This speeds up snap by splitting it into two cases, where market_ids is None or not.
If market_ids are specified then we can avoid construcing a list of all of self._caches.values(). To do this the walrus operator is used. This means that Python >= 3.8 is required, but that is already the oldest tested Python version.
If market_ids is None then we can avoid the conditional on market_ids being None or not on each iteration.
Benchmarks, repeat=5, number=5
┌──────────────────────────────────────────────────────────────────┬─────────┬─────────┬─────────┬────────────────┬─────────────────┬────────────────┐
│ Benchmark │ Min │ Max │ Mean │ Min (+) │ Max (+) │ Mean (+) │
├──────────────────────────────────────────────────────────────────┼─────────┼─────────┼─────────┼────────────────┼─────────────────┼────────────────┤
│ Snap Original vs New with specified market_ids and no contention │ 0.817 │ 0.830 │ 0.827 │ 0.226 (3.6x) │ 0.231 (3.6x) │ 0.228 (3.6x) │
│ Snap Original vs New with market_ids=None and no contention │ 0.360 │ 0.365 │ 0.363 │ 0.316 (1.1x) │ 0.323 (1.1x) │ 0.319 (1.1x) │
│ Snap Original vs New with market_ids=None and contention │ 0.361 │ 0.374 │ 0.367 │ 0.323 (1.1x) │ 0.342 (1.1x) │ 0.330 (1.1x) │
│ Snap Original vs New with specified market_ids and contention │ 0.708 │ 0.722 │ 0.711 │ 0.215 (3.3x) │ 0.219 (3.3x) │ 0.217 (3.3x) │
└──────────────────────────────────────────────────────────────────┴─────────┴─────────┴─────────┴────────────────┴─────────────────┴────────────────┘
This speeds up snap by splitting it into two cases, where market_ids is None or not.
If market_ids are specified then we can avoid construcing a list of all of self._caches.values(). To do this the walrus operator is used. This means that Python >= 3.8 is required, but that is already the oldest tested Python version.
If market_ids is None then we can avoid the conditional on market_ids being None or not on each iteration.
Benchmark:
https://gist.github.com/petedmarsh/71337df6a09d47e5f55d2f0d60e15cf7
Results (Python 3.11.3):