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Posted to common-commits@hadoop.apache.org by wh...@apache.org on 2015/03/11 22:31:21 UTC
[02/12] hadoop git commit: HADOOP-11633. Convert remaining branch-2
.apt.vm files to markdown. Contributed by Masatake Iwasaki.
http://git-wip-us.apache.org/repos/asf/hadoop/blob/245f7b2a/hadoop-tools/hadoop-openstack/src/site/apt/index.apt.vm
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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. See accompanying LICENSE file.
-
- ---
- Hadoop OpenStack Support: Swift Object Store
- ---
- ---
- ${maven.build.timestamp}
-
-%{toc|section=1|fromDepth=0}
-
-Hadoop OpenStack Support: Swift Object Store
-
-* {Introduction}
-
- {{{http://www.openstack.org/}OpenStack}} is an open source cloud infrastructure
- which can be accessed
- from multiple public IaaS providers, and deployed privately. It offers
- infrastructure services such as VM hosting (Nova), authentication (Keystone)
- and storage of binary objects (Swift).
-
- This module enables Apache Hadoop applications -including MapReduce jobs,
- read and write data to and from instances of the
- {{{http://www.openstack.org/software/openstack-storage/}OpenStack Swift object store}}.
-
-* Features
-
- * Read and write of data stored in a Swift object store
-
- * Support of a pseudo-hierachical file system (directories, subdirectories and
- files)
-
- * Standard filesystem operations: <<<create>>>, <<<delete>>>, <<<mkdir>>>,
- <<<ls>>>, <<<mv>>>, <<<stat>>>.
-
- * Can act as a source of data in a MapReduce job, or a sink.
-
- * Support for multiple OpenStack services, and multiple containers from a
- single service.
-
- * Supports in-cluster and remote access to Swift data.
-
- * Supports OpenStack Keystone authentication with password or token.
-
- * Released under the Apache Software License
-
- * Tested against the Hadoop 3.x and 1.x branches, against multiple public
- OpenStack clusters: Rackspace US, Rackspace UK, HP Cloud.
-
- * Tested against private OpenStack clusters, including scalability tests of
- large file uploads.
-
-* Using the Hadoop Swift Filesystem Client
-
-** Concepts: services and containers
-
- OpenStack swift is an <Object Store>; also known as a <blobstore>. It stores
- arbitrary binary objects by name in a <container>.
-
- The Hadoop Swift filesystem library adds another concept, the <service>, which
- defines which Swift blobstore hosts a container -and how to connect to it.
-
-** Containers and Objects
-
- * Containers are created by users with accounts on the Swift filestore, and hold
- <objects>.
-
- * Objects can be zero bytes long, or they can contain data.
-
- * Objects in the container can be up to 5GB; there is a special support for
- larger files than this, which merges multiple objects in to one.
-
- * Each object is referenced by it's <name>; there is no notion of directories.
-
- * You can use any characters in an object name that can be 'URL-encoded'; the
- maximum length of a name is 1034 characters -after URL encoding.
-
- * Names can have <<</>>> characters in them, which are used to create the illusion of
- a directory structure. For example <<<dir/dir2/name>>>. Even though this looks
- like a directory, <it is still just a name>. There is no requirement to have
- any entries in the container called <<<dir>>> or <<<dir/dir2>>>
-
- * That said. if the container has zero-byte objects that look like directory
- names above other objects, they can pretend to be directories. Continuing the
- example, a 0-byte object called <<<dir>>> would tell clients that it is a
- directory while <<<dir/dir2>>> or <<<dir/dir2/name>>> were present. This creates an
- illusion of containers holding a filesystem.
-
- Client applications talk to Swift over HTTP or HTTPS, reading, writing and
- deleting objects using standard HTTP operations (GET, PUT and DELETE,
- respectively). There is also a COPY operation, that creates a new object in the
- container, with a new name, containing the old data. There is no rename
- operation itself, objects need to be copied -then the original entry deleted.
-
-** Eventual Consistency
-
- The Swift Filesystem is *eventually consistent*: an operation on an object may
- not be immediately visible to that client, or other clients. This is a
- consequence of the goal of the filesystem: to span a set of machines, across
- multiple datacenters, in such a way that the data can still be available when
- many of them fail. (In contrast, the Hadoop HDFS filesystem is *immediately
- consistent*, but it does not span datacenters.)
-
- Eventual consistency can cause surprises for client applications that expect
- immediate consistency: after an object is deleted or overwritten, the object
- may still be visible -or the old data still retrievable. The Swift Filesystem
- client for Apache Hadoop attempts to handle this, in conjunction with the
- MapReduce engine, but there may be still be occasions when eventual consistency
- causes surprises.
-
-** Non-atomic "directory" operations.
-
- Hadoop expects some
- operations to be atomic, especially <<<rename()>>>, which is something
- the MapReduce layer relies on to commit the output of a job, renaming data
- from a temp directory to the final path. Because a rename
- is implemented as a copy of every blob under the directory's path, followed
- by a delete of the originals, the intermediate state of the operation
- will be visible to other clients. If two Reducer tasks to rename their temp
- directory to the final path, both operations may succeed, with the result that
- output directory contains mixed data. This can happen if MapReduce jobs
- are being run with <speculation> enabled and Swift used as the direct output
- of the MR job (it can also happen against Amazon S3).
-
- Other consequences of the non-atomic operations are:
-
- 1. If a program is looking for the presence of the directory before acting
- on the data -it may start prematurely. This can be avoided by using
- other mechanisms to co-ordinate the programs, such as the presence of a file
- that is written <after> any bulk directory operations.
-
- 2. A <<<rename()>>> or <<<delete()>>> operation may include files added under
- the source directory tree during the operation, may unintentionally delete
- it, or delete the 0-byte swift entries that mimic directories and act
- as parents for the files. Try to avoid doing this.
-
- The best ways to avoid all these problems is not using Swift as
- the filesystem between MapReduce jobs or other Hadoop workflows. It
- can act as a source of data, and a final destination, but it doesn't meet
- all of Hadoop's expectations of what a filesystem is -it's a <blobstore>.
-
-* Working with Swift Object Stores in Hadoop
-
- Once installed, the Swift FileSystem client can be used by any Hadoop application
- to read from or write to data stored in a Swift container.
-
- Data stored in Swift can be used as the direct input to a MapReduce job
- -simply use the <<<swift:>>> URL (see below) to declare the source of the data.
-
- This Swift Filesystem client is designed to work with multiple
- Swift object stores, both public and private. This allows the client to work
- with different clusters, reading and writing data to and from either of them.
-
- It can also work with the same object stores using multiple login details.
-
- These features are achieved by one basic concept: using a service name in
- the URI referring to a swift filesystem, and looking up all the connection and
- login details for that specific service. Different service names can be defined
- in the Hadoop XML configuration file, so defining different clusters, or
- providing different login details for the same object store(s).
-
-
-** Swift Filesystem URIs
-
- Hadoop uses URIs to refer to files within a filesystem. Some common examples
- are:
-
-+--
- local://etc/hosts
- hdfs://cluster1/users/example/data/set1
- hdfs://cluster2.example.org:8020/users/example/data/set1
-+--
-
- The Swift Filesystem Client adds a new URL type <<<swift>>>. In a Swift Filesystem
- URL, the hostname part of a URL identifies the container and the service to
- work with; the path the name of the object. Here are some examples
-
-+--
- swift://container.rackspace/my-object.csv
- swift://data.hpcloud/data/set1
- swift://dmitry.privatecloud/out/results
-+--
-
- In the last two examples, the paths look like directories: it is not, they are
- simply the objects named <<<data/set1>>> and <<<out/results>>> respectively.
-
-** Installing
-
- The <<<hadoop-openstack>>> JAR must be on the classpath of the Hadoop program trying to
- talk to the Swift service. If installed in the classpath of the Hadoop
- MapReduce service, then all programs started by the MR engine will pick up the
- JAR automatically. This is the easiest way to give all Hadoop jobs access to
- Swift.
-
- Alternatively, the JAR can be included as one of the JAR files that an
- application uses. This lets the Hadoop jobs work with a Swift object store even
- if the Hadoop cluster is not pre-configured for this.
-
- The library also depends upon the Apache HttpComponents library, which
- must also be on the classpath.
-
-** Configuring
-
- To talk to a swift service, the user must must provide:
-
- [[1]] The URL defining the container and the service.
-
- [[1]] In the cluster/job configuration, the login details of that service.
-
- Multiple service definitions can co-exist in the same configuration file: just
- use different names for them.
-
-*** Example: Rackspace US, in-cluster access using API key
-
- This service definition is for use in a Hadoop cluster deployed within Rackspace's
- US infrastructure.
-
-+--
- <property>
- <name>fs.swift.service.rackspace.auth.url</name>
- <value>https://auth.api.rackspacecloud.com/v2.0/tokens</value>
- <description>Rackspace US (multiregion)</description>
- </property>
-
- <property>
- <name>fs.swift.service.rackspace.username</name>
- <value>user4</value>
- </property>
-
- <property>
- <name>fs.swift.service.rackspace.region</name>
- <value>DFW</value>
- </property>
-
- <property>
- <name>fs.swift.service.rackspace.apikey</name>
- <value>fe806aa86dfffe2f6ed8</value>
- </property>
-+--
-
- Here the API key visible in the account settings API keys page is used to log
- in. No property for public/private access -the default is to use the private
- endpoint for Swift operations.
-
- This configuration also selects one of the regions, DFW, for its data.
-
- A reference to this service would use the <<<rackspace>>> service name:
-
----
- swift://hadoop-container.rackspace/
----
-
-*** Example: Rackspace UK: remote access with password authentication
-
- This connects to Rackspace's UK ("LON") datacenter.
