Quickle Structs benchmark

bench.py

In [3]: from typing import NamedTuple

In [4]: from quickle import Struct

In [5]: from dataclasses import dataclass

In [6]: class PointTuple(NamedTuple):
   ...:     x: int
   ...:     y: int
   ...:

In [7]: class PointStruct(Struct):
   ...:     x: int
   ...:     y: int
   ...:

In [8]: @dataclass
   ...: class PointDataclass:
   ...:     x: int
   ...:     y: int
   ...:

In [9]: %timeit PointTuple(1, 2)  # namedtuples
270 ns ± 2.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [10]: %timeit PointDataclass(1, 2)  # dataclasses
262 ns ± 5.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [11]: %timeit PointStruct(1, 2)  # structs
97.9 ns ± 0.723 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [12]: import copy

In [13]: pt = PointTuple(1, 2)

In [14]: pd = PointDataclass(1, 2)

In [15]: ps = PointStruct(1, 2)

In [16]: %timeit copy.copy(pt)  # namedtuples
1.87 µs ± 13.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [17]: %timeit copy.copy(pd)  # dataclasses
1.93 µs ± 7.86 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [18]: %timeit copy.copy(ps)  # structs
365 ns ± 2.12 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

A quick benchmark of instantiating/copying different struct-like types in python. Quickle structs are faster to instantiate and copy than other equivalent options. Note that all are pretty speedy compared to other python operations, so this likely doesn't matter except for very specific workloads.