Avoiding Deadlocks: Lessons Learned with Zephyr Health Using Neo4j and MongoDB

Zephyr Health provides analytics and visualization within our cloud-based Zephyr Analytics Platform – so our customers can optimize their market-shaping efforts. This platform is an innovative, powerful, and complex platform that transforms heterogenous data via research, integration, and modeling. The platform stores data as JSON documents in MongoDB and generates a sparse representation of the same in a Neo4j graph database.

In this presentation, Mahesh will discuss how he tackled deadlocks in relationship-creation and improved the performance of the system significantly. The proof-of-concept includes small graphs (ranging up to 10,000 relationships) and very large graphs (up to 39 million relationships). By addressing the deadlocks, Mahesh was able to increase the average performance of the system to 3,741 relationships per minute.

Dr. Mahesh Chaudhari is sr. software engineer at Zephyr Health Inc. since September 2012. His primary responsibilities involve data modeling and integration, ontology development, algorithm design and incorporation into the Z-Platform. He has a Ph.D. in Computer Science from Arizona State University where he has focused on incremental view maintenance of materialized views defined over loosely-coupled heterogeneous data sources. He has extensive research experience in relational, object-relational, XML databases with query processing and optimization. He has one principal NSF grant at ASU since 2008 and one supplemental grant from NSF since 2011 supporting undergraduate research in database curriculum, benchmark and performance evaluation. He has 2 years of teaching experience and is a recipient of PFF (Preparing Future Faculty) Emeriti Fellow for the academic year of 2009-2010 for excellence in research, teaching & mentorship.