-
-+--
- <property>
- <name>fs.swift.service.rackspaceuk.auth.url</name>
- <value>https://lon.identity.api.rackspacecloud.com/v2.0/tokens</value>
- <description>Rackspace UK</description>
- </property>
-
- <property>
- <name>fs.swift.service.rackspaceuk.username</name>
- <value>user4</value>
- </property>
-
- <property>
- <name>fs.swift.service.rackspaceuk.password</name>
- <value>insert-password-here/value>
- </property>
-
- <property>
- <name>fs.swift.service.rackspace.public</name>
- <value>true</value>
- </property>
-+--
-
- This is a public access point connection, using a password over an API key.
-
- A reference to this service would use the <<<rackspaceuk>>> service name:
-
-+--
- swift://hadoop-container.rackspaceuk/
-+--
-
- Because the public endpoint is used, if this service definition is used within
- the London datacenter, all accesses will be billed at the public
- upload/download rates, <irrespective of where the Hadoop cluster is>.
-
-*** Example: HP cloud service definition
-
- Here is an example that connects to the HP Cloud object store.
-
-+--
- <property>
- <name>fs.swift.service.hpcloud.auth.url</name>
- <value>https://region-a.geo-1.identity.hpcloudsvc.com:35357/v2.0/tokens
- </value>
- <description>HP Cloud</description>
- </property>
-
- <property>
- <name>fs.swift.service.hpcloud.tenant</name>
- <value>FE806AA86</value>
- </property>
-
- <property>
- <name>fs.swift.service.hpcloud.username</name>
- <value>FE806AA86DFFFE2F6ED8</value>
- </property>
-
- <property>
- <name>fs.swift.service.hpcloud.password</name>
- <value>secret-password-goes-here</value>
- </property>
-
- <property>
- <name>fs.swift.service.hpcloud.public</name>
- <value>true</value>
- </property>
-+--
-
- A reference to this service would use the <<<hpcloud>>> service name:
-
-+--
- swift://hadoop-container.hpcloud/
-+--
-
-** General Swift Filesystem configuration options
-
- Some configuration options apply to the Swift client, independent of
- the specific Swift filesystem chosen.
-
-*** Blocksize fs.swift.blocksize
-
- Swift does not break up files into blocks, except in the special case of files
- over 5GB in length. Accordingly, there isn't a notion of a "block size"
- to define where the data is kept.
-
- Hadoop's MapReduce layer depends on files declaring their block size,
- so that it knows how to partition work. Too small a blocksize means that
- many mappers work on small pieces of data; too large a block size means
- that only a few mappers get started.
-
- The block size value reported by Swift, therefore, controls the basic workload
- partioning of the MapReduce engine -and can be an important parameter to
- tune for performance of the cluster.
-
- The property has a unit of kilobytes; the default value is <<<32*1024>>>: 32 MB
-
-+--
- <property>
- <name>fs.swift.blocksize</name>
- <value>32768</value>
- </property>
-+--
-
- This blocksize has no influence on how files are stored in Swift; it only controls
- what the reported size of blocks are - a value used in Hadoop MapReduce to
- divide work.
-
- Note that the MapReduce engine's split logic can be tuned independently by setting
- the <<<mapred.min.split.size>>> and <<<mapred.max.split.size>>> properties,
- which can be done in specific job configurations.
-
-+--
- <property>
- <name>mapred.min.split.size</name>
- <value>524288</value>
- </property>
-
- <property>
- <name>mapred.max.split.size</name>
- <value>1048576</value>
- </property>
-+--
-
- In an Apache Pig script, these properties would be set as:
-
----
- mapred.min.split.size 524288
- mapred.max.split.size 1048576
----
-
-*** Partition size fs.swift.partsize
-
- The Swift filesystem client breaks very large files into partitioned files,
- uploading each as it progresses, and writing any remaning data and an XML
- manifest when a partitioned file is closed.
-
- The partition size defaults to 4608 MB; 4.5GB, the maximum filesize that
- Swift can support.
-
- It is possible to set a smaller partition size, in the <<<fs.swift.partsize>>>
- option. This takes a value in KB.
-
-+--
- <property>
- <name>fs.swift.partsize</name>
- <value>1024</value>
- <description>upload every MB</description>
- </property>
-+--
-
- When should this value be changed from its default?
-
- While there is no need to ever change it for basic operation of
- the Swift filesystem client, it can be tuned
-
- * If a Swift filesystem is location aware, then breaking a file up into
- smaller partitions scatters the data round the cluster. For best performance,
- the property <<<fs.swift.blocksize>>> should be set to a smaller value than the
- partition size of files.
-
- * When writing to an unpartitioned file, the entire write is done in the
- <<<close()>>> operation. When a file is partitioned, the outstanding data to
- be written whenever the outstanding amount of data is greater than the
- partition size. This means that data will be written more incrementally
-
-*** Request size fs.swift.requestsize
-
- The Swift filesystem client reads files in HTTP GET operations, asking for
- a block of data at a time.
-
- The default value is 64KB. A larger value may be more efficient over faster
- networks, as it reduces the overhead of setting up the HTTP operation.
-
- However, if the file is read with many random accesses, requests for
- data will be made from different parts of the file -discarding some of the
- previously requested data. The benefits of larger request sizes may be wasted.
-
- The property <<<fs.swift.requestsize>>> sets the request size in KB.
-
-+--
- <property>
- <name>fs.swift.requestsize</name>
- <value>128</value>
- </property>
-+--
-
-*** Connection timeout fs.swift.connect.timeout
-
- This sets the timeout in milliseconds to connect to a Swift service.
-
-+--
- <property>
- <name>fs.swift.connect.timeout</name>
- <value>15000</value>
- </property>
-+--
-
- A shorter timeout means that connection failures are raised faster -but
- may trigger more false alarms. A longer timeout is more resilient to network
- problems -and may be needed when talking to remote filesystems.
-
-*** Connection timeout fs.swift.socket.timeout
-
- This sets the timeout in milliseconds to wait for data from a connected socket.
-
-+--
- <property>
- <name>fs.swift.socket.timeout</name>
- <value>60000</value>
- </property>
-+--
-
- A shorter timeout means that connection failures are raised faster -but
- may trigger more false alarms. A longer timeout is more resilient to network
- problems -and may be needed when talking to remote filesystems.
-
-*** Connection Retry Count fs.swift.connect.retry.count
-
- This sets the number of times to try to connect to a service whenever
- an HTTP request is made.
-
-+--
- <property>
- <name>fs.swift.connect.retry.count</name>
- <value>3</value>
- </property>
-+--
-
- The more retries, the more resilient it is to transient outages -and the
- less rapid it is at detecting and reporting server connectivity problems.
-
-*** Connection Throttle Delay fs.swift.connect.throttle.delay
-
- This property adds a delay between bulk file copy and delete operations,
- to prevent requests being throttled or blocked by the remote service
-
-+--
- <property>
- <name>fs.swift.connect.throttle.delay</name>
- <value>0</value>
- </property>
-+--
-
- It is measured in milliseconds; "0" means do not add any delay.
-
- Throttling is enabled on the public endpoints of some Swift services.
- If <<<rename()>>> or <<<delete()>>> operations fail with
- <<<SwiftThrottledRequestException>>>
- exceptions, try setting this property.
-
-*** HTTP Proxy
-
- If the client can only access the Swift filesystem via a web proxy
- server, the client configuration must specify the proxy via
- the <<<fs.swift.connect.proxy.host>>> and <<<fs.swift.connect.proxy.port>>>
- properties.
-
-+--
- <property>
- <name>fs.swift.proxy.host</name>
- <value>web-proxy</value>
- </property>
-
- <property>
- <name>fs.swift.proxy.port</name>
- <value>8088</value>
- </property>
-+--
-
- If the host is declared, the proxy port must be set to a valid integer value.
-
-
-** Troubleshooting
-
-*** ClassNotFoundException
-
- The <<<hadoop-openstack>>> JAR -or any dependencies- may not be on your classpath.
-
- If it is a remote MapReduce job that is failing, make sure that the JAR is
- installed on the servers in the cluster -or that the job submission process
- uploads the JAR file to the distributed cache.
-
-*** Failure to Authenticate
-
- A <<<SwiftAuthenticationFailedException>>> is thrown when the client
- cannot authenticate with the OpenStack keystone server. This could be
- because the URL in the service definition is wrong, or because
- the supplied credentials are invalid.
-
- [[1]] Check the authentication URL through <<<curl>>> or your browser
-
- [[1]] Use a Swift client such as CyberDuck to validate your credentials
-
- [[1]] If you have included a tenant ID, try leaving it out. Similarly,
- try adding it if you had not included it.
-
- [[1]] Try switching from API key authentication to password-based authentication,
- by setting the password.
-
- [[1]] Change your credentials. As with Amazon AWS clients, some credentials
- don't seem to like going over the network.
-
-*** Timeout connecting to the Swift Service
-
- This happens if the client application is running outside an OpenStack cluster,
- where it does not have access to the private hostname/IP address for filesystem
- operations. Set the <<<public>>> flag to true -but remember to set it to false
- for use in-cluster.
-
-** Warnings
-
- [[1]] Do not share your login details with anyone, which means do not log the
- details, or check the XML configuration files into any revision control system
- to which you do not have exclusive access.
-
- [[1]] Similarly, do not use your real account details in any documentation *or any
- bug reports submitted online*
-
- [[1]] Prefer the apikey authentication over passwords as it is easier
- to revoke a key -and some service providers allow you to set
- an automatic expiry date on a key when issued.
-
- [[1]] Do not use the public service endpoint from within a public OpenStack
- cluster, as it will run up large bills.
-
- [[1]] Remember: it's not a real filesystem or hierarchical directory structure.
