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Jeong, YunWon
eb1546ae08
* posix: add os.forkpty pty.fork prefers forkpty so a nested spawn is a new session with its own controlling tty. The openpty+login_tty fallback hung the jsontests collector. Assisted-by: Claude * select: rebuild fd sets after EINTR select(2) updates the sets in place. Retrying an interrupted call with empty sets and a NULL timeout blocks forever. Assisted-by: Claude * http.server: keep CGI off the body-read timeout A millisecond handler timeout races the client thread writing the request and is inherited across CGI fork, which RSTs the client under load. Assisted-by: Claude * Revert "http.server: keep CGI off the body-read timeout" Lib edits outside a CPython copy are not allowed. Assisted-by: Claude * host_env: note why forkpty is libc not nix Assisted-by: Claude * benches: run deepcopy 10x more iterations Assisted-by: Claude
73 lines
1.5 KiB
Python
73 lines
1.5 KiB
Python
# Adapted from python/pyperformance 1.14.0 (deepcopy).
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"""
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Benchmark to measure performance of the python builtin method copy.deepcopy
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Performance is tested on a nested dictionary and a dataclass
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Author: Pieter Eendebak
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"""
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import copy
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from dataclasses import dataclass
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@dataclass
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class A:
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string: str
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lst: list
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boolean: bool
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def benchmark_reduce(n):
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""" Benchmark where the __reduce__ functionality is used """
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class C(object):
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def __init__(self):
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self.a = 1
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self.b = 2
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def __reduce__(self):
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return (C, (), self.__dict__)
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def __setstate__(self, state):
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self.__dict__.update(state)
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c = C()
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for _ in range(n):
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_ = copy.deepcopy(c)
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def benchmark_memo(n):
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""" Benchmark where the memo functionality is used """
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A = [1] * 100
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data = {'a': (A, A, A), 'b': [A] * 100}
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for _ in range(n):
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_ = copy.deepcopy(data)
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def benchmark(n):
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""" Benchmark on some standard data types """
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a = {
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'list': [1, 2, 3, 43],
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't': (1 ,2, 3),
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'str': 'hello',
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'subdict': {'a': True}
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}
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dc = A('hello', [1, 2, 3], True)
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for _ in range(n):
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for _ in range(30):
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_ = copy.deepcopy(a)
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for s in ['red', 'blue', 'green']:
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dc.string = s
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for value in range(5):
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dc.lst[0] = value
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for b in [True, False]:
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dc.boolean = b
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_ = copy.deepcopy(dc)
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N = 10
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benchmark(N)
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benchmark_reduce(5 * N)
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benchmark_memo(N)
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