1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
|
from __future__ import annotations
from collections.abc import Callable
from pathlib import Path
import timeit
import pytomlpp
import qtoml
import rtoml
import toml
import tomlkit
import tomli
def benchmark(
name: str,
run_count: int,
func: Callable,
col_width: tuple,
compare_to: float | None = None,
) -> float:
placeholder = "Running..."
print(f"{name:>{col_width[0]}} | {placeholder}", end="", flush=True)
time_taken = timeit.timeit(func, number=run_count)
print("\b" * len(placeholder), end="")
time_suffix = " s"
print(f"{time_taken:{col_width[1]-len(time_suffix)}.3g}{time_suffix}", end="")
if compare_to is None:
print(" | baseline (100%)", end="")
else:
delta = compare_to / time_taken
print(f" | {delta:.2%}", end="")
print()
return time_taken
def run(run_count: int) -> None:
data_path = Path(__file__).parent / "data.toml"
test_data = data_path.read_bytes().decode()
# qtoml has a bug making it crash without this newline normalization
test_data = test_data.replace("\r\n", "\n")
col_width = (10, 10, 28)
col_head = ("parser", "exec time", "performance (more is better)")
print(f"Parsing data.toml {run_count} times:")
print("-" * col_width[0] + "---" + "-" * col_width[1] + "---" + col_width[2] * "-")
print(
f"{col_head[0]:>{col_width[0]}} | {col_head[1]:>{col_width[1]}} | {col_head[2]}"
)
print("-" * col_width[0] + "-+-" + "-" * col_width[1] + "-+-" + col_width[2] * "-")
# fmt: off
baseline = benchmark("rtoml", run_count, lambda: rtoml.loads(test_data), col_width) # noqa: E501
benchmark("pytomlpp", run_count, lambda: pytomlpp.loads(test_data), col_width, compare_to=baseline) # noqa: E501
benchmark("tomli", run_count, lambda: tomli.loads(test_data), col_width, compare_to=baseline) # noqa: E501
benchmark("toml", run_count, lambda: toml.loads(test_data), col_width, compare_to=baseline) # noqa: E501
benchmark("qtoml", run_count, lambda: qtoml.loads(test_data), col_width, compare_to=baseline) # noqa: E501
benchmark("tomlkit", run_count, lambda: tomlkit.parse(test_data), col_width, compare_to=baseline) # noqa: E501
# fmt: on
if __name__ == "__main__":
run(5000)
|
