In this talk, I’ll take a deep dive into Spark’s performance on two benchmarks (TPC-DS and the Big Data Benchmark from UC Berkeley) and one production workload and demonstrate that many commonly-held beliefs about performance bottlenecks do not hold. In particular, I’ll demonstrate that CPU (and not I/O) is often the bottleneck, that network performance can improve job completion time by a median of at most 4%, and that the causes of most stragglers can be identified and fixed. After describing the takeaways from the workloads I studied, I’ll give a brief demo of how the (open-source) tools that I developed can be used by others to understand why Spark jobs are taking longer than expected. I’ll conclude by proposing changes to Spark core that, based on my performance study, could significantly improve performance. This talk is based on a research talk that I’ll be giving at NSDI 2015.
Kay Ousterhout is a Spark committer and PMC member and a PhD student at UC Berkeley. In the Spark project, Kay is a maintainer of the scheduler, and her work on Spark has focused on improving scheduler performance. At UC Berkeley, Kay’s research work centers around understanding and improving performance of large-scale analytics frameworks.