by accident.
++
+The new directory is named according to the following pattern:
++
+----
+/hbase/WALs/<host>,<port>,<startcode>-splitting
+----
++
+An example of such a renamed directory might look like the following:
++
+----
+/hbase/WALs/srv.example.com,60020,1254173957298-splitting
+----
+
+. Each log file is split, one at a time.
++
+The log splitter reads the log file one edit entry at a time and puts each edit entry into the buffer corresponding to the edit's region.
+At the same time, the splitter starts several writer threads.
+Writer threads pick up a corresponding buffer and write the edit entries in the buffer to a temporary recovered edit file.
+The temporary edit file is stored to disk with the following naming pattern:
++
+----
+/hbase/<table_name>/<region_id>/recovered.edits/.temp
+----
++
+This file is used to store all the edits in the WAL log for this region.
+After log splitting completes, the _.temp_ file is renamed to the sequence ID of the first log written to the file.
++
+To determine whether all edits have been written, the sequence ID is compared to the sequence of the last edit that was written to the HFile.
+If the sequence of the last edit is greater than or equal to the sequence ID included in the file name, it is clear that all writes from the edit file have been completed.
+
+. After log splitting is complete, each affected region is assigned to a RegionServer.
++
+When the region is opened, the _recovered.edits_ folder is checked for recovered edits files.
+If any such files are present, they are replayed by reading the edits and saving them to the MemStore.
+After all edit files are replayed, the contents of the MemStore are written to disk (HFile) and the edit files are deleted.
+
+
+===== Handling of Errors During Log Splitting
+
+If you set the `hbase.hlog.split.skip.errors` option to `true`, errors are treated as follows:
+
+* Any error encountered during splitting will be logged.
+* The problematic WAL log will be moved into the _.corrupt_ directory under the hbase `rootdir`,
+* Processing of the WAL will continue
+
+If the `hbase.hlog.split.skip.errors` option is set to `false`, the default, the exception will be propagated and the split will be logged as failed.
+See link:https://issues.apache.org/jira/browse/HBASE-2958[HBASE-2958 When hbase.hlog.split.skip.errors is set to false, we fail the split but thats it].
+We need to do more than just fail split if this flag is set.
+
+====== How EOFExceptions are treated when splitting a crashed RegionServer's WALs
+
+If an EOFException occurs while splitting logs, the split proceeds even when `hbase.hlog.split.skip.errors` is set to `false`.
+An EOFException while reading the last log in the set of files to split is likely, because the RegionServer was likely in the process of writing a record at the time of a crash.
+For background, see link:https://issues.apache.org/jira/browse/HBASE-2643[HBASE-2643 Figure how to deal with eof splitting logs]
+
+===== Performance Improvements during Log Splitting
+
+WAL log splitting and recovery can be resource intensive and take a long time, depending on the number of RegionServers involved in the crash and the size of the regions. <<distributed.log.splitting>> and <<distributed.log.replay>> were developed to improve performance during log splitting.
+
+[[distributed.log.splitting]]
+====== Distributed Log Splitting
+
+_Distributed Log Splitting_ was added in HBase version 0.92 (link:https://issues.apache.org/jira/browse/HBASE-1364[HBASE-1364]) by Prakash Khemani from Facebook.
+It reduces the time to complete log splitting dramatically, improving the availability of regions and tables.
+For example, recovering a crashed cluster took around 9 hours with single-threaded log splitting, but only about six minutes with distributed log splitting.
+
+The information in this section is sourced from Jimmy Xiang's blog post at http://blog.cloudera.com/blog/2012/07/hbase-log-splitting/.
+
+.Enabling or Disabling Distributed Log Splitting
+
+Distributed log processing is enabled by default since HBase 0.92.
+The setting is controlled by the `hbase.master.distributed.log.splitting` property, which can be set to `true` or `false`, but defaults to `true`.
+
+[[log.splitting.step.by.step]]
+.Distributed Log Splitting, Step by Step
+
+After configuring distributed log splitting, the HMaster controls the process.
+The HMaster enrolls each RegionServer in the log splitting process, and the actual work of splitting the logs is done by the RegionServers.
+The general process for log splitting, as described in <<log.splitting.step.by.step>> still applies here.
+
+. If distributed log processing is enabled, the HMaster creates a _split log manager_ instance when the cluster is started.
+ .. The split log manager manages all log files which need to be scanned and split.
+ .. The split log manager places all the logs into the ZooKeeper splitlog node (_/hbase/splitlog_) as tasks.
+ .. You can view the contents of the splitlog by issuing the following `zkCli` command. Example output is shown.
++
+[source,bash]
+----
+ls /hbase/splitlog
+[hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost8.sample.com%2C57020%2C1340474893275-splitting%2Fhost8.sample.com%253A57020.1340474893900,
+hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost3.sample.com%2C57020%2C1340474893299-splitting%2Fhost3.sample.com%253A57020.1340474893931,
+hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost4.sample.com%2C57020%2C1340474893287-splitting%2Fhost4.sample.com%253A57020.1340474893946]
+----
++
+The output contains some non-ASCII characters.
+When decoded, it looks much more simple:
++
+----
+[hdfs://host2.sample.com:56020/hbase/.logs
+/host8.sample.com,57020,1340474893275-splitting
+/host8.sample.com%3A57020.1340474893900,
+hdfs://host2.sample.com:56020/hbase/.logs
+/host3.sample.com,57020,1340474893299-splitting
+/host3.sample.com%3A57020.1340474893931,
+hdfs://host2.sample.com:56020/hbase/.logs
+/host4.sample.com,57020,1340474893287-splitting
+/host4.sample.com%3A57020.1340474893946]
+----
++
+The listing represents WAL file names to be scanned and split, which is a list of log splitting tasks.
+
+. The split log manager monitors the log-splitting tasks and workers.
++
+The split log manager is responsible for the following ongoing tasks:
++
+* Once the split log manager publishes all the tasks to the splitlog znode, it monitors these task nodes and waits for them to be processed.
+* Checks to see if there are any dead split log workers queued up.
+ If it finds tasks claimed by unresponsive workers, it will resubmit those tasks.
+ If the resubmit fails due to some ZooKeeper exception, the dead worker is queued up again for retry.
+* Checks to see if there are any unassigned tasks.
+ If it finds any, it create an ephemeral rescan node so that each split log worker is notified to re-scan unassigned tasks via the `nodeChildrenChanged` ZooKeeper event.
+* Checks for tasks which are assigned but expired.
+ If any are found, they are moved back to `TASK_UNASSIGNED` state again so that they can be retried.
+ It is possible that these tasks are assigned to slow workers, or they may already be finished.
+ This is not a problem, because log splitting tasks have the property of idempotence.
+ In other words, the same log splitting task can be processed many times without causing any problem.
+* The split log manager watches the HBase split log znodes constantly.
+ If any split log task node data is changed, the split log manager retrieves the node data.
+ The node data contains the current state of the task.
+ You can use the `zkCli` `get` command to retrieve the current state of a task.
+ In the example output below, the first line of the output shows that the task is currently unassigned.
++
+----
+get /hbase/splitlog/hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost6.sample.com%2C57020%2C1340474893287-splitting%2Fhost6.sample.com%253A57020.1340474893945
+
+unassigned host2.sample.com:57000
+cZxid = 0×7115
+ctime = Sat Jun 23 11:13:40 PDT 2012
+...
+----
++
+Based on the state of the task whose data is changed, the split log manager does one of the following:
++
+* Resubmit the task if it is unassigned
+* Heartbeat the task if it is assigned
+* Resubmit or fail the task if it is resigned (see <<distributed.log.replay.failure.reasons>>)
+* Resubmit or fail the task if it is completed with errors (see <<distributed.log.replay.failure.reasons>>)
+* Resubmit or fail the task if it could not complete due to errors (see <<distributed.log.replay.failure.reasons>>)
+* Delete the task if it is successfully completed or failed
++
+[[distributed.log.replay.failure.reasons]]
+[NOTE]
+.Reasons a Task Will Fail
+====
+* The task has been deleted.
+* The node no longer exists.
+* The log status manager failed to move the state of the task to `TASK_UNASSIGNED`.
+* The number of resubmits is over the resubmit threshold.
+====
+
+. Each RegionServer's split log worker performs the log-splitting tasks.
++
+Each RegionServer runs a daemon thread called the _split log worker_, which does the work to split the logs.
+The daemon thread starts when the RegionServer starts, and registers itself to watch HBase znodes.
+If any splitlog znode children change, it notifies a sleeping worker thread to wake up and grab more tasks.
+If if a worker's current task's node data is changed, the worker checks to see if the task has been taken by another worker.
+If so, the worker thread stops work on the current task.
++
+The worker monitors the splitlog znode constantly.
+When a new task appears, the split log worker retrieves the task paths and checks each one until it finds an unclaimed task, which it attempts to claim.
+If the claim was successful, it attempts to perform the task and updates the task's `state` property based on the splitting outcome.
+At this point, the split log worker scans for another unclaimed task.
++
+.How the Split Log Worker Approaches a Task
+* It queries the task state and only takes action if the task is in `TASK_UNASSIGNED `state.
+* If the task is is in `TASK_UNASSIGNED` state, the worker attempts to set the state to `TASK_OWNED` by itself.
+ If it fails to set the state, another worker will try to grab it.
+ The split log manager will also ask all workers to rescan later if the task remains unassigned.
+* If the worker succeeds in taking ownership of the task, it tries to get the task state again to make sure it really gets it asynchronously.
+ In the meantime, it starts a split task executor to do the actual work:
+** Get the HBase root folder, create a temp folder under the root, and split the log file to the temp folder.
+** If the split was successful, the task executor sets the task to state `TASK_DONE`.
+** If the worker catches an unexpected IOException, the task is set to state `TASK_ERR`.
+** If the worker is shutting down, set the the task to state `TASK_RESIGNED`.
+** If the task is taken by another worker, just log it.
+
+
+. The split log manager monitors for uncompleted tasks.
++
+The split log manager returns when all tasks are completed successfully.
+If all tasks are completed with some failures, the split log manager throws an exception so that the log splitting can be retried.
+Due to an asynchronous implementation, in very rare cases, the split log manager loses track of some completed tasks.
+For that reason, it periodically checks for remaining uncompleted task in its task map or ZooKeeper.
+If none are found, it throws an exception so that the log splitting can be retried right away instead of hanging there waiting for something that won't happen.
+
+
+[[distributed.log.replay]]
+====== Distributed Log Replay
+
+After a RegionServer fails, its failed regions are assigned to another RegionServer, which are marked as "recovering" in ZooKeeper.
