CheeseShop VS dtparse

By comparison, PyO3 handles virtually all that boilerplate, so your Rust functions can accept and return many native Rust types and everything just works (for example). Or maybe I'm missing some fundamental difference with how JVM data are handled versus Python.

At work, I'm using PyO3 for a project that churns through a lot of data (step 1) and does some pattern mining (step 2). This is the second generation of the project and is on-demand compared with the large, batch project in Spark that it is replacing. The Rust+Python project has really good performance, and using Rust for the core logic is such a joy compared with Scala or Python that a lot of other pieces are written in.

Learning PyO3, I cobbled together a sample project[0] to demonstrate how some functionality works. It's a little outdated (uses PyO3 0.11.0 compared with the current 0.13.1) and doesn't show everything, but I think it's reasonably clear.

One thing I noticed is that passing very large data from Rust and into Python's memory space is a bit of a challenge. I haven't quite grokked who owns what when and how memory gets correctly dropped, but I think the issues I've had are with the amount of RAM used at any moment and not with any memory leaks.

[0] https://github.com/aeshirey/CheeseShop

ciso8601 is blazingly fast, and also its wall time is very stable. By all means, use ciso8601 if the format allows :)

On my machine, ciso8601 always runs in 240ns, and the Rust lib median time is 1250ns.

You can run a benchcmark too! Just call pytest, and it will generate an .svg report: https://github.com/gukoff/dtparse/blob/master/tests/test_per...