As we countdown to the annual Lucene/Solr Revolution conference in Boston this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting Shenghua Wan’s talk, “Solr Distributed Indexing at WalmartLabs”.
As a retail giant, Walmart provides millions of items’ information via its e-commerce websites, and the number grows quickly. This calls for big data technologies to index the documents. Map-Reduce framework is a scalable and high-available base on top of which the distributed indexing can be built. While original Solr has a map-reduce index tool, there exist some barriers which makes it unable to deal with Walmart’s use case easily and efficiently. In this case study, Shenghua demonstrates a way to build your own distributed indexing tool and optimize the performance by making the indexing stage a map-only job before they are merged.
Shenghua Wan is a Senior Software Engineer on the Polaris Search Team at WalmartLabs. His focus is applying big data technologies to deal with large-scale product information to be searched online.
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