+A split log worker directly replays edits from the WAL of the failed RegionServer to the regions at its new location.
+When a region is in "recovering" state, it can accept writes but no reads (including Append and Increment), region splits or merges.
+
+Distributed Log Replay extends the <<distributed.log.splitting>> framework.
+It works by directly replaying WAL edits to another RegionServer instead of creating _recovered.edits_ files.
+It provides the following advantages over distributed log splitting alone:
+
+* It eliminates the overhead of writing and reading a large number of _recovered.edits_ files.
+ It is not unusual for thousands of _recovered.edits_ files to be created and written concurrently during a RegionServer recovery.
+ Many small random writes can degrade overall system performance.
+* It allows writes even when a region is in recovering state.
+ It only takes seconds for a recovering region to accept writes again.
+
+.Enabling Distributed Log Replay
+To enable distributed log replay, set `hbase.master.distributed.log.replay` to `true`.
+This will be the default for HBase 0.99 (link:https://issues.apache.org/jira/browse/HBASE-10888[HBASE-10888]).
+
+You must also enable HFile version 3 (which is the default HFile format starting in HBase 0.99.
+See link:https://issues.apache.org/jira/browse/HBASE-10855[HBASE-10855]). Distributed log replay is unsafe for rolling upgrades.
+
+[[wal.disable]]
+==== Disabling the WAL
+
+It is possible to disable the WAL, to improve performance in certain specific situations.
+However, disabling the WAL puts your data at risk.
+The only situation where this is recommended is during a bulk load.
+This is because, in the event of a problem, the bulk load can be re-run with no risk of data loss.
+
+The WAL is disabled by calling the HBase client field `Mutation.writeToWAL(false)`.
+Use the `Mutation.setDurability(Durability.SKIP_WAL)` and Mutation.getDurability() methods to set and get the field's value.
+There is no way to disable the WAL for only a specific table.
+
+WARNING: If you disable the WAL for anything other than bulk loads, your data is at risk.
+
+[[regions.arch]]
+== Regions
+
+Regions are the basic element of availability and distribution for tables, and are comprised of a Store per Column Family.
+The hierarchy of objects is as follows:
+
+----
+Table (HBase table)
+ Region (Regions for the table)
+ Store (Store per ColumnFamily for each Region for the table)
+ MemStore (MemStore for each Store for each Region for the table)
+ StoreFile (StoreFiles for each Store for each Region for the table)
+ Block (Blocks within a StoreFile within a Store for each Region for the table)
+----
+
+For a description of what HBase files look like when written to HDFS, see <<trouble.namenode.hbase.objects>>.
+
+[[arch.regions.size]]
+=== Considerations for Number of Regions
+
+In general, HBase is designed to run with a small (20-200) number of relatively large (5-20Gb) regions per server.
+The considerations for this are as follows:
+
+[[too_many_regions]]
+==== Why should I keep my Region count low?
+
+Typically you want to keep your region count low on HBase for numerous reasons.
+Usually right around 100 regions per RegionServer has yielded the best results.
+Here are some of the reasons below for keeping region count low:
+
+. MSLAB (MemStore-local allocation buffer) requires 2MB per MemStore (that's 2MB per family per region). 1000 regions that have 2 families each is 3.9GB of heap used, and it's not even storing data yet.
+ NB: the 2MB value is configurable.
+. If you fill all the regions at somewhat the same rate, the global memory usage makes it that it forces tiny flushes when you have too many regions which in turn generates compactions.
+ Rewriting the same data tens of times is the last thing you want.
+ An example is filling 1000 regions (with one family) equally and let's consider a lower bound for global MemStore usage of 5GB (the region server would have a big heap). Once it reaches 5GB it will force flush the biggest region, at that point they should almost all have about 5MB of data so it would flush that amount.
+ 5MB inserted later, it would flush another region that will now have a bit over 5MB of data, and so on.
+ This is currently the main limiting factor for the number of regions; see <<ops.capacity.regions.count>> for detailed formula.
+. The master as is is allergic to tons of regions, and will take a lot of time assigning them and moving them around in batches.
+ The reason is that it's heavy on ZK usage, and it's not very async at the moment (could really be improved -- and has been improved a bunch in 0.96 HBase).
+. In older versions of HBase (pre-HFile v2, 0.90 and previous), tons of regions on a few RS can cause the store file index to rise, increasing heap usage and potentially creating memory pressure or OOME on the RSs
+
+Another issue is the effect of the number of regions on MapReduce jobs; it is typical to have one mapper per HBase region.
+Thus, hosting only 5 regions per RS may not be enough to get sufficient number of tasks for a MapReduce job, while 1000 regions will generate far too many tasks.
+
+See <<ops.capacity.regions>> for configuration guidelines.
+
+[[regions.arch.assignment]]
+=== Region-RegionServer Assignment
+
+This section describes how Regions are assigned to RegionServers.
+
+[[regions.arch.assignment.startup]]
+==== Startup
+
+When HBase starts regions are assigned as follows (short version):
+
+. The Master invokes the `AssignmentManager` upon startup.
+. The `AssignmentManager` looks at the existing region assignments in `hbase:meta`.
+. If the region assignment is still valid (i.e., if the RegionServer is still online) then the assignment is kept.
+. If the assignment is invalid, then the `LoadBalancerFactory` is invoked to assign the region.
+ The load balancer (`StochasticLoadBalancer` by default in HBase 1.0) assign the region to a RegionServer.
+. `hbase:meta` is updated with the RegionServer assignment (if needed) and the RegionServer start codes (start time of the RegionServer process) upon region opening by the RegionServer.
+
+[[regions.arch.assignment.failover]]
+==== Failover
+
+When a RegionServer fails:
+
+. The regions immediately become unavailable because the RegionServer is down.
+. The Master will detect that the RegionServer has failed.
+. The region assignments will be considered invalid and will be re-assigned just like the startup sequence.
+. In-flight queries are re-tried, and not lost.
+. Operations are switched to a new RegionServer within the following amount of time:
++
+[source]
+----
+ZooKeeper session timeout + split time + assignment/replay time
+----
+
+
+[[regions.arch.balancer]]
+==== Region Load Balancing
+
+Regions can be periodically moved by the <<master.processes.loadbalancer>>.
+
+[[regions.arch.states]]
+==== Region State Transition
+
+HBase maintains a state for each region and persists the state in `hbase:meta`.
+The state of the `hbase:meta` region itself is persisted in ZooKeeper.
+You can see the states of regions in transition in the Master web UI.
+Following is the list of possible region states.
+
+.Possible Region States
+* `OFFLINE`: the region is offline and not opening
+* `OPENING`: the region is in the process of being opened
+* `OPEN`: the region is open and the RegionServer has notified the master
+* `FAILED_OPEN`: the RegionServer failed to open the region
+* `CLOSING`: the region is in the process of being closed
+* `CLOSED`: the RegionServer has closed the region and notified the master
+* `FAILED_CLOSE`: the RegionServer failed to close the region
+* `SPLITTING`: the RegionServer notified the master that the region is splitting
+* `SPLIT`: the RegionServer notified the master that the region has finished splitting
+* `SPLITTING_NEW`: this region is being created by a split which is in progress
+* `MERGING`: the RegionServer notified the master that this region is being merged with another region
+* `MERGED`: the RegionServer notified the master that this region has been merged
+* `MERGING_NEW`: this region is being created by a merge of two regions
+
+.Region State Transitions
+image::region_states.png[]
+
+.Graph Legend
+* Brown: Offline state, a special state that can be transient (after closed before opening), terminal (regions of disabled tables), or initial (regions of newly created tables)
+* Palegreen: Online state that regions can serve requests
+* Lightblue: Transient states
+* Red: Failure states that need OPS attention
+* Gold: Terminal states of regions split/merged
+* Grey: Initial states of regions created through split/merge
+
+.Transition State Descriptions
+. The master moves a region from `OFFLINE` to `OPENING` state and tries to assign the region to a RegionServer.
+ The RegionServer may or may not have received the open region request.
+ The master retries sending the open region request to the RegionServer until the RPC goes through or the master runs out of retries.
+ After the RegionServer receives the open region request, the RegionServer begins opening the region.
+. If the master is running out of retries, the master prevents the RegionServer from opening the region by moving the region to `CLOSING` state and trying to close it, even if the RegionServer is starting to open the region.
+. After the RegionServer opens the region, it continues to try to notify the master until the master moves the region to `OPEN` state and notifies the RegionServer.
+ The region is now open.
+. If the RegionServer cannot open the region, it notifies the master.
+ The master moves the region to `CLOSED` state and tries to open the region on a different RegionServer.
+. If the master cannot open the region on any of a certain number of regions, it moves the region to `FAILED_OPEN` state, and takes no further action until an operator intervenes from the HBase shell, or the server is dead.
+. The master moves a region from `OPEN` to `CLOSING` state.
+ The RegionServer holding the region may or may not have received the close region request.
+ The master retries sending the close request to the server until the RPC goes through or the master runs out of retries.
+. If the RegionServer is not online, or throws `NotServingRegionException`, the master moves the region to `OFFLINE` state and re-assigns it to a different RegionServer.
+. If the RegionServer is online, but not reachable after the master runs out of retries, the master moves the region to `FAILED_CLOSE` state and takes no further action until an operator intervenes from the HBase shell, or the server is dead.
+. If the RegionServer gets the close region request, it closes the region and notifies the master.
+ The master moves the region to `CLOSED` state and re-assigns it to a different RegionServer.
+. Before assigning a region, the master moves the region to `OFFLINE` state automatically if it is in `CLOSED` state.
+. When a RegionServer is about to split a region, it notifies the master.
+ The master moves the region to be split from `OPEN` to `SPLITTING` state and add the two new regions to be created to the RegionServer.
+ These two regions are in `SPLITING_NEW` state initially.
+. After notifying the master, the RegionServer starts to split the region.
+ Once past the point of no return, the RegionServer notifies the master again so the master can update the `hbase:meta` table.
+ However, the master does not update the region states until it is notified by the server that the split is done.
+ If the split is successful, the splitting region is moved from `SPLITTING` to `SPLIT` state and the two new regions are moved from `SPLITTING_NEW` to `OPEN` state.
+. If the split fails, the splitting region is moved from `SPLITTING` back to `OPEN` state, and the two new regions which were created are moved from `SPLITTING_NEW` to `OFFLINE` state.
+. When a RegionServer is about to merge two regions, it notifies the master first.
+ The master moves the two regions to be merged from `OPEN` to `MERGING` state, and adds the new region which will hold the contents of the merged regions region to the RegionServer.
+ The new region is in `MERGING_NEW` state initially.
+. After notifying the master, the RegionServer starts to merge the two regions.
