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Real-Time Searching of Big Data with Solr and Hadoop

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Hadoop and HBase make it easy to store terabytes of data, but how do you scale your search mechanism to sift through these mountains of bits and retrieve large result sets in a matter of milliseconds? ...

Hadoop and HBase make it easy to store terabytes of data, but how do you scale your search mechanism to sift through these mountains of bits and retrieve large result sets in a matter of milliseconds? Solr sharding and careful index creation made these requirements come to life in our production environment. This presentation discusses how we handle millions of rapid fire queries from dozens of parallel search clients against many terabytes of data while addressing high availability through load balancing and replication. Originally presented by Rod Cope, Founder and CTO of OpenLogic, at the 2010 Lucene Revolution Conference in Boston.

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Real-Time Searching of Big Data with Solr and Hadoop Presentation Transcript