Organizations continue to adopt Solr because of its ability to scale to meet even the most demanding workflows. Recently, Lucidworks has been leading the effort to identify, measure, and expand the limits of Solr. As part of this effort, we've learned a few things along the way that should prove useful for any organization wanting to scale Solr. Specifically, Tim will present design patterns and processes we've found to be useful when scaling Solr, as well as some anti-patterns that should be avoided. Attendees will come away with a better understanding of what works and what doesn't when building a large-scale search application using Solr.

Timothy Potter is a senior member of the engineering team at Lucidworks and a committer on the Apache Solr project. Tim focuses on scalability and hardening the distributed features in Solr. Previously, Tim was an architect on the Big Data team at Dachis Group, where he worked on large-scale machine learning, text mining, and social network analysis problems using Hadoop, Cassandra, and Storm. Tim is the co-author of Solr In Action, a comprehensive guide to using Solr 4. Mr. Potter holds several US Patents related to J2EE-based enterprise application integration. He lives with his two Shiba Inus in the mountains outside Denver, CO.