+ Once past the point of no return, the RegionServer notifies the master again so the master can update the META.
+ However, the master does not update the region states until it is notified by the RegionServer that the merge has completed.
+ If the merge is successful, the two merging regions are moved from `MERGING` to `MERGED` state and the new region is moved from `MERGING_NEW` to `OPEN` state.
+. If the merge fails, the two merging regions are moved from `MERGING` back to `OPEN` state, and the new region which was created to hold the contents of the merged regions is moved from `MERGING_NEW` to `OFFLINE` state.
+. For regions in `FAILED_OPEN` or `FAILED_CLOSE` states, the master tries to close them again when they are reassigned by an operator via HBase Shell.
+
+[[regions.arch.locality]]
+=== Region-RegionServer Locality
+
+Over time, Region-RegionServer locality is achieved via HDFS block replication.
+The HDFS client does the following by default when choosing locations to write replicas:
+
+. First replica is written to local node
+. Second replica is written to a random node on another rack
+. Third replica is written on the same rack as the second, but on a different node chosen randomly
+. Subsequent replicas are written on random nodes on the cluster.
+ See _Replica Placement: The First Baby Steps_ on this page: link:http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture]
+
+Thus, HBase eventually achieves locality for a region after a flush or a compaction.
+In a RegionServer failover situation a RegionServer may be assigned regions with non-local StoreFiles (because none of the replicas are local), however as new data is written in the region, or the table is compacted and StoreFiles are re-written, they will become "local" to the RegionServer.
+
+For more information, see _Replica Placement: The First Baby Steps_ on this page: link:http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html[HDFS Architecture] and also Lars George's blog on link:http://www.larsgeorge.com/2010/05/hbase-file-locality-in-hdfs.html[HBase and HDFS locality].
+
+[[arch.region.splits]]
+=== Region Splits
+
+Regions split when they reach a configured threshold.
+Below we treat the topic in short.
+For a longer exposition, see link:http://hortonworks.com/blog/apache-hbase-region-splitting-and-merging/[Apache HBase Region Splitting and Merging] by our Enis Soztutar.
+
+Splits run unaided on the RegionServer; i.e. the Master does not participate.
+The RegionServer splits a region, offlines the split region and then adds the daughter regions to `hbase:meta`, opens daughters on the parent's hosting RegionServer and then reports the split to the Master.
+See <<disable.splitting>> for how to manually manage splits (and for why you might do this).
+
+==== Custom Split Policies
+ou can override the default split policy using a custom link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/regionserver/RegionSplitPolicy.html[RegionSplitPolicy](HBase 0.94+). Typically a custom split policy should extend
+HBase's default split policy: link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/regionserver/IncreasingToUpperBoundRegionSplitPolicy.html[IncreasingToUpperBoundRegionSplitPolicy].
+
+The policy can set globally through the HBase configuration or on a per-table
+basis.
+
+.Configuring the Split Policy Globally in _hbase-site.xml_
+[source,xml]
+----
+<property>
+ <name>hbase.regionserver.region.split.policy</name>
+ <value>org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy</value>
+</property>
+----
+
+.Configuring a Split Policy On a Table Using the Java API
+[source,java]
+HTableDescriptor tableDesc = new HTableDescriptor("test");
+tableDesc.setValue(HTableDescriptor.SPLIT_POLICY, ConstantSizeRegionSplitPolicy.class.getName());
+tableDesc.addFamily(new HColumnDescriptor(Bytes.toBytes("cf1")));
+admin.createTable(tableDesc);
+----
+
+[source]
+.Configuring the Split Policy On a Table Using HBase Shell
+----
+hbase> create 'test', {METHOD => 'table_att', CONFIG => {'SPLIT_POLICY' => 'org.apache.hadoop.hbase.regionserver.ConstantSizeRegionSplitPolicy'}},
+{NAME => 'cf1'}
+----
+
+The default split policy can be overwritten using a custom link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/regionserver/RegionSplitPolicy.html[RegionSplitPolicy(HBase 0.94+)]. Typically a custom split policy should extend HBase's default split policy: link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/regionserver/ConstantSizeRegionSplitPolicy.html[ConstantSizeRegionSplitPolicy].
+
+The policy can be set globally through the HBaseConfiguration used or on a per table basis:
+[source,java]
+----
+HTableDescriptor myHtd = ...;
+myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
+----
+
+[[manual_region_splitting_decisions]]
+=== Manual Region Splitting
+
+It is possible to manually split your table, either at table creation (pre-splitting), or at a later time as an administrative action.
+You might choose to split your region for one or more of the following reasons.
+There may be other valid reasons, but the need to manually split your table might also point to problems with your schema design.
+
+.Reasons to Manually Split Your Table
+* Your data is sorted by timeseries or another similar algorithm that sorts new data at the end of the table.
+ This means that the Region Server holding the last region is always under load, and the other Region Servers are idle, or mostly idle.
+ See also <<timeseries>>.
+* You have developed an unexpected hotspot in one region of your table.
+ For instance, an application which tracks web searches might be inundated by a lot of searches for a celebrity in the event of news about that celebrity.
+ See <<perf.one.region,perf.one.region>> for more discussion about this particular scenario.
+* After a big increase in the number of RegionServers in your cluster, to get the load spread out quickly.
+* Before a bulk-load which is likely to cause unusual and uneven load across regions.
+
+See <<disable.splitting>> for a discussion about the dangers and possible benefits of managing splitting completely manually.
+
+==== Determining Split Points
+
+The goal of splitting your table manually is to improve the chances of balancing the load across the cluster in situations where good rowkey design alone won't get you there.
+Keeping that in mind, the way you split your regions is very dependent upon the characteristics of your data.
+It may be that you already know the best way to split your table.
+If not, the way you split your table depends on what your keys are like.
+
+Alphanumeric Rowkeys::
+ If your rowkeys start with a letter or number, you can split your table at letter or number boundaries.
+ For instance, the following command creates a table with regions that split at each vowel, so the first region has A-D, the second region has E-H, the third region has I-N, the fourth region has O-V, and the fifth region has U-Z.
+
+Using a Custom Algorithm::
+ The RegionSplitter tool is provided with HBase, and uses a _SplitAlgorithm_ to determine split points for you.
+ As parameters, you give it the algorithm, desired number of regions, and column families.
+ It includes two split algorithms.
+ The first is the `link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/RegionSplitter.HexStringSplit.html[HexStringSplit]` algorithm, which assumes the row keys are hexadecimal strings.
+ The second, `link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/RegionSplitter.UniformSplit.html[UniformSplit]`, assumes the row keys are random byte arrays.
+ You will probably need to develop your own `link:http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/RegionSplitter.SplitAlgorithm.html[SplitAlgorithm]`, using the provided ones as models.
+
+=== Online Region Merges
+
+Both Master and RegionServer participate in the event of online region merges.
+Client sends merge RPC to the master, then the master moves the regions together to the RegionServer where the more heavily loaded region resided. Finally the master sends the merge request to this RegionServer which then runs the merge.
+Similar to process of region splitting, region merges run as a local transaction on the RegionServer. It offlines the regions and then merges two regions on the file system, atomically delete merging regions from `hbase:meta` and adds the merged region to `hbase:meta`, opens the merged region on the RegionServer and reports the merge to the Master.
+
+An example of region merges in the HBase shell
+[source,bourne]
+----
+$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME'
+$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME', true
+----
+It's an asynchronous operation and call returns immediately without waiting merge completed.
+Passing `true` as the optional third parameter will force a merge. Normally only adjacent regions can be merged.
+The `force` parameter overrides this behaviour and is for expert use only.
+
+=== Store
+
+A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region.
+
+[[store.memstore]]
+==== MemStore
+
+The MemStore holds in-memory modifications to the Store.
+Modifications are Cells/KeyValues.
+When a flush is requested, the current MemStore is moved to a snapshot and is cleared.
+HBase continues to serve edits from the new MemStore and backing snapshot until the flusher reports that the flush succeeded.
+At this point, the
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[04/18] hbase git commit: HBASE-13665 Fix docs and site building on
branch-1
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+////
+/**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+////
+
+[[trouble]]
+= Troubleshooting and Debugging Apache HBase
+:doctype: book
+:numbered:
+:toc: left
+:icons: font
+:experimental:
+
+[[trouble.general]]
+== General Guidelines
+
+Always start with the master log (TODO: Which lines?). Normally it's just printing the same lines over and over again.
+If not, then there's an issue.
+Google or link:http://search-hadoop.com[search-hadoop.com] should return some hits for those exceptions you're seeing.
+
+An error rarely comes alone in Apache HBase, usually when something gets screwed up what will follow may be hundreds of exceptions and stack traces coming from all over the place.
+The best way to approach this type of problem is to walk the log up to where it all began, for example one trick with RegionServers is that they will print some metrics when aborting so grepping for _Dump_ should get you around the start of the problem.
+
+RegionServer suicides are 'normal', as this is what they do when something goes wrong.
+For example, if ulimit and max transfer threads (the two most important initial settings, see <<ulimit>> and <<dfs.datanode.max.transfer.threads>>) aren't changed, it will make it impossible at some point for DataNodes to create new threads that from the HBase point of view is seen as if HDFS was gone.
+Think about what would happen if your MySQL database was suddenly unable to access files on your local file system, well it's the same with HBase and HDFS.
+Another very common reason to see RegionServers committing seppuku is when they enter prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout.
+For more information on GC pauses, see the link:http://www.cloudera.com/blog/2011/02/avoiding-full-gcs-in-hbase-with-memstore-local-allocation-buffers-part-1/[3 part blog post] by Todd Lipcon and <<gcpause>> above.
+
+[[trouble.log]]
+== Logs
+
+The key process logs are as follows... (replace <user> with the user that started the service, and <hostname> for the machine name)
+
+NameNode: _$HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log_
+
+DataNode: _$HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log_
+
+JobTracker: _$HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log_
+
+TaskTracker: _$HADOOP_HOME/logs/hadoop-<user>-tasktracker-<hostname>.log_
+
+HMaster: _$HBASE_HOME/logs/hbase-<user>-master-<hostname>.log_
+
+RegionServer: _$HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log_
+
+ZooKeeper: _TODO_
+
+[[trouble.log.locations]]
+=== Log Locations
+
+For stand-alone deployments the logs are obviously going to be on a single machine, however this is a development configuration only.
+Production deployments need to run on a cluster.
+
+[[trouble.log.locations.namenode]]
+==== NameNode
+
+The NameNode log is on the NameNode server.
+The HBase Master is typically run on the NameNode server, and well as ZooKeeper.
+
+For smaller clusters the JobTracker/ResourceManager is typically run on the NameNode server as well.