- Some operations (directory rename and delete) take time and are not atomic or
- isolated from other operations taking place.
-
- [[1]] Append is not supported.
-
- [[1]] Unix-style permissions are not supported. All accounts with write access to
- a repository have unlimited access; the same goes for those with read access.
-
- [[1]] In the public clouds, do not make the containers public unless you are happy
- with anyone reading your data, and are prepared to pay the costs of their
- downloads.
-
-** Limits
-
- * Maximum length of an object path: 1024 characters
-
- * Maximum size of a binary object: no absolute limit. Files > 5GB are
- partitioned into separate files in the native filesystem, and merged during
- retrieval. <Warning:> the partitioned/large file support is the
- most complex part of the Hadoop/Swift FS integration, and, along with
- authentication, the most troublesome to support.
-
-** Testing the hadoop-openstack module
-
- The <<<hadoop-openstack>>> can be remotely tested against any public
- or private cloud infrastructure which supports the OpenStack Keystone
- authentication mechanism. It can also be tested against private
- OpenStack clusters. OpenStack Development teams are strongly encouraged to test
- the Hadoop swift filesystem client against any version of Swift that they
- are developing or deploying, to stress their cluster and to identify
- bugs early.
-
- The module comes with a large suite of JUnit tests -tests that are
- only executed if the source tree includes credentials to test against a
- specific cluster.
-
- After checking out the Hadoop source tree, create the file:
-
-+--
- hadoop-tools/hadoop-openstack/src/test/resources/auth-keys.xml
-+--
-
- Into this file, insert the credentials needed to bond to the test filesystem,
- as decribed above.
-
- Next set the property <<<test.fs.swift.name>>> to the URL of a
- swift container to test against. The tests expect exclusive access
- to this container -do not keep any other data on it, or expect it
- to be preserved.
-
-+--
- <property>
- <name>test.fs.swift.name</name>
- <value>swift://test.myswift/</value>
- </property>
-+--
-
- In the base hadoop directory, run:
-
-+--
- mvn clean install -DskipTests
-+--
-
- This builds a set of Hadoop JARs consistent with the <<<hadoop-openstack>>>
- module that is about to be tested.
-
- In the <<<hadoop-tools/hadoop-openstack>>> directory run
-
-+--
- mvn test -Dtest=TestSwiftRestClient
-+--
-
- This runs some simple tests which include authenticating
- against the remote swift service. If these tests fail, so will all
- the rest. If it does fail: check your authentication.
-
- Once this test succeeds, you can run the full test suite
-
-+--
- mvn test
-+--
-
- Be advised that these tests can take an hour or more, especially against a
- remote Swift service -or one that throttles bulk operations.
-
- Once the <<<auth-keys.xml>>> file is in place, the <<<mvn test>>> runs from
- the Hadoop source base directory will automatically run these OpenStack tests
- While this ensures that no regressions have occurred, it can also add significant
- time to test runs, and may run up bills, depending on who is providing\
- the Swift storage service. We recommend having a separate source tree
- set up purely for the Swift tests, and running it manually or by the CI tooling
- at a lower frequency than normal test runs.
-
- Finally: Apache Hadoop is an open source project. Contributions of code
- -including more tests- are very welcome.
http://git-wip-us.apache.org/repos/asf/hadoop/blob/245f7b2a/hadoop-tools/hadoop-openstack/src/site/markdown/index.md
----------------------------------------------------------------------
diff --git a/hadoop-tools/hadoop-openstack/src/site/markdown/index.md b/hadoop-tools/hadoop-openstack/src/site/markdown/index.md
new file mode 100644
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--- /dev/null
+++ b/hadoop-tools/hadoop-openstack/src/site/markdown/index.md
@@ -0,0 +1,544 @@
+<!---
+ 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. See accompanying LICENSE file.
+-->
+
+* [Hadoop OpenStack Support: Swift Object Store](#Hadoop_OpenStack_Support:_Swift_Object_Store)
+ * [Introduction](#Introduction)
+ * [Features](#Features)
+ * [Using the Hadoop Swift Filesystem Client](#Using_the_Hadoop_Swift_Filesystem_Client)
+ * [Concepts: services and containers](#Concepts:_services_and_containers)
+ * [Containers and Objects](#Containers_and_Objects)
+ * [Eventual Consistency](#Eventual_Consistency)
+ * [Non-atomic "directory" operations.](#Non-atomic_directory_operations.)
+ * [Working with Swift Object Stores in Hadoop](#Working_with_Swift_Object_Stores_in_Hadoop)
+ * [Swift Filesystem URIs](#Swift_Filesystem_URIs)
+ * [Installing](#Installing)
+ * [Configuring](#Configuring)
+ * [Example: Rackspace US, in-cluster access using API key](#Example:_Rackspace_US_in-cluster_access_using_API_key)
+ * [Example: Rackspace UK: remote access with password authentication](#Example:_Rackspace_UK:_remote_access_with_password_authentication)
+ * [Example: HP cloud service definition](#Example:_HP_cloud_service_definition)
+ * [General Swift Filesystem configuration options](#General_Swift_Filesystem_configuration_options)
+ * [Blocksize fs.swift.blocksize](#Blocksize_fs.swift.blocksize)
+ * [Partition size fs.swift.partsize](#Partition_size_fs.swift.partsize)
+ * [Request size fs.swift.requestsize](#Request_size_fs.swift.requestsize)
+ * [Connection timeout fs.swift.connect.timeout](#Connection_timeout_fs.swift.connect.timeout)
+ * [Connection timeout fs.swift.socket.timeout](#Connection_timeout_fs.swift.socket.timeout)
+ * [Connection Retry Count fs.swift.connect.retry.count](#Connection_Retry_Count_fs.swift.connect.retry.count)
+ * [Connection Throttle Delay fs.swift.connect.throttle.delay](#Connection_Throttle_Delay_fs.swift.connect.throttle.delay)
+ * [HTTP Proxy](#HTTP_Proxy)
+ * [Troubleshooting](#Troubleshooting)
+ * [ClassNotFoundException](#ClassNotFoundException)
+ * [Failure to Authenticate](#Failure_to_Authenticate)
+ * [Timeout connecting to the Swift Service](#Timeout_connecting_to_the_Swift_Service)
+ * [Warnings](#Warnings)
+ * [Limits](#Limits)
+ * [Testing the hadoop-openstack module](#Testing_the_hadoop-openstack_module)
+
+Hadoop OpenStack Support: Swift Object Store
+============================================
+
+Introduction
+------------
+
+[OpenStack](http://www.openstack.org/) is an open source cloud infrastructure which can be accessed from multiple public IaaS providers, and deployed privately. It offers infrastructure services such as VM hosting (Nova), authentication (Keystone) and storage of binary objects (Swift).
+
+This module enables Apache Hadoop applications -including MapReduce jobs, read and write data to and from instances of the [OpenStack Swift object store](http://www.openstack.org/software/openstack-storage/).
+
+Features
+--------
+
+* Read and write of data stored in a Swift object store
+
+* Support of a pseudo-hierachical file system (directories, subdirectories and
+ files)
+
+* Standard filesystem operations: `create`, `delete`, `mkdir`,
+ `ls`, `mv`, `stat`.
+
+* Can act as a source of data in a MapReduce job, or a sink.
+
+* Support for multiple OpenStack services, and multiple containers from a
+ single service.
+
+* Supports in-cluster and remote access to Swift data.
+
+* Supports OpenStack Keystone authentication with password or token.
+
+* Released under the Apache Software License
+
+* Tested against the Hadoop 3.x and 1.x branches, against multiple public
+ OpenStack clusters: Rackspace US, Rackspace UK, HP Cloud.
+
+* Tested against private OpenStack clusters, including scalability tests of
+ large file uploads.
+
+Using the Hadoop Swift Filesystem Client
+----------------------------------------
+
+### Concepts: services and containers
+
+OpenStack swift is an *Object Store*; also known as a *blobstore*. It stores arbitrary binary objects by name in a *container*.
+
+The Hadoop Swift filesystem library adds another concept, the *service*, which defines which Swift blobstore hosts a container -and how to connect to it.
+
+### Containers and Objects
+
+* Containers are created by users with accounts on the Swift filestore, and hold
+ *objects*.
+
+* Objects can be zero bytes long, or they can contain data.
+
+* Objects in the container can be up to 5GB; there is a special support for
+ larger files than this, which merges multiple objects in to one.
+
+* Each object is referenced by it's *name*; there is no notion of directories.
+
+* You can use any characters in an object name that can be 'URL-encoded'; the
+ maximum length of a name is 1034 characters -after URL encoding.
+
+* Names can have `/` characters in them, which are used to create the illusion of
+ a directory structure. For example `dir/dir2/name`. Even though this looks
+ like a directory, *it is still just a name*. There is no requirement to have
+ any entries in the container called `dir` or `dir/dir2`
+
+* That said. if the container has zero-byte objects that look like directory
+ names above other objects, they can pretend to be directories. Continuing the
+ example, a 0-byte object called `dir` would tell clients that it is a
+ directory while `dir/dir2` or `dir/dir2/name` were present. This creates an
+ illusion of containers holding a filesystem.
+
+Client applications talk to Swift over HTTP or HTTPS, reading, writing and deleting objects using standard HTTP operations (GET, PUT and DELETE, respectively). There is also a COPY operation, that creates a new object in the container, with a new name, containing the old data. There is no rename operation itself, objects need to be copied -then the original entry deleted.
+
+### Eventual Consistency
+
+The Swift Filesystem is \*eventually consistent\*: an operation on an object may not be immediately visible to that client, or other clients. This is a consequence of the goal of the filesystem: to span a set of machines, across multiple datacenters, in such a way that the data can still be available when many of them fail. (In contrast, the Hadoop HDFS filesystem is \*immediately consistent\*, but it does not span datacenters.)
