Python Pool Analysis August 2016
It can be difficult to find the best method for parallel data processing. In Python, I came across six different options to run the same code concurrently, some truly in parallel and some interleaved as green threads. Overwhelmed with options, I decided to dig into the details of each pool and really test them.
I created a CLI to test each of the six different Python pool implementations for both CPU and I/O-bounded workloads. I have open sourced the CLI and my test results on GitHub so other developers can run their own tests or just skip the dirty details of benchmarking and use my own conclusions as a reference.