+
+[[trouble.log.locations.datanode]]
+==== DataNode
+
+Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.
+
+Additionally, each DataNode server will also have a TaskTracker/NodeManager log for MapReduce task execution.
+
+[[trouble.log.levels]]
+=== Log Levels
+
+[[rpc.logging]]
+==== Enabling RPC-level logging
+
+Enabling the RPC-level logging on a RegionServer can often given insight on timings at the server.
+Once enabled, the amount of log spewed is voluminous.
+It is not recommended that you leave this logging on for more than short bursts of time.
+To enable RPC-level logging, browse to the RegionServer UI and click on _Log Level_.
+Set the log level to `DEBUG` for the package `org.apache.hadoop.ipc` (Thats right, for `hadoop.ipc`, NOT, `hbase.ipc`). Then tail the RegionServers log.
+Analyze.
+
+To disable, set the logging level back to `INFO` level.
+
+[[trouble.log.gc]]
+=== JVM Garbage Collection Logs
+
+HBase is memory intensive, and using the default GC you can see long pauses in all threads including the _Juliet Pause_ aka "GC of Death". To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.
+
+To enable, in _hbase-env.sh_, uncomment one of the below lines :
+
+[source,bourne]
+----
+
+# This enables basic gc logging to the .out file.
+# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
+
+# This enables basic gc logging to its own file.
+# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"
+
+# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
+# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"
+
+# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR.
+----
+
+At this point you should see logs like so:
+
+[source]
+----
+
+64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
+64898.953: [CMS-concurrent-mark-start]
+64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]
+----
+
+In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark.
+This pauses the entire VM, all threads for that period of time.
+
+The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds.
+It has reduced the "ParNew" from about 5.5m to 576k.
+Later on in this cycle we see:
+
+[source]
+----
+
+64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs]
+64901.445: [CMS-concurrent-preclean-start]
+64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
+64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
+64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
+64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs]
+64901.578: [CMS-concurrent-abortable-preclean-start]
+64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
+64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs]
+64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
+64901.621: [CMS-concurrent-sweep-start]
+----
+
+The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds.
+But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in time.
+
+There are a few more minor GCs, then there is a pause at the 2nd last line:
+[source]
+----
+
+64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
+----
+
+The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.
+
+At this point the sweep starts, and you can watch the heap size go down:
+
+[source]
+----
+
+64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+... lines removed ...
+64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs]
+----
+
+At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).
+
+The key points here is to keep all these pauses low.
+CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.
+
+This can be due to the size of the ParNew, which should be relatively small.
+If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if its too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.
+
+Add the below line in _hbase-env.sh_:
+[source,bourne]
+----
+
+export SERVER_GC_OPTS="$SERVER_GC_OPTS -XX:NewSize=64m -XX:MaxNewSize=64m"
+----
+
+Similarly, to enable GC logging for client processes, uncomment one of the below lines in _hbase-env.sh_:
+
+[source,bourne]
+----
+
+# This enables basic gc logging to the .out file.
+# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
+
+# This enables basic gc logging to its own file.
+# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"
+
+# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
+# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"
+
+# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR .
+----
+
+For more information on GC pauses, see the link:http://www.cloudera.com/blog/2011/02/avoiding-full-gcs-in-hbase-with-memstore-local-allocation-buffers-part-1/[3 part blog post] by Todd Lipcon and <<gcpause>> above.
+
+[[trouble.resources]]
+== Resources
+
+[[trouble.resources.searchhadoop]]
+=== search-hadoop.com
+
+link:http://search-hadoop.com[search-hadoop.com] indexes all the mailing lists and is great for historical searches.
+Search here first when you have an issue as its more than likely someone has already had your problem.
+
+[[trouble.resources.lists]]
+=== Mailing Lists
+
+Ask a question on the link:http://hbase.apache.org/mail-lists.html[Apache HBase mailing lists].
+The 'dev' mailing list is aimed at the community of developers actually building Apache HBase and for features currently under development, and 'user' is generally used for questions on released versions of Apache HBase.
+Before going to the mailing list, make sure your question has not already been answered by searching the mailing list archives first.
+Use <<trouble.resources.searchhadoop>>.
+Take some time crafting your question.
+See link:http://www.mikeash.com/getting_answers.html[Getting Answers] for ideas on crafting good questions.
+A quality question that includes all context and exhibits evidence the author has tried to find answers in the manual and out on lists is more likely to get a prompt response.
+
+[[trouble.resources.irc]]
+=== IRC
+
+#hbase on irc.freenode.net
+
+[[trouble.resources.jira]]
+=== JIRA
+
+link:https://issues.apache.org/jira/browse/HBASE[JIRA] is also really helpful when looking for Hadoop/HBase-specific issues.
+
+[[trouble.tools]]
+== Tools
+
+[[trouble.tools.builtin]]
+=== Builtin Tools
+
+[[trouble.tools.builtin.webmaster]]
+==== Master Web Interface
+
+The Master starts a web-interface on port 16010 by default.
+(Up to and including 0.98 this was port 60010)
+
+The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally, the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap). The Master web UI allows navigation to each RegionServer's web UI.
+
+[[trouble.tools.builtin.webregion]]
+==== RegionServer Web Interface
+
+RegionServers starts a web-interface on port 16030 by default.
+(Up to an including 0.98 this was port 60030)
+
+The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).
+
+See <<hbase_metrics>> for more information in metric definitions.
+
+[[trouble.tools.builtin.zkcli]]
+==== zkcli
+
+`zkcli` is a very useful tool for investigating ZooKeeper-related issues.
+To invoke:
+[source,bourne]
+----
+./hbase zkcli -server host:port <cmd> <args>
+----
+
+The commands (and arguments) are:
+
+[source]
+----
+ connect host:port
+ get path [watch]
+ ls path [watch]
+ set path data [version]
+ delquota [-n|-b] path
+ quit
+ printwatches on|off
+ create [-s] [-e] path data acl
+ stat path [watch]
+ close
+ ls2 path [watch]
+ history
+ listquota path
+ setAcl path acl
+ getAcl path
+ sync path
+ redo cmdno
+ addauth scheme auth
+ delete path [version]
+ setquota -n|-b val path
+----
+
+[[trouble.tools.external]]
+=== External Tools
+
+[[trouble.tools.tail]]
+==== tail
+
+`tail` is the command line tool that lets you look at the end of a file.
+Add the `-f` option and it will refresh when new data is available.
+It's useful when you are wondering what's happening, for example, when a cluster is taking a long time to shutdown or startup as you can just fire a new terminal and tail the master log (and maybe a few RegionServers).
+
+[[trouble.tools.top]]
+==== top
+
+`top` is probably one of the most important tools when first trying to see what's running on a machine and how the resources are consumed.
+Here's an example from production system:
+
+[source]
+----
+top - 14:46:59 up 39 days, 11:55, 1 user, load average: 3.75, 3.57, 3.84
+Tasks: 309 total, 1 running, 308 sleeping, 0 stopped, 0 zombie
+Cpu(s): 4.5%us, 1.6%sy, 0.0%ni, 91.7%id, 1.4%wa, 0.1%hi, 0.6%si, 0.0%st
+Mem: 24414432k total, 24296956k used, 117476k free, 7196k buffers
+Swap: 16008732k total, 14348k used, 15994384k free, 11106908k cached
+
+ PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
+15558 hadoop 18 -2 3292m 2.4g 3556 S 79 10.4 6523:52 java
+13268 hadoop 18 -2 8967m 8.2g 4104 S 21 35.1 5170:30 java
+ 8895 hadoop 18 -2 1581m 497m 3420 S 11 2.1 4002:32 java
+…
+----
+
+Here we can see that the system load average during the last five minutes is 3.75, which very roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes.
+In general, the _perfect_ utilization equals to the number of cores, under that number the machine is under utilized and over that the machine is over utilized.
+This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001.
+
+Apart from load, we can see that the system is using almost all its available RAM but most of it is used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high numbers would indicate swapping activity which is the nemesis of performance of Java systems.
+Another way to detect swapping is when the load average goes through the roof (although this could also be caused by things like a dying disk, among others).
+
+The list of processes isn't super useful by default, all we know is that 3 java processes are using about 111% of the CPUs.
+To know which is which, simply type `c` and each line will be expanded.
+Typing `1` will give you the detail of how each CPU is used instead of the average for all of them like shown here.
+
+[[trouble.tools.jps]]
+==== jps
+
+`jps` is shipped with every JDK and gives the java process ids for the current user (if root, then it gives the ids for all users). Example:
+
+[source,bourne]
+----
+hadoop@sv4borg12:~$ jps
+1322 TaskTracker
+17789 HRegionServer
+27862 Child
+1158 DataNode
+25115 HQuorumPeer
+2950 Jps
+19750 ThriftServer
+18776 jmx
+----
+
+In order, we see a:
+
+* Hadoop TaskTracker, manages the local Childs
+* HBase RegionServer, serves regions
+* Child, its MapReduce task, cannot tell which type exactly
+* Hadoop TaskTracker, manages the local Childs
+* Hadoop DataNode, serves blocks
+* HQuorumPeer, a ZooKeeper ensemble member
+* Jps, well... it's the current process
+* ThriftServer, it's a special one will be running only if thrift was started
+* jmx, this is a local process that's part of our monitoring platform ( poorly named maybe). You probably don't have that.
+
+You can then do stuff like checking out the full command line that started the process:
+
+[source,bourne]
+----
+hadoop@sv4borg12:~$ ps aux | grep HRegionServer
+hadoop 17789 155 35.2 9067824 8604364 ? S<l Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start
+----
+
+[[trouble.tools.jstack]]
+==== jstack
+
+`jstack` is one of the most important tools when trying to figure out what a java process is doing apart from looking at the logs.
+It has to be used in conjunction with jps in order to give it a process id.