+
+Eventual consistency can cause surprises for client applications that expect immediate consistency: after an object is deleted or overwritten, the object may still be visible -or the old data still retrievable. The Swift Filesystem client for Apache Hadoop attempts to handle this, in conjunction with the MapReduce engine, but there may be still be occasions when eventual consistency causes surprises.
+
+### Non-atomic "directory" operations.
+
+Hadoop expects some operations to be atomic, especially `rename()`, which is something the MapReduce layer relies on to commit the output of a job, renaming data from a temp directory to the final path. Because a rename is implemented as a copy of every blob under the directory's path, followed by a delete of the originals, the intermediate state of the operation will be visible to other clients. If two Reducer tasks to rename their temp directory to the final path, both operations may succeed, with the result that output directory contains mixed data. This can happen if MapReduce jobs are being run with *speculation* enabled and Swift used as the direct output of the MR job (it can also happen against Amazon S3).
+
+Other consequences of the non-atomic operations are:
+
+1. If a program is looking for the presence of the directory before acting
+ on the data -it may start prematurely. This can be avoided by using
+ other mechanisms to co-ordinate the programs, such as the presence of a file
+ that is written *after* any bulk directory operations.
+
+2. A `rename()` or `delete()` operation may include files added under
+ the source directory tree during the operation, may unintentionally delete
+ it, or delete the 0-byte swift entries that mimic directories and act
+ as parents for the files. Try to avoid doing this.
+
+The best ways to avoid all these problems is not using Swift as the filesystem between MapReduce jobs or other Hadoop workflows. It can act as a source of data, and a final destination, but it doesn't meet all of Hadoop's expectations of what a filesystem is -it's a *blobstore*.
+
+Working with Swift Object Stores in Hadoop
+------------------------------------------
+
+Once installed, the Swift FileSystem client can be used by any Hadoop application to read from or write to data stored in a Swift container.
+
+Data stored in Swift can be used as the direct input to a MapReduce job -simply use the `swift:` URL (see below) to declare the source of the data.
+
+This Swift Filesystem client is designed to work with multiple Swift object stores, both public and private. This allows the client to work with different clusters, reading and writing data to and from either of them.
+
+It can also work with the same object stores using multiple login details.
+
+These features are achieved by one basic concept: using a service name in the URI referring to a swift filesystem, and looking up all the connection and login details for that specific service. Different service names can be defined in the Hadoop XML configuration file, so defining different clusters, or providing different login details for the same object store(s).
+
+### Swift Filesystem URIs
+
+Hadoop uses URIs to refer to files within a filesystem. Some common examples are:
+
+ local://etc/hosts
+ hdfs://cluster1/users/example/data/set1
+ hdfs://cluster2.example.org:8020/users/example/data/set1
+
+The Swift Filesystem Client adds a new URL type `swift`. In a Swift Filesystem URL, the hostname part of a URL identifies the container and the service to work with; the path the name of the object. Here are some examples
+
+ swift://container.rackspace/my-object.csv
+ swift://data.hpcloud/data/set1
+ swift://dmitry.privatecloud/out/results
+
+In the last two examples, the paths look like directories: it is not, they are simply the objects named `data/set1` and `out/results` respectively.
+
+### Installing
+
+The `hadoop-openstack` JAR must be on the classpath of the Hadoop program trying to talk to the Swift service. If installed in the classpath of the Hadoop MapReduce service, then all programs started by the MR engine will pick up the JAR automatically. This is the easiest way to give all Hadoop jobs access to Swift.
+
+Alternatively, the JAR can be included as one of the JAR files that an application uses. This lets the Hadoop jobs work with a Swift object store even if the Hadoop cluster is not pre-configured for this.
+
+The library also depends upon the Apache HttpComponents library, which must also be on the classpath.
+
+### Configuring
+
+To talk to a swift service, the user must must provide:
+
+1. The URL defining the container and the service.
+
+2. In the cluster/job configuration, the login details of that service.
+
+Multiple service definitions can co-exist in the same configuration file: just use different names for them.
+
+#### Example: Rackspace US, in-cluster access using API key
+
+This service definition is for use in a Hadoop cluster deployed within Rackspace's US infrastructure.
+
+ <property>
+ <name>fs.swift.service.rackspace.auth.url</name>
+ <value>https://auth.api.rackspacecloud.com/v2.0/tokens</value>
+ <description>Rackspace US (multiregion)</description>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspace.username</name>
+ <value>user4</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspace.region</name>
+ <value>DFW</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspace.apikey</name>
+ <value>fe806aa86dfffe2f6ed8</value>
+ </property>
+
+Here the API key visible in the account settings API keys page is used to log in. No property for public/private access -the default is to use the private endpoint for Swift operations.
+
+This configuration also selects one of the regions, DFW, for its data.
+
+A reference to this service would use the `rackspace` service name:
+
+ swift://hadoop-container.rackspace/
+
+#### Example: Rackspace UK: remote access with password authentication
+
+This connects to Rackspace's UK ("LON") datacenter.
+
+ <property>
+ <name>fs.swift.service.rackspaceuk.auth.url</name>
+ <value>https://lon.identity.api.rackspacecloud.com/v2.0/tokens</value>
+ <description>Rackspace UK</description>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspaceuk.username</name>
+ <value>user4</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspaceuk.password</name>
+ <value>insert-password-here/value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.rackspace.public</name>
+ <value>true</value>
+ </property>
+
+This is a public access point connection, using a password over an API key.
+
+A reference to this service would use the `rackspaceuk` service name:
+
+ swift://hadoop-container.rackspaceuk/
+
+Because the public endpoint is used, if this service definition is used within the London datacenter, all accesses will be billed at the public upload/download rates, *irrespective of where the Hadoop cluster is*.
+
+#### Example: HP cloud service definition
+
+Here is an example that connects to the HP Cloud object store.
+
+ <property>
+ <name>fs.swift.service.hpcloud.auth.url</name>
+ <value>https://region-a.geo-1.identity.hpcloudsvc.com:35357/v2.0/tokens
+ </value>
+ <description>HP Cloud</description>
+ </property>
+
+ <property>
+ <name>fs.swift.service.hpcloud.tenant</name>
+ <value>FE806AA86</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.hpcloud.username</name>
+ <value>FE806AA86DFFFE2F6ED8</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.hpcloud.password</name>
+ <value>secret-password-goes-here</value>
+ </property>
+
+ <property>
+ <name>fs.swift.service.hpcloud.public</name>
+ <value>true</value>
+ </property>
+
+A reference to this service would use the `hpcloud` service name:
+
+ swift://hadoop-container.hpcloud/
+
+### General Swift Filesystem configuration options
+
+Some configuration options apply to the Swift client, independent of the specific Swift filesystem chosen.
+
+#### Blocksize fs.swift.blocksize
+
+Swift does not break up files into blocks, except in the special case of files over 5GB in length. Accordingly, there isn't a notion of a "block size" to define where the data is kept.
+
+Hadoop's MapReduce layer depends on files declaring their block size, so that it knows how to partition work. Too small a blocksize means that many mappers work on small pieces of data; too large a block size means that only a few mappers get started.
+
+The block size value reported by Swift, therefore, controls the basic workload partioning of the MapReduce engine -and can be an important parameter to tune for performance of the cluster.
+
+The property has a unit of kilobytes; the default value is `32*1024`: 32 MB
+
+ <property>
+ <name>fs.swift.blocksize</name>
+ <value>32768</value>
+ </property>
+
+This blocksize has no influence on how files are stored in Swift; it only controls what the reported size of blocks are - a value used in Hadoop MapReduce to divide work.
+
+Note that the MapReduce engine's split logic can be tuned independently by setting the `mapred.min.split.size` and `mapred.max.split.size` properties, which can be done in specific job configurations.
+
+ <property>
+ <name>mapred.min.split.size</name>
+ <value>524288</value>
+ </property>
+
+ <property>
+ <name>mapred.max.split.size</name>
+ <value>1048576</value>
+ </property>
+
+In an Apache Pig script, these properties would be set as:
+
+ mapred.min.split.size 524288
+ mapred.max.split.size 1048576
+
+#### Partition size fs.swift.partsize
+
+The Swift filesystem client breaks very large files into partitioned files, uploading each as it progresses, and writing any remaning data and an XML manifest when a partitioned file is closed.
+
+The partition size defaults to 4608 MB; 4.5GB, the maximum filesize that Swift can support.
+
+It is possible to set a smaller partition size, in the `fs.swift.partsize` option. This takes a value in KB.
+
+ <property>
+ <name>fs.swift.partsize</name>
+ <value>1024</value>
+ <description>upload every MB</description>
+ </property>
+
+When should this value be changed from its default?
+
+While there is no need to ever change it for basic operation of the Swift filesystem client, it can be tuned
+
+* If a Swift filesystem is location aware, then breaking a file up into
+ smaller partitions scatters the data round the cluster. For best performance,
+ the property `fs.swift.blocksize` should be set to a smaller value than the
+ partition size of files.
+
+* When writing to an unpartitioned file, the entire write is done in the
+ `close()` operation. When a file is partitioned, the outstanding data to
+ be written whenever the outstanding amount of data is greater than the
+ partition size. This means that data will be written more incrementally
+
+#### Request size fs.swift.requestsize
+
+The Swift filesystem client reads files in HTTP GET operations, asking for a block of data at a time.
+
+The default value is 64KB. A larger value may be more efficient over faster networks, as it reduces the overhead of setting up the HTTP operation.
+
+However, if the file is read with many random accesses, requests for data will be made from different parts of the file -discarding some of the previously requested data. The benefits of larger request sizes may be wasted.
