As we countdown to the annual Lucene/Solr Revolution conference in Austin this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting LinkedIn engineers Diego Buthay and Sriram Sankar’s session on how they run their search architecture for the massive social network.

LinkedIn’s corpus is a richly structured professional graph comprised of 300M+ people, 3M+ companies, 2M+ groups, and 1.5M+ publishers. Members perform billions of searches, and each of those searches is highly personalized based on the searcher’s identity and relationships with other professional entities in LinkedIn’s economic graph. And all this data is in constant flux as LinkedIn adds more than 2 members every second in over 200 countries (2/3 of whom are outside the United States). As a result, we’ve built a system quite different from those used for other search applications. In this talk, we will discuss some of the unique systems challenges we’ve faced as we deliver highly personalized search over semi-structured data at massive scale.”

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Diego (“Mono”) Buthay is a staff engineer at LinkedIn, where he works on the back-end infrastructure for all of LinkedIn’s search products. Before that, he built the search-as-a-service platform at IndexTank, which LinkedIn acquired in 2011. He has BS and MS degrees in computer software engineering from the University of Buenos Aires. Sriram Sankar is a principal staff engineer at LinkedIn, where he leads the development of its next-generation search architecture. Before that, he led Facebook’s search quality efforts for Graph Search, and was a key contributor to Unicorn. He previously worked at Google on search quality and ads infrastructure. He is also the author of JavaCC, a leading parser generator for Java. Sriram has a PhD from Stanford University and a BS from IIT Kanpur.”

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