Correctness and Performance of Apache Spark SQL

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In this talk, we present a comprehensive framework we developed at Databricks for assessing the correctness, stability, and performance of our Spark SQL engine. Apache Spark is one of the most actively developed open source projects, with more than 1200 contributors from all over the world. At this scale and pace of development, mistakes bound to happen. We will discuss various approaches we take, including random query generation, random data generation, random fault injection, and longevity stress tests. We will demonstrate the effectiveness of the framework by highlighting several correctness issues we have found through random query generation and critical performance regressions we were able to diagnose within hours due to our automated benchmarking tools.

Session hashtag: #SAISDev10



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About Bogdan Ghit

Bogdan Ghit is a computer scientist and software engineer at Databricks, where he works on optimizing the SQL performance of Apache Spark. Prior to joining Databricks, Bogdan pursued his PhD at Delft University of Technology where he worked broadly on datacenter scheduling with a focus on data analytics frameworks such as Hadoop and Spark. His thesis has led to a large number of publications in top conferences such as ACM Sigmetrics and ACM HPDC.

About Nicolas Poggi

Nicolas is a researcher overseeing the performance and scalability of new Spark releases at Databricks. Where he along with the Amsterdam SQL performance team is implementing the new benchmarking and monitoring infrastructure for the Databricks cloud platform. Previously, he was leading a project on upcoming architectures for Big Data processing at the Barcelona Supercomputing (BSC) - Microsoft Research joint center. Nicolas received his Ph.D. in Distributed Systems and Computer Architecture at UPC/BarcelonaTech, where he is still contributing part of the HPC and of the Data Centric Computing research groups.