Apache Impala (incubating) is an exceptional, best-of-breed massively parallel processing SQL query engine that is a fundamental component of the big data software stack. However, Impala is a complex engine and requires a thorough technical understanding to utilize it fully. When Impala is improperly configured or used, it may use too many resources, and performance could be very poor.
For many users, understanding Impala query performance is like a trip on the mystery bus. Impala provides a query plan and query profile to help users choose an optimal plan and understand how a query is executed and how many resources it uses. But digging through query profiles isn’t fun for everyone. Juan Yu demystifies the cost model Impala Planner uses and how Impala optimizes queries and explains how to identify performance bottleneck through query plan and profile and how to drive Impala to its full potential.
Juan Yu is a software engineer at Cloudera working on the Impala project, where she helps customers investigate, troubleshoot, and resolve escalations and analyzes performance issues to identify bottlenecks, failure points, and security holes. Juan also implements enhancements in Impala to improve customer experience. Previously, Juan was a software engineer at Interactive Intelligence and held developer positions at Bluestreak, Gameloft, and Engenuity.
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