Spark Summit 2014 brought the Apache Spark community together on June 30- July 2, 2014 at the The Westin St. Francis in San Francisco. It featured production users of Spark, Shark, Spark Streaming and related projects.
In this talk I’ll describe Spark SQL, a new Alpha component that is part of the Spark 1.0 release. Spark SQL lets developers natively query data stored in both existing RDDs and external sources such as Apache Hive. A key feature of Spark SQL is the ability to blur the lines between relational tables and RDDs, making it easy for developers to intermix SQL commands that query external data with complex analytics. In addition to Spark SQL, I’ll also talk about the Catalyst optimizer framework, which allows Spark SQL to automatically rewrite query plans to execute more efficiently.
Michael Armbrust is the lead developer of the Spark SQL project at Databricks. He received his PhD from UC Berkeley in 2013, and was advised by Michael Franklin, David Patterson and Armando Fox. His thesis focused on building systems that allow developers to rapidly build scalable interactive applications, and specifically defined the notion of scale independence. His interests broadly include distributed systems, large-scale structured storage and query optimization.