+
+The property `fs.swift.requestsize` sets the request size in KB.
+
+ <property>
+ <name>fs.swift.requestsize</name>
+ <value>128</value>
+ </property>
+
+#### Connection timeout fs.swift.connect.timeout
+
+This sets the timeout in milliseconds to connect to a Swift service.
+
+ <property>
+ <name>fs.swift.connect.timeout</name>
+ <value>15000</value>
+ </property>
+
+A shorter timeout means that connection failures are raised faster -but may trigger more false alarms. A longer timeout is more resilient to network problems -and may be needed when talking to remote filesystems.
+
+#### Connection timeout fs.swift.socket.timeout
+
+This sets the timeout in milliseconds to wait for data from a connected socket.
+
+ <property>
+ <name>fs.swift.socket.timeout</name>
+ <value>60000</value>
+ </property>
+
+A shorter timeout means that connection failures are raised faster -but may trigger more false alarms. A longer timeout is more resilient to network problems -and may be needed when talking to remote filesystems.
+
+#### Connection Retry Count fs.swift.connect.retry.count
+
+This sets the number of times to try to connect to a service whenever an HTTP request is made.
+
+ <property>
+ <name>fs.swift.connect.retry.count</name>
+ <value>3</value>
+ </property>
+
+The more retries, the more resilient it is to transient outages -and the less rapid it is at detecting and reporting server connectivity problems.
+
+#### Connection Throttle Delay fs.swift.connect.throttle.delay
+
+This property adds a delay between bulk file copy and delete operations, to prevent requests being throttled or blocked by the remote service
+
+ <property>
+ <name>fs.swift.connect.throttle.delay</name>
+ <value>0</value>
+ </property>
+
+It is measured in milliseconds; "0" means do not add any delay.
+
+Throttling is enabled on the public endpoints of some Swift services. If `rename()` or `delete()` operations fail with `SwiftThrottledRequestException` exceptions, try setting this property.
+
+#### HTTP Proxy
+
+If the client can only access the Swift filesystem via a web proxy server, the client configuration must specify the proxy via the `fs.swift.connect.proxy.host` and `fs.swift.connect.proxy.port` properties.
+
+ <property>
+ <name>fs.swift.proxy.host</name>
+ <value>web-proxy</value>
+ </property>
+
+ <property>
+ <name>fs.swift.proxy.port</name>
+ <value>8088</value>
+ </property>
+
+If the host is declared, the proxy port must be set to a valid integer value.
+
+### Troubleshooting
+
+#### ClassNotFoundException
+
+The `hadoop-openstack` JAR -or any dependencies- may not be on your classpath.
+
+If it is a remote MapReduce job that is failing, make sure that the JAR is installed on the servers in the cluster -or that the job submission process uploads the JAR file to the distributed cache.
+
+#### Failure to Authenticate
+
+A `SwiftAuthenticationFailedException` is thrown when the client cannot authenticate with the OpenStack keystone server. This could be because the URL in the service definition is wrong, or because the supplied credentials are invalid.
+
+1. Check the authentication URL through `curl` or your browser
+
+2. Use a Swift client such as CyberDuck to validate your credentials
+
+3. If you have included a tenant ID, try leaving it out. Similarly,
+ try adding it if you had not included it.
+
+4. Try switching from API key authentication to password-based authentication,
+ by setting the password.
+
+5. Change your credentials. As with Amazon AWS clients, some credentials
+ don't seem to like going over the network.
+
+#### Timeout connecting to the Swift Service
+
+This happens if the client application is running outside an OpenStack cluster, where it does not have access to the private hostname/IP address for filesystem operations. Set the `public` flag to true -but remember to set it to false for use in-cluster.
+
+### Warnings
+
+1. Do not share your login details with anyone, which means do not log the
+ details, or check the XML configuration files into any revision control system
+ to which you do not have exclusive access.
+
+2. Similarly, do not use your real account details in any
+ documentation \*or any bug reports submitted online\*
+
+3. Prefer the apikey authentication over passwords as it is easier
+ to revoke a key -and some service providers allow you to set
+ an automatic expiry date on a key when issued.
+
+4. Do not use the public service endpoint from within a public OpenStack
+ cluster, as it will run up large bills.
+
+5. Remember: it's not a real filesystem or hierarchical directory structure.
+ Some operations (directory rename and delete) take time and are not atomic or
+ isolated from other operations taking place.
+
+6. Append is not supported.
+
+7. Unix-style permissions are not supported. All accounts with write access to
+ a repository have unlimited access; the same goes for those with read access.
+
+8. In the public clouds, do not make the containers public unless you are happy
+ with anyone reading your data, and are prepared to pay the costs of their
+ downloads.
+
+### Limits
+
+* Maximum length of an object path: 1024 characters
+
+* Maximum size of a binary object: no absolute limit. Files \> 5GB are
+ partitioned into separate files in the native filesystem, and merged during
+ retrieval. *Warning:* the partitioned/large file support is the
+ most complex part of the Hadoop/Swift FS integration, and, along with
+ authentication, the most troublesome to support.
+
+### Testing the hadoop-openstack module
+
+The `hadoop-openstack` can be remotely tested against any public or private cloud infrastructure which supports the OpenStack Keystone authentication mechanism. It can also be tested against private OpenStack clusters. OpenStack Development teams are strongly encouraged to test the Hadoop swift filesystem client against any version of Swift that they are developing or deploying, to stress their cluster and to identify bugs early.
+
+The module comes with a large suite of JUnit tests -tests that are only executed if the source tree includes credentials to test against a specific cluster.
+
+After checking out the Hadoop source tree, create the file:
+
+ hadoop-tools/hadoop-openstack/src/test/resources/auth-keys.xml
+
+Into this file, insert the credentials needed to bond to the test filesystem, as decribed above.
+
+Next set the property `test.fs.swift.name` to the URL of a swift container to test against. The tests expect exclusive access to this container -do not keep any other data on it, or expect it to be preserved.
+
+ <property>
+ <name>test.fs.swift.name</name>
+ <value>swift://test.myswift/</value>
+ </property>
+
+In the base hadoop directory, run:
+
+ mvn clean install -DskipTests
+
+This builds a set of Hadoop JARs consistent with the `hadoop-openstack` module that is about to be tested.
+
+In the `hadoop-tools/hadoop-openstack` directory run
+
+ mvn test -Dtest=TestSwiftRestClient
+
+This runs some simple tests which include authenticating against the remote swift service. If these tests fail, so will all the rest. If it does fail: check your authentication.
+
+Once this test succeeds, you can run the full test suite
+
+ mvn test
+
+Be advised that these tests can take an hour or more, especially against a remote Swift service -or one that throttles bulk operations.
+
+Once the `auth-keys.xml` file is in place, the `mvn test` runs from the Hadoop source base directory will automatically run these OpenStack tests While this ensures that no regressions have occurred, it can also add significant time to test runs, and may run up bills, depending on who is providingthe Swift storage service. We recommend having a separate source tree set up purely for the Swift tests, and running it manually or by the CI tooling at a lower frequency than normal test runs.
+
+Finally: Apache Hadoop is an open source project. Contributions of code -including more tests- are very welcome.
http://git-wip-us.apache.org/repos/asf/hadoop/blob/245f7b2a/hadoop-tools/hadoop-openstack/src/site/resources/css/site.css
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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.
+*/
+#banner {
+ height: 93px;
+ background: none;
+}
+
+#bannerLeft img {
+ margin-left: 30px;
+ margin-top: 10px;
+}
+
+#bannerRight img {
+ margin: 17px;
+}
http://git-wip-us.apache.org/repos/asf/hadoop/blob/245f7b2a/hadoop-tools/hadoop-sls/src/site/apt/SchedulerLoadSimulator.apt.vm
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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.
-
- ---
- Yarn Scheduler Load Simulator (SLS)
- ---
- ---
- ${maven.build.timestamp}
-
-Yarn Scheduler Load Simulator (SLS)
-
-%{toc|section=1|fromDepth=0}
-
-* Overview
-
-** Overview
-
- The Yarn scheduler is a fertile area of interest with different
- implementations, e.g., Fifo, Capacity and Fair schedulers. Meanwhile, several
- optimizations are also made to improve scheduler performance for different
- scenarios and workload. Each scheduler algorithm has its own set of features,
- and drives scheduling decisions by many factors, such as fairness, capacity
- guarantee, resource availability, etc. It is very important to evaluate a
- scheduler algorithm very well before we deploy in a production cluster.
- Unfortunately, currently it is non-trivial to evaluate a scheduler algorithm.
- Evaluating in a real cluster is always time and cost consuming, and it is
- also very hard to find a large-enough cluster. Hence, a simulator which can
- predict how well a scheduler algorithm for some specific workload would be
- quite useful.
-
- The Yarn Scheduler Load Simulator (SLS) is such a tool, which can simulate
- large-scale Yarn clusters and application loads in a single machine.This
- simulator would be invaluable in furthering Yarn by providing a tool for
- researchers and developers to prototype new scheduler features and predict
- their behavior and performance with reasonable amount of confidence,
- thereby aiding rapid innovation.
-
- The simulator will exercise the real Yarn <<<ResourceManager>>> removing the
- network factor by simulating <<<NodeManagers>>> and <<<ApplicationMasters>>>
- via handling and dispatching <<<NM>>>/<<<AMs>>> heartbeat events from within
- the same JVM. To keep tracking of scheduler behavior and performance, a
- scheduler wrapper will wrap the real scheduler.
-
- The size of the cluster and the application load can be loaded from
- configuration files, which are generated from job history files directly by
- adopting {{{https://hadoop.apache.org/docs/stable/rumen.html}Apache Rumen}}.
-
- The simulator will produce real time metrics while executing, including:
-
- * Resource usages for whole cluster and each queue, which can be utilized to
- configure cluster and queue's capacity.
