"network performance" entries

For many who use and deploy Apache Spark, knowing how to find critical bottlenecks is extremely important. In a recent O’Reilly webcast, Making Sense of Spark Performance, Spark committer and PMC member Kay Ousterhout gave a brief overview of how Spark works, and dove into how she measured performance bottlenecks using new metrics, including block-time analysis. Ousterhout walked through high-level takeaways from her in-depth analysis of several workloads, and offered a live demo of a new performance analysis tool and explained how you can use it to improve your Spark performance.

Her research uncovered surprising insights into Spark’s performance on two benchmarks (TPC-DS and the Big Data Benchmark), and one production workload. As part of our overall series of webcasts on big data, data science, and engineering, this webcast debunked commonly held ideas surrounding network performance, showing that CPU — not I/O — is often a critical bottleneck, and demonstrated how to identify and fix stragglers.

Network performance is almost irrelevant

While there’s been a lot of research work on performance — mainly surrounding the issues of whether to cache input data in-memory or on machine, scheduling, straggler tasks, and network performance — there haven’t been comprehensive studies into what’s most important to performance overall. This is where Ousterhout’s research comes in — taking on what she refers to as “community dogma,” beginning with the idea that network and disk I/O are major bottlenecks. Read more…