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Comment by laurencerowe | Hacker News Reader

For algorithmic code PyPy can provide substantial speedups over CPython. I've used PyPy in code fingerprinting large bioinformatics files and seen big speedups. I've also tried porting a webapp processing JSON from CPython and seen no perceptible speedup.

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The JSON library is probably a C-extension so PyPy won't make it any faster.

It's a long time since I looked, but profiling my code not much time was spent parsing / serializing JSON. Most of the time spent was manipulating dicts/lists in Python which cPython is already pretty good at since the whole language seems to basically be implemented in terms of dicsts. I don't think PyPy has the hidden class optimizations of JS engines which are able to find speedups in these types of cases.

Apparently that isn't always the case. See the PyPy Status Blog: PyPy's new JSON parser https://morepypy.blogspot.com/2019/10/pypys-new-json-parser.... which talks about being more efficient with both deserialization and memory.

For algorithmic/numerical code, especially if you have to deal with numpy-related data, Numba has a much easier barrier for entry, plus you remain with cpython while speeding up computation-intensive code by a few orders of magnitude.

Looks like numba has cffi support now so it would be an option. If I can dig out the code (it was about 5 years ago) I'll probably try adapting it to numba to benchmark it against pypy.

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