-
- * The detailed application execution trace (recorded in relation to simulated
- time), which can be analyzed to understand/validate the scheduler behavior
- (individual jobs turn around time, throughput, fairness, capacity guarantee,
- etc.).
-
- * Several key metrics of scheduler algorithm, such as time cost of each
- scheduler operation (allocate, handle, etc.), which can be utilized by Hadoop
- developers to find the code spots and scalability limits.
-
-** Goals
-
- * Exercise the scheduler at scale without a real cluster using real job
- traces.
-
- * Being able to simulate real workloads.
-
-** Architecture
-
- The following figure illustrates the implementation architecture of the
- simulator.
-
-[images/sls_arch.png] The architecture of the simulator
-
- The simulator takes input of workload traces, and fetches the cluster and
- applications information. For each NM and AM, the simulator builds a simulator
- to simulate their running. All NM/AM simulators run in a thread pool. The
- simulator reuses Yarn Resource Manager, and builds a wrapper out of the
- scheduler. The Scheduler Wrapper can track the scheduler behaviors and
- generates several logs, which are the outputs of the simulator and can be
- further analyzed.
-
-** Usecases
-
- * Engineering
-
- * Verify correctness of scheduler algorithm under load
-
- * Cheap/practical way for finding code hotspots/critical-path.
-
- * Validate the impact of changes and new features.
-
- * Determine what drives the scheduler scalability limits.
-
- []
-
- * QA
-
- * Validate scheduler behavior for "large" clusters and several workload
- profiles.
-
- * Solutions/Sales.
-
- * Sizing model for predefined/typical workloads.
-
- * Cluster sizing tool using real customer data (job traces).
-
- * Determine minimum SLAs under a particular workload.
-
-* Usage
-
- This section will show how to use the simulator. Here let <<<$HADOOP_ROOT>>>
- represent the Hadoop install directory. If you build Hadoop yourself,
- <<<$HADOOP_ROOT>>> is <<<hadoop-dist/target/hadoop-$VERSION>>>. The simulator
- is located at <<<$HADOOP_ROOT/share/hadoop/tools/sls>>>. The fold <<<sls>>>
- containers four directories: <<<bin>>>, <<<html>>>, <<<sample-conf>>>, and
- <<<sample-data>>>
-
- * <<<bin>>>: contains running scripts for the simulator.
-
- * <<<html>>>: contains several html/css/js files we needed for real-time
- tracking.
-
- * <<<sample-conf>>>: specifies the simulator configurations.
-
- * <<<sample-data>>>: provides an example rumen trace, which can be used to
- generate inputs of the simulator.
-
- []
-
- The following sections will describe how to use the simulator step by step.
- Before start, make sure that command <<<hadoop>>> is included in your
- <<<$PATH>>> environment parameter.
-
-** Step 1: Configure Hadoop and the simulator
-
- Before we start, make sure Hadoop and the simulator are configured well.
- All configuration files for Hadoop and the simulator should be placed in
- directory <<<$HADOOP_ROOT/etc/hadoop>>>, where the <<<ResourceManager>>>
- and Yarn scheduler load their configurations. Directory
- <<<$HADOOP_ROOT/share/hadoop/tools/sls/sample-conf/>>> provides several
- example configurations, that can be used to start a demo.
-
- For configuration of Hadoop and Yarn scheduler, users can refer to Yarn’s
- website ({{{http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/}
- http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/}}).
-
- For the simulator, it loads configuration information from file
- <<<$HADOOP_ROOT/etc/hadoop/sls-runner.xml>>>.
-
- Here we illustrate each configuration parameter in <<<sls-runner.xml>>>.
- Note that <<<$HADOOP_ROOT/share/hadoop/tools/sls/sample-conf/sls-runner.xml>>>
- contains all the default values for these configuration parameters.
-
- * <<<yarn.sls.runner.pool.size>>>
-
- The simulator uses a thread pool to simulate the <<<NM>>> and <<<AM>>> running
- , and this parameter specifies the number of threads in the pool.
-
- * <<<yarn.sls.nm.memory.mb>>>
-
- The total memory for each <<<NMSimulator>>>.
-
- * <<<yarn.sls.nm.vcores>>>
-
- The total vCores for each <<<NMSimulator>>>.
-
- * <<<yarn.sls.nm.heartbeat.interval.ms>>>
-
- The heartbeat interval for each <<<NMSimulator>>>.
-
- * <<<yarn.sls.am.heartbeat.interval.ms>>>
-
- The heartbeat interval for each <<<AMSimulator>>>.
-
- * <<<yarn.sls.am.type.mapreduce>>>
-
- The <<<AMSimulator>>> implementation for MapReduce-like applications.
- Users can specify implementations for other type of applications.
-
- * <<<yarn.sls.container.memory.mb>>>
-
- The memory required for each container simulator.
-
- * <<<yarn.sls.container.vcores>>>
-
- The vCores required for each container simulator.
-
- * <<<yarn.sls.runner.metrics.switch>>>
-
- The simulator introduces {{{http://metrics.codahale.com/}Metrics}} to measure
- the behaviors of critical components and operations. This field specifies
- whether we open (<<<ON>>>) or close (<<<OFF>>>) the Metrics running.
-
- * <<<yarn.sls.metrics.web.address.port>>>
-
- The port used by simulator to provide real-time tracking. The default value is
- 10001.
-
- * <<<org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler>>>
-
- The implementation of scheduler metrics of Fifo Scheduler.
-
- * <<<org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler>>>
-
- The implementation of scheduler metrics of Fair Scheduler.
-
- * <<<org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler>>>
-
- The implementation of scheduler metrics of Capacity Scheduler.
-
-** Step 2: Run the simulator
-
- The simulator supports two types of input files: the rumen traces and its own
- input traces. The script to start the simulator is <<<slsrun.sh>>>.
-
-+----+
-$ cd $HADOOP_ROOT/share/hadoop/tools/sls
-$ bin/slsrun.sh
- --input-rumen|--input-sls=<TRACE_FILE1,TRACE_FILE2,...>
- --output-dir=<SLS_SIMULATION_OUTPUT_DIRECTORY> [--nodes=<SLS_NODES_FILE>]
- [--track-jobs=<JOBID1,JOBID2,...>] [--print-simulation]
-+----+
-
- * <<<--input-rumen>>>: The input rumen trace files. Users can input multiple
- files, separated by comma. One example trace is provided in
- <<<$HADOOP_ROOT/share/hadoop/tools/sls/sample-data/2jobs2min-rumen-jh.json>>>.
-
- * <<<--input-sls>>>: Simulator its own file format. The simulator also
- provides a tool to convert rumen traces to sls traces (<<<rumen2sls.sh>>>).
- Refer to appendix for an example of sls input json file.
-
- * <<<--output-dir>>>: The output directory for generated running logs and
- metrics.
-
- * <<<--nodes>>>: The cluster topology. By default, the simulator will use the
- topology fetched from the input json files. Users can specifies a new topology
- by setting this parameter. Refer to the appendix for the topology file format.
-
- * <<<--track-jobs>>>: The particular jobs that will be tracked during
- simulator running, spearated by comma.
-
- * <<<--print-simulation>>>: Whether to print out simulation information
- before simulator running, including number of nodes, applications, tasks,
- and information for each application.
-
- In comparison to rumen format, here the sls format is much simpler and users
- can easily generate various workload. The simulator also provides a tool to
- convert rumen traces to sls traces.
-
-+----+
-$ bin/rumen2sls.sh
- --rumen-file=<RUMEN_FILE>
- --output-dir=<SLS_OUTPUT_DIRECTORY>
- [--output-prefix=<SLS_FILE_PREFIX>]
-+----+
-
- * <<<--rumen-file>>>: The rumen format file. One example trace is provided
- in directory <<<sample-data>>>.
-
- * <<<--output-dir>>>: The output directory of generated simulation traces.
- Two files will be generated in this output directory, including one trace
- file including all job and task information, and another file showing the
- topology information.
-
- * <<<--output-prefix>>>: The prefix of the generated files. The default value
- is ”sls”, and the two generated files are <<<sls-jobs.json>>> and
- <<<sls-nodes.json>>>.
-
-* Metrics
-
- The Yarn Scheduler Load Simulator has integrated
- {{{http://metrics.codahale.com/}Metrics}} to measure the behaviors of critical
- components and operations, including running applications and containers,
- cluster available resources, scheduler operation timecost, et al. If the
- switch <<<yarn.sls.runner.metrics.switch>>> is set <<<ON>>>, <<<Metrics>>>
- will run and output it logs in <<<--output-dir>>> directory specified by users.
- Users can track these information during simulator running, and can also
- analyze these logs after running to evaluate the scheduler performance.
-
-** Real-time Tracking
-
- The simulator provides an interface for tracking its running in real-time.
- Users can go to <<<http://host:port/simulate>>> to track whole running,
- and <<<http://host:port/track>>> to track a particular job or queue. Here
- the <<<host>>> is the place when we run the simulator, and <<<port>>> is
- the value configured by <<<yarn.sls.metrics.web.address.port>>> (default value
- is 10001).
-
- Here we'll illustrate each chart shown in the webpage.
-
- The first figure describes the number of running applications and containers.
-
-[images/sls_running_apps_containers.png] Number of running applications/containers
-
- The second figure describes the allocated and available resources (memory)
- in the cluster.
-
-[images/sls_cluster_memory.png] Cluster Resource (Memory)
-
- The third figure describes the allocated resource for each queue. Here we have
- three queues: sls_queue_1, sls_queue_2, and sls_queue_3.The first two queues
- are configured with 25% share, while the last one has 50% share.