+It shows a list of threads, each one has a name, and they appear in the order that they were created (so the top ones are the most recent threads). Here are a few example:
+
+The main thread of a RegionServer waiting for something to do from the master:
+
+[source]
+----
+"regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
+java.lang.Thread.State: TIMED_WAITING (parking)
+ at sun.misc.Unsafe.park(Native Method)
+ - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
+ at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
+ at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
+ at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
+ at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
+ at java.lang.Thread.run(Thread.java:619)
+----
+
+The MemStore flusher thread that is currently flushing to a file:
+
+[source]
+----
+"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
+java.lang.Thread.State: WAITING (on object monitor)
+ at java.lang.Object.wait(Native Method)
+ at java.lang.Object.wait(Object.java:485)
+ at org.apache.hadoop.ipc.Client.call(Client.java:803)
+ - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
+ at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
+ at $Proxy1.complete(Unknown Source)
+ at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
+ at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
+ at java.lang.reflect.Method.invoke(Method.java:597)
+ at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
+ at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
+ at $Proxy1.complete(Unknown Source)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
+ - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
+ at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
+ at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
+ at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
+ at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
+ at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
+ - locked <0x00007f16d00e6f08> (a java.lang.Object)
+ at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
+ at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
+ at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
+ at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
+ at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
+ at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)
+----
+
+A handler thread that's waiting for stuff to do (like put, delete, scan, etc):
+
+[source]
+----
+"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
+ java.lang.Thread.State: WAITING (parking)
+ at sun.misc.Unsafe.park(Native Method)
+ - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
+ at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
+ at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
+ at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
+ at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)
+----
+
+And one that's busy doing an increment of a counter (it's in the phase where it's trying to create a scanner in order to read the last value):
+
+[source]
+----
+"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
+ java.lang.Thread.State: RUNNABLE
+ at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56)
+ at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79)
+ at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
+ at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209)
+ at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
+ at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
+ at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
+ at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
+ at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
+ at java.lang.reflect.Method.invoke(Method.java:597)
+ at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
+ at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)
+----
+
+A thread that receives data from HDFS:
+
+[source]
+----
+"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
+ java.lang.Thread.State: RUNNABLE
+ at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
+ at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
+ at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
+ at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
+ - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
+ - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
+ - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
+ at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
+ at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
+ at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
+ at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
+ at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
+ at java.io.FilterInputStream.read(FilterInputStream.java:116)
+ at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304)
+ at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
+ at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
+ - locked <0x00007f1808539178> (a java.io.BufferedInputStream)
+ at java.io.DataInputStream.readInt(DataInputStream.java:370)
+ at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
+ at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)
+----
+
+And here is a master trying to recover a lease after a RegionServer died:
+
+[source]
+----
+"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70]
+--
+ java.lang.Thread.State: WAITING (on object monitor)
+ at java.lang.Object.wait(Native Method)
+ at java.lang.Object.wait(Object.java:485)
+ at org.apache.hadoop.ipc.Client.call(Client.java:726)
+ - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
+ at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
+ at $Proxy1.recoverBlock(Unknown Source)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java:2832)
+ at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
+ at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186)
+ at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
+ at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
+ at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
+ at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
+ at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648)
+ at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
+ at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)
+----
+
+[[trouble.tools.opentsdb]]
+==== OpenTSDB
+
+link:http://opentsdb.net[OpenTSDB] is an excellent alternative to Ganglia as it uses Apache HBase to store all the time series and doesn't have to downsample.
+Monitoring your own HBase cluster that hosts OpenTSDB is a good exercise.
+
+Here's an example of a cluster that's suffering from hundreds of compactions launched almost all around the same time, which severely affects the IO performance: (TODO: insert graph plotting compactionQueueSize)
+
+It's a good practice to build dashboards with all the important graphs per machine and per cluster so that debugging issues can be done with a single quick look.
+For example, at StumbleUpon there's one dashboard per cluster with the most important metrics from both the OS and Apache HBase.
+You can then go down at the machine level and get even more detailed metrics.
+
+[[trouble.tools.clustersshtop]]
+==== clusterssh+top
+
+clusterssh+top, it's like a poor man's monitoring system and it can be quite useful when you have only a few machines as it's very easy to setup.
+Starting clusterssh will give you one terminal per machine and another terminal in which whatever you type will be retyped in every window.
+This means that you can type `top` once and it will start it for all of your machines at the same time giving you full view of the current state of your cluster.
+You can also tail all the logs at the same time, edit files, etc.
+
+[[trouble.client]]
+== Client
+
+For more information on the HBase client, see <<client,client>>.
+
+[[trouble.client.scantimeout]]
+=== ScannerTimeoutException or UnknownScannerException
+
+This is thrown if the time between RPC calls from the client to RegionServer exceeds the scan timeout.
+For example, if `Scan.setCaching` is set to 500, then there will be an RPC call to fetch the next batch of rows every 500 `.next()` calls on the ResultScanner because data is being transferred in blocks of 500 rows to the client.
+Reducing the setCaching value may be an option, but setting this value too low makes for inefficient processing on numbers of rows.
+
+See <<perf.hbase.client.caching>>.
+
+=== Performance Differences in Thrift and Java APIs
+
+Poor performance, or even `ScannerTimeoutExceptions`, can occur if `Scan.setCaching` is too high, as discussed in <<trouble.client.scantimeout>>.
+If the Thrift client uses the wrong caching settings for a given workload, performance can suffer compared to the Java API.
+To set caching for a given scan in the Thrift client, use the `scannerGetList(scannerId, numRows)` method, where `numRows` is an integer representing the number of rows to cache.
+In one case, it was found that reducing the cache for Thrift scans from 1000 to 100 increased performance to near parity with the Java API given the same queries.
+
+See also Jesse Andersen's link:http://blog.cloudera.com/blog/2014/04/how-to-use-the-hbase-thrift-interface-part-3-using-scans/[blog post] about using Scans with Thrift.
+
+[[trouble.client.lease.exception]]
+=== `LeaseException` when calling `Scanner.next`
+
+In some situations clients that fetch data from a RegionServer get a LeaseException instead of the usual <<trouble.client.scantimeout>>.
+Usually the source of the exception is `org.apache.hadoop.hbase.regionserver.Leases.removeLease(Leases.java:230)` (line number may vary). It tends to happen in the context of a slow/freezing `RegionServer#next` call.
+It can be prevented by having `hbase.rpc.timeout` > `hbase.regionserver.lease.period`.
+Harsh J investigated the issue as part of the mailing list thread link:http://mail-archives.apache.org/mod_mbox/hbase-user/201209.mbox/%3CCAOcnVr3R-LqtKhFsk8Bhrm-YW2i9O6J6Fhjz2h7q6_sxvwd2yw%40mail.gmail.com%3E[HBase, mail # user - Lease does not exist exceptions]
+
+[[trouble.client.scarylogs]]
+=== Shell or client application throws lots of scary exceptions during normal operation
+
+Since 0.20.0 the default log level for `org.apache.hadoop.hbase.*`is DEBUG.
+
+On your clients, edit _$HBASE_HOME/conf/log4j.properties_ and change this: `log4j.logger.org.apache.hadoop.hbase=DEBUG` to this: `log4j.logger.org.apache.hadoop.hbase=INFO`, or even `log4j.logger.org.apache.hadoop.hbase=WARN`.
+
+[[trouble.client.longpauseswithcompression]]
+=== Long Client Pauses With Compression
+
+This is a fairly frequent question on the Apache HBase dist-list.
+The scenario is that a client is typically inserting a lot of data into a relatively un-optimized HBase cluster.
+Compression can exacerbate the pauses, although it is not the source of the problem.
+
+See <<precreate.regions>> on the pattern for pre-creating regions and confirm that the table isn't starting with a single region.
+
+See <<perf.configurations>> for cluster configuration, particularly `hbase.hstore.blockingStoreFiles`, `hbase.hregion.memstore.block.multiplier`, `MAX_FILESIZE` (region size), and `MEMSTORE_FLUSHSIZE.`
+
+A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked on the MemStores which are blocked by the flusher thread which is blocked because there are too many files to compact because the compactor is given too many small files to compact and has to compact the same data repeatedly.
+This situation can occur even with minor compactions.
+Compounding this situation, Apache HBase doesn't compress data in memory.
+Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which results in a smaller StoreFile.
+The upside is that more data is packed into the same region, but performance is achieved by being able to write larger files - which is why HBase waits until the flushsize before writing a new StoreFile.
+And smaller StoreFiles become targets for compaction.
+Without compression the files are much bigger and don't need as much compaction, however this is at the expense of I/O.
+
+For additional information, see this thread on link:http://search-hadoop.com/m/WUnLM6ojHm1/Long+client+pauses+with+compression&subj=Long+client+pauses+with+compression[Long client pauses with compression].
+
+[[trouble.client.security.rpc.krb]]
+=== Secure Client Connect ([Caused by GSSException: No valid credentials provided...])
+
+You may encounter the following error:
+
+----
+Secure Client Connect ([Caused by GSSException: No valid credentials provided
+ (Mechanism level: Request is a replay (34) V PROCESS_TGS)])
+----
+
+This issue is caused by bugs in the MIT Kerberos replay_cache component, link:http://krbdev.mit.edu/rt/Ticket/Display.html?id=1201[#1201] and link:http://krbdev.mit.edu/rt/Ticket/Display.html?id=5924[#5924].
+These bugs caused the old version of krb5-server to erroneously block subsequent requests sent from a Principal.
+This caused krb5-server to block the connections sent from one Client (one HTable instance with multi-threading connection instances for each RegionServer); Messages, such as `Request is a replay (34)`, are logged in the client log You can ignore the messages, because HTable will retry 5 * 10 (50) times for each failed connection by default.
+HTable will throw IOException if any connection to the RegionServer fails after the retries, so that the user client code for HTable instance can handle it further.
+NOTE: `HTable` is deprecated in HBase 1.0, in favor of `Table`.
+
+Alternatively, update krb5-server to a version which solves these issues, such as krb5-server-1.10.3.
+See JIRA link:https://issues.apache.org/jira/browse/HBASE-10379[HBASE-10379] for more details.
+
+[[trouble.client.zookeeper]]
+=== ZooKeeper Client Connection Errors
+
+Errors like this...
+
+[source]
+----
+
+11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
+ unexpected error, closing socket connection and attempting reconnect
+ java.net.ConnectException: Connection refused: no further information
+ at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
+ at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
+ 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
+ server localhost/127.0.0.1:2181
+ 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
+ unexpected error, closing socket connection and attempting reconnect
+ java.net.ConnectException: Connection refused: no further information
+ at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
+ at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
+ 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
+ server localhost/127.0.0.1:2181
+----
+
+...are either due to ZooKeeper being down, or unreachable due to network issues.
+
+The utility <<trouble.tools.builtin.zkcli>> may help investigate ZooKeeper issues.
+
+[[trouble.client.oome.directmemory.leak]]
+=== Client running out of memory though heap size seems to be stable (but the off-heap/direct heap keeps growing)
+
+You are likely running into the issue that is described and worked through in the mail thread link:http://search-hadoop.com/m/ubhrX8KvcH/Suspected+memory+leak&subj=Re+Suspected+memory+leak[HBase, mail # user - Suspected memory leak] and continued over in link:http://search-hadoop.com/m/p2Agc1Zy7Va/MaxDirectMemorySize+Was%253A+Suspected+memory+leak&subj=Re+FeedbackRe+Suspected+memory+leak[HBase, mail # dev - FeedbackRe: Suspected memory leak].
