Spark Autotuning: Spark Summit East talk by: Lawrence Spracklen

While the performance delivered by Spark has enabled data scientists to undertake sophisticated analyses on big and complex data in actionable timeframes, too often, the process of manually configuring the underlying Spark jobs (including the number and size of the executors) can be a significant and time consuming undertaking. Not only it does this configuration process typically rely heavily on repeated trial-and-error, it necessitates that data scientists have a low-level understanding of Spark and detailed cluster sizing information. At Alpine Data we have been working to eliminate this requirement, and develop algorithms that can be used to automatically tune Spark jobs with minimal user involvement,
In this presentation, we discuss the algorithms we have developed and illustrate how they leverage information about the size of the data being analyzed, the analytical operations being used in the flow, the cluster size, configuration and real-time utilization, to automatically determine the optimal Spark job configuration for peak performance.