-
-[images/sls_queue_allocated_memory.png] Queue Allocated Resource (Memory)
-
- The fourth figure describes the timecost for each scheduler operation.
-
-[images/sls_scheduler_operation_timecost.png] Scheduler Opertion Timecost
-
- Finally, we measure the memory used by the simulator.
-
-[images/sls_JVM.png] JVM Memory
-
- The simulator also provides an interface for tracking some particular
- jobs and queues. Go to <<<http://<Host>:<Port>/track>>> to get these
- information.
-
- Here the first figure illustrates the resource usage information for queue
- <<<SLS_Queue_1>>>.
-
-[images/sls_track_queue.png] Tracking Queue <<<sls_queue_3>>>
-
- The second figure illustrates the resource usage information for job
- <<<job_1369942127770_0653>>>.
-
-[images/sls_track_job.png] Tracking Job <<<job_1369942127770_0653>>>
-
-** Offline Analysis
-
- After the simulator finishes, all logs are saved in the output directory
- specified by <<<--output-dir>>> in
- <<<$HADOOP_ROOT/share/hadoop/tools/sls/bin/slsrun.sh>>>.
-
- * File <<<realtimetrack.json>>>: records all real-time tracking logs every 1
- second.
-
- * File <<<jobruntime.csv>>>: records all jobs’ start and end time in the
- simulator.
-
- * Folder <<<metrics>>>: logs generated by the Metrics.
-
- []
-
- Users can also reproduce those real-time tracking charts in offline mode.
- Just upload the <<<realtimetrack.json>>> to
- <<<$HADOOP_ROOT/share/hadoop/tools/sls/html/showSimulationTrace.html>>>.
- For browser security problem, need to put files <<<realtimetrack.json>>> and
- <<<showSimulationTrace.html>>> in the same directory.
-
-* Appendix
-
-** Resources
-
- {{{https://issues.apache.org/jira/browse/YARN-1021}YARN-1021}} is the main
- JIRA that introduces Yarn Scheduler Load Simulator to Hadoop Yarn project.
-
-** SLS JSON input file format
-
- Here we provide an example format of the sls json file, which contains 2 jobs.
- The first job has 3 map tasks and the second one has 2 map tasks.
-
-+----+
-{
- "am.type" : "mapreduce",
- "job.start.ms" : 0,
- "job.end.ms" : 95375,
- "job.queue.name" : "sls_queue_1",
- "job.id" : "job_1",
- "job.user" : "default",
- "job.tasks" : [ {
- "container.host" : "/default-rack/node1",
- "container.start.ms" : 6664,
- "container.end.ms" : 23707,
- "container.priority" : 20,
- "container.type" : "map"
- }, {
- "container.host" : "/default-rack/node3",
- "container.start.ms" : 6665,
- "container.end.ms" : 21593,
- "container.priority" : 20,
- "container.type" : "map"
- }, {
- "container.host" : "/default-rack/node2",
- "container.start.ms" : 68770,
- "container.end.ms" : 86613,
- "container.priority" : 20,
- "container.type" : "map"
- } ]
-}
-{
- "am.type" : "mapreduce",
- "job.start.ms" : 105204,
- "job.end.ms" : 197256,
- "job.queue.name" : "sls_queue_2",
- "job.id" : "job_2",
- "job.user" : "default",
- "job.tasks" : [ {
- "container.host" : "/default-rack/node1",
- "container.start.ms" : 111822,
- "container.end.ms" : 133985,
- "container.priority" : 20,
- "container.type" : "map"
- }, {
- "container.host" : "/default-rack/node2",
- "container.start.ms" : 111788,
- "container.end.ms" : 131377,
- "container.priority" : 20,
- "container.type" : "map"
- } ]
-}
-+----+
-
-** Simulator input topology file format
-
- Here is an example input topology file which has 3 nodes organized in 1 rack.
-
-+----+
-{
- "rack" : "default-rack",
- "nodes" : [ {
- "node" : "node1"
- }, {
- "node" : "node2"
- }, {
- "node" : "node3"
- }]
-}
-+----+
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/hadoop/blob/245f7b2a/hadoop-tools/hadoop-sls/src/site/markdown/SchedulerLoadSimulator.md
----------------------------------------------------------------------
diff --git a/hadoop-tools/hadoop-sls/src/site/markdown/SchedulerLoadSimulator.md b/hadoop-tools/hadoop-sls/src/site/markdown/SchedulerLoadSimulator.md
new file mode 100644
index 0000000..ca179ee
--- /dev/null
+++ b/hadoop-tools/hadoop-sls/src/site/markdown/SchedulerLoadSimulator.md
@@ -0,0 +1,357 @@
+<!---
+ 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. See accompanying LICENSE file.
+-->
+
+Yarn Scheduler Load Simulator (SLS)
+===================================
+
+* [Yarn Scheduler Load Simulator (SLS)](#Yarn_Scheduler_Load_Simulator_SLS)
+ * [Overview](#Overview)
+ * [Overview](#Overview)
+ * [Goals](#Goals)
+ * [Architecture](#Architecture)
+ * [Usecases](#Usecases)
+ * [Usage](#Usage)
+ * [Step 1: Configure Hadoop and the simulator](#Step_1:_Configure_Hadoop_and_the_simulator)
+ * [Step 2: Run the simulator](#Step_2:_Run_the_simulator)
+ * [Metrics](#Metrics)
+ * [Real-time Tracking](#Real-time_Tracking)
+ * [Offline Analysis](#Offline_Analysis)
+ * [Appendix](#Appendix)
+ * [Resources](#Resources)
+ * [SLS JSON input file format](#SLS_JSON_input_file_format)
+ * [Simulator input topology file format](#Simulator_input_topology_file_format)
+
+Overview
+--------
+
+### Overview
+
+The Yarn scheduler is a fertile area of interest with different implementations, e.g., Fifo, Capacity and Fair schedulers. Meanwhile, several optimizations are also made to improve scheduler performance for different scenarios and workload. Each scheduler algorithm has its own set of features, and drives scheduling decisions by many factors, such as fairness, capacity guarantee, resource availability, etc. It is very important to evaluate a scheduler algorithm very well before we deploy in a production cluster. Unfortunately, currently it is non-trivial to evaluate a scheduler algorithm. Evaluating in a real cluster is always time and cost consuming, and it is also very hard to find a large-enough cluster. Hence, a simulator which can predict how well a scheduler algorithm for some specific workload would be quite useful.
+
+The Yarn Scheduler Load Simulator (SLS) is such a tool, which can simulate large-scale Yarn clusters and application loads in a single machine.This simulator would be invaluable in furthering Yarn by providing a tool for researchers and developers to prototype new scheduler features and predict their behavior and performance with reasonable amount of confidence, thereby aiding rapid innovation.
+o
+The simulator will exercise the real Yarn `ResourceManager` removing the network factor by simulating `NodeManagers` and `ApplicationMasters` via handling and dispatching `NM`/`AMs` heartbeat events from within the same JVM. To keep tracking of scheduler behavior and performance, a scheduler wrapper will wrap the real scheduler.
+
+The size of the cluster and the application load can be loaded from configuration files, which are generated from job history files directly by adopting [Apache Rumen](https://hadoop.apache.org/docs/stable/rumen.html).
+
+The simulator will produce real time metrics while executing, including:
+
+* Resource usages for whole cluster and each queue, which can be utilized to
+ configure cluster and queue's capacity.
+
+* The detailed application execution trace (recorded in relation to simulated
+ time), which can be analyzed to understand/validate the scheduler behavior
+ (individual jobs turn around time, throughput, fairness, capacity guarantee,
+ etc.).
+
+* Several key metrics of scheduler algorithm, such as time cost of each
+ scheduler operation (allocate, handle, etc.), which can be utilized by Hadoop
+ developers to find the code spots and scalability limits.
+
+### Goals
+
+* Exercise the scheduler at scale without a real cluster using real job
+ traces.
+
+* Being able to simulate real workloads.
+
+### Architecture
+
+The following figure illustrates the implementation architecture of the simulator.
+
+
+
+The simulator takes input of workload traces, and fetches the cluster and applications information. For each NM and AM, the simulator builds a simulator to simulate their running. All NM/AM simulators run in a thread pool. The simulator reuses Yarn Resource Manager, and builds a wrapper out of the scheduler. The Scheduler Wrapper can track the scheduler behaviors and generates several logs, which are the outputs of the simulator and can be further analyzed.
+
+### Usecases
+
+* Engineering
+ * Verify correctness of scheduler algorithm under load
+ * Cheap/practical way for finding code hotspots/critical-path.
+ * Validate the impact of changes and new features.
+ * Determine what drives the scheduler scalability limits.
+
+* QA
+ * Validate scheduler behavior for "large" clusters and several workload profiles.
+
+* Solutions/Sales.
+ * Sizing model for predefined/typical workloads.
+ * Cluster sizing tool using real customer data (job traces).
+ * Determine minimum SLAs under a particular workload.
+
+Usage
+-----
+
+This section will show how to use the simulator. Here let `$HADOOP_ROOT` represent the Hadoop install directory. If you build Hadoop yourself, `$HADOOP_ROOT` is `hadoop-dist/target/hadoop-$VERSION`. The simulator is located at `$HADOOP_ROOT/share/hadoop/tools/sls`. The fold `sls` containers four directories: `bin`, `html`, `sample-conf`, and `sample-data`
+
+* `bin`: contains running scripts for the simulator.
+
+* `html`: contains several html/css/js files we needed for real-time tracking.
+
+* `sample-conf`: specifies the simulator configurations.
+
+* `sample-data`: provides an example rumen trace, which can be used to
+ generate inputs of the simulator.
+
+The following sections will describe how to use the simulator step by step. Before start, make sure that command `hadoop` is included in your `$PATH` environment parameter.
