[Hadoop Wiki] Trivial Update of "Hbase/PoweredBy" by JimKellerman

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The following page has been changed by JimKellerman:
http://wiki.apache.org/hadoop/Hbase/PoweredBy

The comment on the change is:
Escape some words that look like Wiki words but are not.

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- [http://www.mahalo.com Mahalo], "...the world's first human-powered search engine". All the markup that powers the wiki is stored in HBase. It's been in use for a few months now. MediaWiki - the same software that power Wikipedia - has version/revision control. Mahalo's in-house editors produce a lot of revisions per day, which was not working well in a RDBMS. An hbase-based solution for this was built and tested, and the data migrated out of MySQL and into HBase. Right now it's at something like 6 million items in HBase. The upload tool runs every hour from a shell script to back up that data, and on 6 nodes takes about 5-10 minutes to run - and does not slow down production at all. 
+ [http://www.mahalo.com Mahalo], "...the world's first human-powered search engine". All the markup that powers the wiki is stored in HBase. It's been in use for a few months now. !MediaWiki - the same software that power Wikipedia - has version/revision control. Mahalo's in-house editors produce a lot of revisions per day, which was not working well in a RDBMS. An hbase-based solution for this was built and tested, and the data migrated out of MySQL and into HBase. Right now it's at something like 6 million items in HBase. The upload tool runs every hour from a shell script to back up that data, and on 6 nodes takes about 5-10 minutes to run - and does not slow down production at all. 
  
  [http://www.powerset.com/ Powerset (a Microsoft company)] uses HBase to store raw documents.  We have a ~70 node hadoop cluster running DFS, mapreduce, and hbase.  In our wikipedia hbase table, we have one row for each wikipedia page (~2.5M pages and climbing).  We use this as input to our indexing jobs, which are run in hadoop mapreduce.  Uploading the entire wikipedia dump to our cluster takes a couple hours.  Scanning the table inside mapreduce is very fast -- the latency is in the noise compared to everything else we do.
  
- [http://www.tokenizer.org Shopping Engine at Tokenizer] is a web crawler; it uses HBase to store URLs and Outlinks (AnchorText + LinkedURL): more than a billion. It was initially designed as Nutch-Hadoop extension, then (due to very specific 'shopping' scenario) moved to SOLR + MySQL(InnoDB) (ten thousands queries per second), and now - to HBase. HBase is significantly faster due to: no need for huge transaction logs, column-oriented design exactly matches 'lazy' business logic, data compression, MapReduce support. Number of mutable 'indexes' (term from RDBMS) significantly reduced due to the fact that each 'row::column' structure is physically sorted by 'row'. MySQL InnoDB engine is best DB choice for highly-concurrent updates. However, necessity to flash a block of data to harddrive even if we changed only few bytes is obvious bottleneck. HBase greatly helps: not-so-popular in modern DBMS 'delete-insert', 'mutable primary key', and 'natural primary key' patterns become a 
 big advantage with HBase.
+ [http://www.tokenizer.org Shopping Engine at Tokenizer] is a web crawler; it uses HBase to store URLs and Outlinks (!AnchorText + LinkedURL): more than a billion. It was initially designed as Nutch-Hadoop extension, then (due to very specific 'shopping' scenario) moved to SOLR + MySQL(InnoDB) (ten thousands queries per second), and now - to HBase. HBase is significantly faster due to: no need for huge transaction logs, column-oriented design exactly matches 'lazy' business logic, data compression, !MapReduce support. Number of mutable 'indexes' (term from RDBMS) significantly reduced due to the fact that each 'row::column' structure is physically sorted by 'row'. MySQL InnoDB engine is best DB choice for highly-concurrent updates. However, necessity to flash a block of data to harddrive even if we changed only few bytes is obvious bottleneck. HBase greatly helps: not-so-popular in modern DBMS 'delete-insert', 'mutable primary key', and 'natural primary key' patterns become 
 a big advantage with HBase.
  
- [http://trendmicro.com/ Trend Micro] Advanced Threats Research is running Hadoop 0.18.1 and HBase 0.18.0. Our application is a web crawling application with concurrent batch content analysis of various kinds. All of the workflow components are implemented as subclasses of TableMap and/or TableReduce on a cluster of 25 nodes. We see a constant rate of 2500 requests/sec or greater, peaking periodically near 100K/sec when some of the batch scan tasks run.
+ [http://trendmicro.com/ Trend Micro] Advanced Threats Research is running Hadoop 0.18.1 and HBase 0.18.0. Our application is a web crawling application with concurrent batch content analysis of various kinds. All of the workflow components are implemented as subclasses of !TableMap and/or !TableReduce on a cluster of 25 nodes. We see a constant rate of 2500 requests/sec or greater, peaking periodically near 100K/sec when some of the batch scan tasks run.
  
  [http://www.videosurf.com/ VideoSurf] - "The video search engine that has taught computers to see". We're using Hbase to persist various large graphs of data and other statistics. Hbase was a real win for us because it let us store substantially larger datasets without the need for manually partitioning the data and it's column-oriented nature allowed us to create schemas that were substantially more efficient for storing and retrieving data.