+A workaround is passing your client-side JVM a reasonable value for `-XX:MaxDirectMemorySize`.
+By default, the `MaxDirectMemorySize` is equal to your `-Xmx` max heapsize setting (if `-Xmx` is set). Try setting it to something smaller (for example, one user had success setting it to `1g` when they had a client-side heap of `12g`). If you set it too small, it will bring on `FullGCs` so keep it a bit hefty.
+You want to make this setting client-side only especially if you are running the new experimental server-side off-heap cache since this feature depends on being able to use big direct buffers (You may have to keep separate client-side and server-side config dirs).
+
+[[trouble.client.slowdown.admin]]
+=== Client Slowdown When Calling Admin Methods (flush, compact, etc.)
+
+This is a client issue fixed by link:https://issues.apache.org/jira/browse/HBASE-5073[HBASE-5073] in 0.90.6.
+There was a ZooKeeper leak in the client and the client was getting pummeled by ZooKeeper events with each additional invocation of the admin API.
+
+[[trouble.client.security.rpc]]
+=== Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided(Mechanism level: Failed to find any Kerberos tgt)])
+
+There can be several causes that produce this symptom.
+
+First, check that you have a valid Kerberos ticket.
+One is required in order to set up communication with a secure Apache HBase cluster.
+Examine the ticket currently in the credential cache, if any, by running the `klist` command line utility.
+If no ticket is listed, you must obtain a ticket by running the `kinit` command with either a keytab specified, or by interactively entering a password for the desired principal.
+
+Then, consult the link:http://docs.oracle.com/javase/1.5.0/docs/guide/security/jgss/tutorials/Troubleshooting.html[Java Security Guide troubleshooting section].
+The most common problem addressed there is resolved by setting `javax.security.auth.useSubjectCredsOnly` system property value to `false`.
+
+Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher.
+If you have this problematic combination of components in your environment, to work around this problem, first log in with `kinit` and then immediately refresh the credential cache with `kinit -R`.
+The refresh will rewrite the credential cache without the problematic formatting.
+
+Finally, depending on your Kerberos configuration, you may need to install the link:http://docs.oracle.com/javase/1.4.2/docs/guide/security/jce/JCERefGuide.html[Java Cryptography Extension], or JCE.
+Insure the JCE jars are on the classpath on both server and client systems.
+
+You may also need to download the link:http://www.oracle.com/technetwork/java/javase/downloads/jce-6-download-429243.html[unlimited strength JCE policy files].
+Uncompress and extract the downloaded file, and install the policy jars into _<java-home>/lib/security_.
+
+[[trouble.mapreduce]]
+== MapReduce
+
+[[trouble.mapreduce.local]]
+=== You Think You're On The Cluster, But You're Actually Local
+
+This following stacktrace happened using `ImportTsv`, but things like this can happen on any job with a mis-configuration.
+
+[source,text]
+----
+ WARN mapred.LocalJobRunner: job_local_0001
+java.lang.IllegalArgumentException: Can't read partitions file
+ at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111)
+ at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
+ at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
+ at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560)
+ at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
+ at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
+ at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
+Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
+ at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
+ at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
+ at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
+ at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424)
+ at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419)
+ at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296)
+----
+
+...see the critical portion of the stack? It's...
+
+[source]
+----
+at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
+----
+
+LocalJobRunner means the job is running locally, not on the cluster.
+
+To solve this problem, you should run your MR job with your `HADOOP_CLASSPATH` set to include the HBase dependencies.
+The "hbase classpath" utility can be used to do this easily.
+For example (substitute VERSION with your HBase version):
+
+[source,bourne]
+----
+HADOOP_CLASSPATH=`hbase classpath` hadoop jar $HBASE_HOME/hbase-server-VERSION.jar rowcounter usertable
+----
+
+See http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpathfor more information on HBase MapReduce jobs and classpaths.
+
+[[trouble.hbasezerocopybytestring]]
+=== Launching a job, you get java.lang.IllegalAccessError: com/google/protobuf/HBaseZeroCopyByteString or class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString
+
+See link:https://issues.apache.org/jira/browse/HBASE-10304[HBASE-10304 Running an hbase job jar: IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString] and link:https://issues.apache.org/jira/browse/HBASE-11118[HBASE-11118 non environment variable solution for "IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString"].
+The issue can also show up when trying to run spark jobs.
+See link:https://issues.apache.org/jira/browse/HBASE-10877[HBASE-10877 HBase non-retriable exception list should be expanded].
+
+[[trouble.namenode]]
+== NameNode
+
+For more information on the NameNode, see <<arch.hdfs>>.
+
+[[trouble.namenode.disk]]
+=== HDFS Utilization of Tables and Regions
+
+To determine how much space HBase is using on HDFS use the `hadoop` shell commands from the NameNode.
+For example...
+
+
+[source,bourne]
+----
+hadoop fs -dus /hbase/
+----
+...returns the summarized disk utilization for all HBase objects.
+
+
+[source,bourne]
+----
+hadoop fs -dus /hbase/myTable
+----
+...returns the summarized disk utilization for the HBase table 'myTable'.
+
+
+[source,bourne]
+----
+hadoop fs -du /hbase/myTable
+----
+...returns a list of the regions under the HBase table 'myTable' and their disk utilization.
+
+For more information on HDFS shell commands, see the link:http://hadoop.apache.org/common/docs/current/file_system_shell.html[HDFS FileSystem Shell documentation].
+
+[[trouble.namenode.hbase.objects]]
+=== Browsing HDFS for HBase Objects
+
+Sometimes it will be necessary to explore the HBase objects that exist on HDFS.
+These objects could include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc.
+The easiest way to do this is with the NameNode web application that runs on port 50070.
+The NameNode web application will provide links to the all the DataNodes in the cluster so that they can be browsed seamlessly.
+
+The HDFS directory structure of HBase tables in the cluster is...
+[source]
+----
+
+/hbase
+ /<Table> (Tables in the cluster)
+ /<Region> (Regions for the table)
+ /<ColumnFamily> (ColumnFamilies for the Region for the table)
+ /<StoreFile> (StoreFiles for the ColumnFamily for the Regions for the table)
+----
+
+The HDFS directory structure of HBase WAL is..
+[source]
+----
+
+/hbase
+ /.logs
+ /<RegionServer> (RegionServers)
+ /<WAL> (WAL files for the RegionServer)
+----
+
+See the link:http://hadoop.apache.org/common/docs/current/hdfs_user_guide.html[HDFS User Guide] for other non-shell diagnostic utilities like `fsck`.
+
+[[trouble.namenode.0size.hlogs]]
+==== Zero size WALs with data in them
+
+Problem: when getting a listing of all the files in a RegionServer's _.logs_ directory, one file has a size of 0 but it contains data.
+
+Answer: It's an HDFS quirk.
+A file that's currently being written to will appear to have a size of 0 but once it's closed it will show its true size
+
+[[trouble.namenode.uncompaction]]
+==== Use Cases
+
+Two common use-cases for querying HDFS for HBase objects is research the degree of uncompaction of a table.
+If there are a large number of StoreFiles for each ColumnFamily it could indicate the need for a major compaction.
+Additionally, after a major compaction if the resulting StoreFile is "small" it could indicate the need for a reduction of ColumnFamilies for the table.
+
+[[trouble.network]]
+== Network
+
+[[trouble.network.spikes]]
+=== Network Spikes
+
+If you are seeing periodic network spikes you might want to check the `compactionQueues` to see if major compactions are happening.
+
+See <<managed.compactions>> for more information on managing compactions.
+
+[[trouble.network.loopback]]
+=== Loopback IP
+
+HBase expects the loopback IP Address to be 127.0.0.1.
+See the Getting Started section on <<loopback.ip>>.
+
+[[trouble.network.ints]]
+=== Network Interfaces
+
+Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in <<trouble.casestudy>>.
+
+[[trouble.rs]]
+== RegionServer
+
+For more information on the RegionServers, see <<regionserver.arch>>.
+
+[[trouble.rs.startup]]
+=== Startup Errors
+
+[[trouble.rs.startup.master_no_region]]
+==== Master Starts, But RegionServers Do Not
+
+The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost and resolves to the master's own localhost.
+
+The RegionServers are erroneously informing the Master that their IP addresses are 127.0.0.1.
+
+Modify _/etc/hosts_ on the region servers, from...
+
+[source]
+----
+# Do not remove the following line, or various programs
+# that require network functionality will fail.
+127.0.0.1 fully.qualified.regionservername regionservername localhost.localdomain localhost
+::1 localhost6.localdomain6 localhost6
+----
+
+\... to (removing the master node's name from localhost)...
+
+[source]
+----
+# Do not remove the following line, or various programs
+# that require network functionality will fail.
+127.0.0.1 localhost.localdomain localhost
+::1 localhost6.localdomain6 localhost6
+----
+
+[[trouble.rs.startup.compression]]
+==== Compression Link Errors
+
+Since compression algorithms such as LZO need to be installed and configured on each cluster this is a frequent source of startup error.
+If you see messages like this...
+
+[source]
+----
+
+11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
+java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
+ at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
+ at java.lang.Runtime.loadLibrary0(Runtime.java:823)
+ at java.lang.System.loadLibrary(System.java:1028)
+----
+
+\... then there is a path issue with the compression libraries.
+See the Configuration section on link:[LZO compression configuration].
+
+[[trouble.rs.runtime]]
+=== Runtime Errors
+
+[[trouble.rs.runtime.hang]]
+==== RegionServer Hanging
+
+Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump, does it look like threads are BLOCKED but no one holds the lock all are blocked on? See link:https://issues.apache.org/jira/browse/HBASE-3622[HBASE 3622 Deadlock in
+ HBaseServer (JVM bug?)].
+Adding `-XX:+UseMembar` to the HBase `HBASE_OPTS` in _conf/hbase-env.sh_ may fix it.
+
+[[trouble.rs.runtime.filehandles]]
+==== java.io.IOException...(Too many open files)
+
+If you see log messages like this...
+
+[source]
+----
+
+2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode:
+Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files
+ at java.io.UnixFileSystem.createFileExclusively(Native Method)
+ at java.io.File.createNewFile(File.java:883)
+----
+
+\... see the Getting Started section on link:[ulimit and nproc configuration].
+
+[[trouble.rs.runtime.xceivers]]
+==== xceiverCount 258 exceeds the limit of concurrent xcievers 256
+
+This typically shows up in the DataNode logs.
+
+See the Getting Started section on link:[xceivers configuration].