+
+### Step 1: Configure Hadoop and the simulator
+
+Before we start, make sure Hadoop and the simulator are configured well. All configuration files for Hadoop and the simulator should be placed in directory `$HADOOP_ROOT/etc/hadoop`, where the `ResourceManager` and Yarn scheduler load their configurations. Directory `$HADOOP_ROOT/share/hadoop/tools/sls/sample-conf/` provides several example configurations, that can be used to start a demo.
+
+For configuration of Hadoop and Yarn scheduler, users can refer to Yarn’s website (<http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/>).
+
+For the simulator, it loads configuration information from file `$HADOOP_ROOT/etc/hadoop/sls-runner.xml`.
+
+Here we illustrate each configuration parameter in `sls-runner.xml`. Note that `$HADOOP_ROOT/share/hadoop/tools/sls/sample-conf/sls-runner.xml` contains all the default values for these configuration parameters.
+
+* `yarn.sls.runner.pool.size`
+
+ The simulator uses a thread pool to simulate the `NM` and `AM` running,
+ and this parameter specifies the number of threads in the pool.
+
+* `yarn.sls.nm.memory.mb`
+
+ The total memory for each `NMSimulator`.
+
+* `yarn.sls.nm.vcores`
+
+ The total vCores for each `NMSimulator`.
+
+* `yarn.sls.nm.heartbeat.interval.ms`
+
+ The heartbeat interval for each `NMSimulator`.
+
+* `yarn.sls.am.heartbeat.interval.ms`
+
+ The heartbeat interval for each `AMSimulator`.
+
+* `yarn.sls.am.type.mapreduce`
+
+ The `AMSimulator` implementation for MapReduce-like applications.
+ Users can specify implementations for other type of applications.
+
+* `yarn.sls.container.memory.mb`
+
+ The memory required for each container simulator.
+
+* `yarn.sls.container.vcores`
+
+ The vCores required for each container simulator.
+
+* `yarn.sls.runner.metrics.switch`
+
+ The simulator introduces [Metrics](http://metrics.codahale.com/) to measure
+ the behaviors of critical components and operations. This field specifies
+ whether we open (`ON`) or close (`OFF`) the Metrics running.
+
+* `yarn.sls.metrics.web.address.port`
+
+ The port used by simulator to provide real-time tracking. The default value is
+ 10001.
+
+* `org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler`
+
+ The implementation of scheduler metrics of Fifo Scheduler.
+
+* `org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler`
+
+ The implementation of scheduler metrics of Fair Scheduler.
+
+* `org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler`
+
+ The implementation of scheduler metrics of Capacity Scheduler.
+
+### Step 2: Run the simulator
+
+The simulator supports two types of input files: the rumen traces and its own input traces. The script to start the simulator is `slsrun.sh`.
+
+ $ cd $HADOOP_ROOT/share/hadoop/tools/sls
+ $ bin/slsrun.sh
+ --input-rumen |--input-sls=<TRACE_FILE1,TRACE_FILE2,...>
+ --output-dir=<SLS_SIMULATION_OUTPUT_DIRECTORY> [--nodes=<SLS_NODES_FILE>]
+ [--track-jobs=<JOBID1,JOBID2,...>] [--print-simulation]
+
+* `--input-rumen`: The input rumen trace files. Users can input multiple
+ files, separated by comma. One example trace is provided in
+ `$HADOOP_ROOT/share/hadoop/tools/sls/sample-data/2jobs2min-rumen-jh.json`.
+
+* `--input-sls`: Simulator its own file format. The simulator also
+ provides a tool to convert rumen traces to sls traces (`rumen2sls.sh`).
+ Refer to appendix for an example of sls input json file.
+
+* `--output-dir`: The output directory for generated running logs and
+ metrics.
+
+* `--nodes`: The cluster topology. By default, the simulator will use the
+ topology fetched from the input json files. Users can specifies a new topology
+ by setting this parameter. Refer to the appendix for the topology file format.
+
+* `--track-jobs`: The particular jobs that will be tracked during
+ simulator running, spearated by comma.
+
+* `--print-simulation`: Whether to print out simulation information
+ before simulator running, including number of nodes, applications, tasks,
+ and information for each application.
+
+ In comparison to rumen format, here the sls format is much simpler and users
+ can easily generate various workload. The simulator also provides a tool to
+ convert rumen traces to sls traces.
+
+ $ bin/rumen2sls.sh
+ --rumen-file=<RUMEN_FILE>
+ --output-dir=<SLS_OUTPUT_DIRECTORY>
+ [--output-prefix=<SLS_FILE_PREFIX>]
+
+* `--rumen-file`: The rumen format file. One example trace is provided
+ in directory `sample-data`.
+
+* `--output-dir`: The output directory of generated simulation traces.
+ Two files will be generated in this output directory, including one trace
+ file including all job and task information, and another file showing the
+ topology information.
+
+* `--output-prefix`: The prefix of the generated files. The default value
+ is "sls", and the two generated files are `sls-jobs.json` and
+ `sls-nodes.json`.
+
+Metrics
+-------
+
+The Yarn Scheduler Load Simulator has integrated [Metrics](http://metrics.codahale.com/) to measure the behaviors of critical components and operations, including running applications and containers, cluster available resources, scheduler operation timecost, et al. If the switch `yarn.sls.runner.metrics.switch` is set `ON`, `Metrics` will run and output it logs in `--output-dir` directory specified by users. Users can track these information during simulator running, and can also analyze these logs after running to evaluate the scheduler performance.
+
+### Real-time Tracking
+
+The simulator provides an interface for tracking its running in real-time. Users can go to `http://host:port/simulate` to track whole running, and `http://host:port/track` to track a particular job or queue. Here the `host` is the place when we run the simulator, and `port` is the value configured by `yarn.sls.metrics.web.address.port` (default value is 10001).
+
+Here we'll illustrate each chart shown in the webpage.
+
+The first figure describes the number of running applications and containers.
+
+
+
+The second figure describes the allocated and available resources (memory) in the cluster.
+
+
+
+The third figure describes the allocated resource for each queue. Here we have three queues: sls\_queue\_1, sls\_queue\_2, and sls\_queue\_3.The first two queues are configured with 25% share, while the last one has 50% share.
+
+
+
+The fourth figure describes the timecost for each scheduler operation.
+
+
+
+Finally, we measure the memory used by the simulator.
+
+
+
+The simulator also provides an interface for tracking some particular jobs and queues. Go to `http://<Host>:<Port>/track` to get these information.
+
+Here the first figure illustrates the resource usage information for queue `SLS_Queue_1`.
+
+
+
+The second figure illustrates the resource usage information for job `job_1369942127770_0653`.
+
+
+
+### Offline Analysis
+
+After the simulator finishes, all logs are saved in the output directory specified by `--output-dir` in `$HADOOP_ROOT/share/hadoop/tools/sls/bin/slsrun.sh`.
+
+* File `realtimetrack.json`: records all real-time tracking logs every 1
+ second.
+
+* File `jobruntime.csv`: records all jobs’ start and end time in the
+ simulator.
+
+* Folder `metrics`: logs generated by the Metrics.
+
+Users can also reproduce those real-time tracking charts in offline mode. Just upload the `realtimetrack.json` to `$HADOOP_ROOT/share/hadoop/tools/sls/html/showSimulationTrace.html`. For browser security problem, need to put files `realtimetrack.json` and `showSimulationTrace.html` in the same directory.
+
+Appendix
+--------
+
+### Resources
+
+[YARN-1021](https://issues.apache.org/jira/browse/YARN-1021) is the main JIRA that introduces Yarn Scheduler Load Simulator to Hadoop Yarn project.
+
+### SLS JSON input file format
+
+Here we provide an example format of the sls json file, which contains 2 jobs. The first job has 3 map tasks and the second one has 2 map tasks.
+
+ {
+ "am.type" : "mapreduce",
+ "job.start.ms" : 0,
+ "job.end.ms" : 95375,
+ "job.queue.name" : "sls_queue_1",
+ "job.id" : "job_1",
+ "job.user" : "default",
+ "job.tasks" : [ {
+ "container.host" : "/default-rack/node1",
+ "container.start.ms" : 6664,
+ "container.end.ms" : 23707,
+ "container.priority" : 20,
+ "container.type" : "map"
+ }, {
+ "container.host" : "/default-rack/node3",
+ "container.start.ms" : 6665,
+ "container.end.ms" : 21593,
+ "container.priority" : 20,
+ "container.type" : "map"
+ }, {
+ "container.host" : "/default-rack/node2",
+ "container.start.ms" : 68770,
+ "container.end.ms" : 86613,
+ "container.priority" : 20,
+ "container.type" : "map"
+ } ]
+ }
+ {
+ "am.type" : "mapreduce",
+ "job.start.ms" : 105204,
+ "job.end.ms" : 197256,
+ "job.queue.name" : "sls_queue_2",
+ "job.id" : "job_2",
+ "job.user" : "default",
+ "job.tasks" : [ {
+ "container.host" : "/default-rack/node1",
+ "container.start.ms" : 111822,
+ "container.end.ms" : 133985,
+ "container.priority" : 20,
+ "container.type" : "map"
+ }, {
+ "container.host" : "/default-rack/node2",
+ "container.start.ms" : 111788,
+ "container.end.ms" : 131377,
+ "container.priority" : 20,
+ "container.type" : "map"
+ } ]
+ }
+
+### Simulator input topology file format
+
+Here is an example input topology file which has 3 nodes organized in 1 rack.
+
+ {
+ "rack" : "default-rack",
+ "nodes" : [ {
+ "node" : "node1"
+ }, {
+ "node" : "node2"
+ }, {
+ "node" : "node3"
+ }]
+ }