spark-bench is an open-source benchmarking tool, and it’s also so much more.
spark-bench is a flexible system for simulating, comparing, testing, and benchmarking
Spark applications and Spark itself. spark-bench originally began as a benchmarking suite
to get timing numbers on very specific algorithms mostly in the machine learning domain.
Since then it has morphed into a highly configurable and flexible framework suitable for many use cases.
This talk will discuss the high level design and capabilities of spark-bench before
walking through some major, practical use cases. Use cases include, but are certainly not
- regression testing changes to Spark;
- comparing performance of different hardware and Spark tuning options;
- simulating multiple notebook users hitting a cluster at the same time;
- comparing parameters of a machine learning algorithm on the same set of data;
- providing insight into bottlenecks through use of compute-intensive and i/o-intensive workloads;
- and, yes, even benchmarking.
In particular this talk will address the use of spark-bench in developing new features features for Spark core.