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The "Hbase/PoweredBy" page has been changed by AndrewPurtell. http://wiki.apache.org/hadoop/Hbase/PoweredBy?action=diff&rev1=37&rev2=38 -------------------------------------------------- [[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]] uses HBase as a foundation for cloud scale storage for a variety of applications. We have been developing with HBase since version 0.1 and production since version 0.20.0. [[http://www.veoh.com/|Veoh Networks]] uses HBase to store and process visitor(human) and entity(non-human) profiles which are used for behavioral targeting, demographic detection, and personalization services. Our site reads this data in real-time (heavily cached) and submits updates via various batch map/reduce jobs. With 25 million unique visitors a month storing this data in a traditional RDBMS is not an option. We currently have a 24 node Hadoop/HBase cluster and our profiling system is sharing this cluster with our other Hadoop data pipeline processes.