+
+[[trouble.rs.runtime.oom_nt]]
+==== System instability, and the presence of "java.lang.OutOfMemoryError: unable to createnew native thread in exceptions" HDFS DataNode logs or that of any system daemon
+
+See the Getting Started section on ulimit and nproc configuration.
+The default on recent Linux distributions is 1024 - which is far too low for HBase.
+
+[[trouble.rs.runtime.gc]]
+==== DFS instability and/or RegionServer lease timeouts
+
+If you see warning messages like this...
+
+[source]
+----
+
+2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000
+2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000
+2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying
+----
+
+\... or see full GC compactions then you may be experiencing full GC's.
+
+[[trouble.rs.runtime.nolivenodes]]
+==== "No live nodes contain current block" and/or YouAreDeadException
+
+These errors can happen either when running out of OS file handles or in periods of severe network problems where the nodes are unreachable.
+
+See the Getting Started section on ulimit and nproc configuration and check your network.
+
+[[trouble.rs.runtime.zkexpired]]
+==== ZooKeeper SessionExpired events
+
+Master or RegionServers shutting down with messages like those in the logs:
+
+[source]
+----
+
+WARN org.apache.zookeeper.ClientCnxn: Exception
+closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
+java.io.IOException: TIMED OUT
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
+WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000
+INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
+INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
+INFO org.apache.zookeeper.ClientCnxn: Server connection successful
+WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e
+java.io.IOException: Session Expired
+ at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589)
+ at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
+ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired
+----
+
+The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the world"). Since the RegionServer's local ZooKeeper client cannot send heartbeats, the session times out.
+By design, we shut down any node that isn't able to contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that may already be assigned elsewhere.
+
+* Make sure you give plenty of RAM (in _hbase-env.sh_), the default of 1GB won't be able to sustain long running imports.
+* Make sure you don't swap, the JVM never behaves well under swapping.
+* Make sure you are not CPU starving the RegionServer thread.
+ For example, if you are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably starving the RegionServer enough to create longer garbage collection pauses.
+* Increase the ZooKeeper session timeout
+
+If you wish to increase the session timeout, add the following to your _hbase-site.xml_ to increase the timeout from the default of 60 seconds to 120 seconds.
+
+[source,xml]
+----
+<property>
+ <name>zookeeper.session.timeout</name>
+ <value>1200000</value>
+</property>
+<property>
+ <name>hbase.zookeeper.property.tickTime</name>
+ <value>6000</value>
+</property>
+----
+
+Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least that amount of time to be transferred to another RegionServer.
+For a production system serving live requests, we would instead recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having less garbage to collect per machine).
+
+If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading.
+
+See <<trouble.zookeeper.general>> for other general information about ZooKeeper troubleshooting.
+
+[[trouble.rs.runtime.notservingregion]]
+==== NotServingRegionException
+
+This exception is "normal" when found in the RegionServer logs at DEBUG level.
+This exception is returned back to the client and then the client goes back to `hbase:meta` to find the new location of the moved region.
+
+However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong.
+
+[[trouble.rs.runtime.double_listed_regions]]
+==== Regions listed by domain name, then IP
+
+Fix your DNS.
+In versions of Apache HBase before 0.92.x, reverse DNS needs to give same answer as forward lookup.
+See link:https://issues.apache.org/jira/browse/HBASE-3431[HBASE 3431 RegionServer is not using the name given it by the master; double entry in master listing of servers] for gorey details.
+
+[[brand.new.compressor]]
+==== Logs flooded with '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Gotbrand-new compressor' messages
+
+We are not using the native versions of compression libraries.
+See link:https://issues.apache.org/jira/browse/HBASE-1900[HBASE-1900 Put back native support when hadoop 0.21 is released].
+Copy the native libs from hadoop under HBase lib dir or symlink them into place and the message should go away.
+
+[[trouble.rs.runtime.client_went_away]]
+==== Server handler X on 60020 caught: java.nio.channels.ClosedChannelException
+
+If you see this type of message it means that the region server was trying to read/send data from/to a client but it already went away.
+Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce job is killed or fails) or if the client receives a SocketTimeoutException.
+It's harmless, but you should consider digging in a bit more if you aren't doing something to trigger them.
+
+=== Snapshot Errors Due to Reverse DNS
+
+Several operations within HBase, including snapshots, rely on properly configured reverse DNS.
+Some environments, such as Amazon EC2, have trouble with reverse DNS.
+If you see errors like the following on your RegionServers, check your reverse DNS configuration:
+
+----
+
+2013-05-01 00:04:56,356 DEBUG org.apache.hadoop.hbase.procedure.Subprocedure: Subprocedure 'backup1'
+coordinator notified of 'acquire', waiting on 'reached' or 'abort' from coordinator.
+----
+
+In general, the hostname reported by the RegionServer needs to be the same as the hostname the Master is trying to reach.
+You can see a hostname mismatch by looking for the following type of message in the RegionServer's logs at start-up.
+
+----
+
+2013-05-01 00:03:00,614 INFO org.apache.hadoop.hbase.regionserver.HRegionServer: Master passed us hostname
+to use. Was=myhost-1234, Now=ip-10-55-88-99.ec2.internal
+----
+
+[[trouble.rs.shutdown]]
+=== Shutdown Errors
+
+
+
+[[trouble.master]]
+== Master
+
+For more information on the Master, see <<master,master>>.
+
+[[trouble.master.startup]]
+=== Startup Errors
+
+[[trouble.master.startup.migration]]
+==== Master says that you need to run the HBase migrations script
+
+Upon running that, the HBase migrations script says no files in root directory.
+
+HBase expects the root directory to either not exist, or to have already been initialized by HBase running a previous time.
+If you create a new directory for HBase using Hadoop DFS, this error will occur.
+Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase.
+Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself.
+
+[[trouble.master.startup.zk.buffer]]
+==== Packet len6080218 is out of range!
+
+If you have many regions on your cluster and you see an error like that reported above in this sections title in your logs, see link:https://issues.apache.org/jira/browse/HBASE-4246[HBASE-4246 Cluster with too many regions cannot withstand some master failover scenarios].
+
+[[trouble.master.shutdown]]
+=== Shutdown Errors
+
+
+
+[[trouble.zookeeper]]
+== ZooKeeper
+
+[[trouble.zookeeper.startup]]
+=== Startup Errors
+
+[[trouble.zookeeper.startup.address]]
+==== Could not find my address: xyz in list of ZooKeeper quorum servers
+
+A ZooKeeper server wasn't able to start, throws that error.
+xyz is the name of your server.
+
+This is a name lookup problem.
+HBase tries to start a ZooKeeper server on some machine but that machine isn't able to find itself in the `hbase.zookeeper.quorum` configuration.
+
+Use the hostname presented in the error message instead of the value you used.
+If you have a DNS server, you can set `hbase.zookeeper.dns.interface` and `hbase.zookeeper.dns.nameserver` in _hbase-site.xml_ to make sure it resolves to the correct FQDN.
+
+[[trouble.zookeeper.general]]
+=== ZooKeeper, The Cluster Canary
+
+ZooKeeper is the cluster's "canary in the mineshaft". It'll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster.
+
+See the link:http://wiki.apache.org/hadoop/ZooKeeper/Troubleshooting[ZooKeeper Operating Environment Troubleshooting] page.
+It has suggestions and tools for checking disk and networking performance; i.e.
+the operating environment your ZooKeeper and HBase are running in.
+
+Additionally, the utility <<trouble.tools.builtin.zkcli>> may help investigate ZooKeeper issues.
+
+[[trouble.ec2]]
+== Amazon EC2
+
+[[trouble.ec2.zookeeper]]
+=== ZooKeeper does not seem to work on Amazon EC2
+
+HBase does not start when deployed as Amazon EC2 instances.
+Exceptions like the below appear in the Master and/or RegionServer logs:
+
+[source]
+----
+
+ 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
+ connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
+ 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
+ closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
+ java.net.ConnectException: Connection refused
+----
+
+Security group policy is blocking the ZooKeeper port on a public address.
+Use the internal EC2 host names when configuring the ZooKeeper quorum peer list.
+
+[[trouble.ec2.instability]]
+=== Instability on Amazon EC2
+
+Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list.
+Search for old threads using link:http://search-hadoop.com/[Search Hadoop]
+
+[[trouble.ec2.connection]]
+=== Remote Java Connection into EC2 Cluster Not Working
+
+See Andrew's answer here, up on the user list: link:http://search-hadoop.com/m/sPdqNFAwyg2[Remote Java client connection into EC2 instance].
+
+[[trouble.versions]]
+== HBase and Hadoop version issues
+
+[[trouble.versions.205]]
+=== `NoClassDefFoundError` when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x)
+
+Apache HBase 0.90.x does not ship with hadoop-0.20.205.x, etc.
+To make it run, you need to replace the hadoop jars that Apache HBase shipped with in its _lib_ directory with those of the Hadoop you want to run HBase on.
+If even after replacing Hadoop jars you get the below exception:
+
+[source]
+----
+
+sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration
+sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<init>(DefaultMetricsSystem.java:37)
+sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<clinit>(DefaultMetricsSystem.java:34)
+sv4r6s38: at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229)
+sv4r6s38: at org.apache.hadoop.security.KerberosName.<clinit>(KerberosName.java:83)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
+----
+
+you need to copy under _hbase/lib_, the _commons-configuration-X.jar_ you find in your Hadoop's _lib_ directory.
+That should fix the above complaint.
+
+[[trouble.wrong.version]]
+=== ...cannot communicate with client version...
+
+If you see something like the following in your logs [computeroutput]+... 2012-09-24
+ 10:20:52,168 FATAL org.apache.hadoop.hbase.master.HMaster: Unhandled exception. Starting
+ shutdown. org.apache.hadoop.ipc.RemoteException: Server IPC version 7 cannot communicate
+ with client version 4 ...+ ...are you trying to talk to an Hadoop 2.0.x from an HBase that has an Hadoop 1.0.x client? Use the HBase built against Hadoop 2.0 or rebuild your HBase passing the +-Dhadoop.profile=2.0+ attribute to Maven (See <<maven.build.hadoop>> for more).
+
+== IPC Configuration Conflicts with Hadoop
+
+If the Hadoop configuration is loaded after the HBase configuration, and you have configured custom IPC settings in both HBase and Hadoop, the Hadoop values may overwrite the HBase values.
+There is normally no need to change these settings for HBase, so this problem is an edge case.
+However, link:https://issues.apache.org/jira/browse/HBASE-11492[HBASE-11492] renames these settings for HBase to remove the chance of a conflict.
+Each of the setting names have been prefixed with `hbase.`, as shown in the following table.
+No action is required related to these changes unless you are already experiencing a conflict.
+
+These changes were backported to HBase 0.98.x and apply to all newer versions.
+
+[cols="1,1", options="header"]
+|===
+| Pre-0.98.x
+| 0.98-x And Newer
+
+| ipc.server.listen.queue.size
+| hbase.ipc.server.listen.queue.size
+
+| ipc.server.max.callqueue.size
+| hbase.ipc.server.max.callqueue.size
+
+| ipc.server.callqueue.handler.factor
+| hbase.ipc.server.callqueue.handler.factor
+
+| ipc.server.callqueue.read.share
+| hbase.ipc.server.callqueue.read.share
+
+| ipc.server.callqueue.type
+| hbase.ipc.server.callqueue.type
+
+| ipc.server.queue.max.call.delay
+| hbase.ipc.server.queue.max.call.delay
+
+| ipc.server.max.callqueue.length
+| hbase.ipc.server.max.callqueue.length
+
+| ipc.server.read.threadpool.size
+| hbase.ipc.server.read.threadpool.size
+
+| ipc.server.tcpkeepalive
+| hbase.ipc.server.tcpkeepalive
+
+| ipc.server.tcpnodelay
+| hbase.ipc.server.tcpnodelay
+
+| ipc.client.call.purge.timeout
+| hbase.ipc.client.call.purge.timeout
+
+| ipc.client.connection.maxidletime
+| hbase.ipc.client.connection.maxidletime
+
+| ipc.client.idlethreshold
+| hbase.ipc.client.idlethreshold
+
+| ipc.client.kill.max
+| hbase.ipc.client.kill.max
+
+| ipc.server.scan.vtime.weight
+| hbase.ipc.server.scan.vtime.weight
+|===
+
+== HBase and HDFS
+
+General configuration guidance for Apache HDFS is out of the scope of this guide.
+Refer to the documentation available at http://hadoop.apache.org/ for extensive information about configuring HDFS.
+This section deals with HDFS in terms of HBase.
+
+In most cases, HBase stores its data in Apache HDFS.
+This includes the HFiles containing the data, as well as the write-ahead logs (WALs) which store data before it is written to the HFiles and protect against RegionServer crashes.
+HDFS provides reliability and protection to data in HBase because it is distributed.
+To operate with the most efficiency, HBase needs data to be available locally.
+Therefore, it is a good practice to run an HDFS DataNode on each RegionServer.
+
+.Important Information and Guidelines for HBase and HDFS
+
+HBase is a client of HDFS.::
+ HBase is an HDFS client, using the HDFS `DFSClient` class, and references to this class appear in HBase logs with other HDFS client log messages.
+
+Configuration is necessary in multiple places.::
+ Some HDFS configurations relating to HBase need to be done at the HDFS (server) side.
+ Others must be done within HBase (at the client side). Other settings need to be set at both the server and client side.
+
+Write errors which affect HBase may be logged in the HDFS logs rather than HBase logs.::
+ When writing, HDFS pipelines communications from one DataNode to another.
+ HBase communicates to both the HDFS NameNode and DataNode, using the HDFS client classes.
+ Communication problems between DataNodes are logged in the HDFS logs, not the HBase logs.
+
+HBase communicates with HDFS using two different ports.::
+ HBase communicates with DataNodes using the `ipc.Client` interface and the `DataNode` class.
+ References to these will appear in HBase logs.
+ Each of these communication channels use a different port (50010 and 50020 by default). The ports are configured in the HDFS configuration, via the `dfs.datanode.address` and `dfs.datanode.ipc.address` parameters.
+
+Errors may be logged in HBase, HDFS, or both.::
+ When troubleshooting HDFS issues in HBase, check logs in both places for errors.
+
+HDFS takes a while to mark a node as dead. You can configure HDFS to avoid using stale DataNodes.::
+ By default, HDFS does not mark a node as dead until it is unreachable for 630 seconds.
+ In Hadoop 1.1 and Hadoop 2.x, this can be alleviated by enabling checks for stale DataNodes, though this check is disabled by default.
+ You can enable the check for reads and writes separately, via `dfs.namenode.avoid.read.stale.datanode` and `dfs.namenode.avoid.write.stale.datanode settings`.
+ A stale DataNode is one that has not been reachable for `dfs.namenode.stale.datanode.interval` (default is 30 seconds). Stale datanodes are avoided, and marked as the last possible target for a read or write operation.
+ For configuration details, see the HDFS documentation.
+
+Settings for HDFS retries and timeouts are important to HBase.::
+ You can configure settings for various retries and timeouts.
+ Always refer to the HDFS documentation for current recommendations and defaults.
+ Some of the settings important to HBase are listed here.
+ Defaults are current as of Hadoop 2.3.
+ Check the Hadoop documentation for the most current values and recommendations.
+
+.Connection Timeouts
+Connection timeouts occur between the client (HBASE) and the HDFS DataNode.
+They may occur when establishing a connection, attempting to read, or attempting to write.
+The two settings below are used in combination, and affect connections between the DFSClient and the DataNode, the ipc.cClient and the DataNode, and communication between two DataNodes.
+
+`dfs.client.socket-timeout` (default: 60000)::
+ The amount of time before a client connection times out when establishing a connection or reading.
+ The value is expressed in milliseconds, so the default is 60 seconds.
+
+`dfs.datanode.socket.write.timeout` (default: 480000)::
+ The amount of time before a write operation times out.
+ The default is 8 minutes, expressed as milliseconds.
+
+.Typical Error Logs
+The following types of errors are often seen in the logs.
+
+`INFO HDFS.DFSClient: Failed to connect to /xxx50010, add to deadNodes and
+ continue java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel
+ to be ready for connect. ch : java.nio.channels.SocketChannel[connection-pending
+ remote=/region-server-1:50010]`::
+ All DataNodes for a block are dead, and recovery is not possible.
+ Here is the sequence of events that leads to this error:
+
+`INFO org.apache.hadoop.HDFS.DFSClient: Exception in createBlockOutputStream
+ java.net.SocketTimeoutException: 69000 millis timeout while waiting for channel to be
+ ready for connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/
+ xxx:50010]`::
+ This type of error indicates a write issue.
+ In this case, the master wants to split the log.
+ It does not have a local DataNodes so it tries to connect to a remote DataNode, but the DataNode is dead.
+
+[[trouble.tests]]
+== Running unit or integration tests
+
+[[trouble.hdfs_2556]]
+=== Runtime exceptions from MiniDFSCluster when running tests
+
+If you see something like the following
+
+[source]
+----
+...
+java.lang.NullPointerException: null
+at org.apache.hadoop.hdfs.MiniDFSCluster.startDataNodes
+at org.apache.hadoop.hdfs.MiniDFSCluster.<init>
+at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
+at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniDFSCluster
+at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
+...
+----
+
+or
+
+[source]
+----
+...
+java.io.IOException: Shutting down
+at org.apache.hadoop.hbase.MiniHBaseCluster.init
+at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
+at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
+at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniHBaseCluster
+at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
+...
+----
+
+\... then try issuing the command +umask 022+ before launching tests.
+This is a workaround for link:https://issues.apache.org/jira/browse/HDFS-2556[HDFS-2556]
+
+[[trouble.casestudy]]
+== Case Studies
+
+For Performance and Troubleshooting Case Studies, see <<casestudies>>.
+
+[[trouble.crypto]]
+== Cryptographic Features
+
+[[trouble.crypto.hbase_10132]]
+=== sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD
+
+This problem manifests as exceptions ultimately caused by:
+
+[source]
+----
+Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD
+ at sun.security.pkcs11.wrapper.PKCS11.C_DecryptUpdate(Native Method)
+ at sun.security.pkcs11.P11Cipher.implDoFinal(P11Cipher.java:795)
+----
+
+This problem appears to affect some versions of OpenJDK 7 shipped by some Linux vendors.
+NSS is configured as the default provider.
+If the host has an x86_64 architecture, depending on if the vendor packages contain the defect, the NSS provider will not function correctly.
+
+To work around this problem, find the JRE home directory and edit the file _lib/security/java.security_.
+Edit the file to comment out the line:
+
+[source]
+----
+security.provider.1=sun.security.pkcs11.SunPKCS11 ${java.home}/lib/security/nss.cfg
+----
+
+Then renumber the remaining providers accordingly.
+
+== Operating System Specific Issues
+
+=== Page Allocation Failure
+
+NOTE: This issue is known to affect CentOS 6.2 and possibly CentOS 6.5.
+It may also affect some versions of Red Hat Enterprise Linux, according to https://bugzilla.redhat.com/show_bug.cgi?id=770545.
+
+Some users have reported seeing the following error:
+
+----
+kernel: java: page allocation failure. order:4, mode:0x20
+----
+
+Raising the value of `min_free_kbytes` was reported to fix this problem.
+This parameter is set to a percentage of the amount of RAM on your system, and is described in more detail at http://www.centos.org/docs/5/html/5.1/Deployment_Guide/s3-proc-sys-vm.html.
+
+To find the current value on your system, run the following command:
+
+----
+[user@host]# cat /proc/sys/vm/min_free_kbytes
+----
+
+Next, raise the value.
+Try doubling, then quadrupling the value.
+Note that setting the value too low or too high could have detrimental effects on your system.
+Consult your operating system vendor for specific recommendations.
+
+Use the following command to modify the value of `min_free_kbytes`, substituting _<value>_ with your intended value:
+
+----
+[user@host]# echo <value> > /proc/sys/vm/min_free_kbytes
+----
+
+== JDK Issues
+
+=== NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet
+
+If you see this in your logs:
+[source]
+----
+Caused by: java.lang.NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet()Ljava/util/concurrent/ConcurrentHashMap$KeySetView;
+ at org.apache.hadoop.hbase.master.ServerManager.findServerWithSameHostnamePortWithLock(ServerManager.java:393)
+ at org.apache.hadoop.hbase.master.ServerManager.checkAndRecordNewServer(ServerManager.java:307)
+ at org.apache.hadoop.hbase.master.ServerManager.regionServerStartup(ServerManager.java:244)
+ at org.apache.hadoop.hbase.master.MasterRpcServices.regionServerStartup(MasterRpcServices.java:304)
+ at org.apache.hadoop.hbase.protobuf.generated.RegionServerStatusProtos$RegionServerStatusService$2.callBlockingMethod(RegionServerStatusProtos.java:7910)
+ at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2020)
+ ... 4 more
+----
+then check if you compiled with jdk8 and tried to run it on jdk7.
+If so, this won't work.
+Run on jdk8 or recompile with jdk7.
+See link:https://issues.apache.org/jira/browse/HBASE-10607[HBASE-10607 JDK8 NoSuchMethodError involving ConcurrentHashMap.keySet if running on JRE 7].