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Improve performances of uuid.* functions #128150

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picnixz opened this issue Dec 21, 2024 · 0 comments
Open

Improve performances of uuid.* functions #128150

picnixz opened this issue Dec 21, 2024 · 0 comments
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performance Performance or resource usage stdlib Python modules in the Lib dir type-feature A feature request or enhancement

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@picnixz
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picnixz commented Dec 21, 2024

Feature or enhancement

The dedicated UUID constructors (e.g., uuid.uuid4()) generate bytes and pass them to the UUID constructor. However, the latter performs multiple and redundant checks. We can by-pass those checks since we are actually creating manually the UUID object. Here are the benchmarks for a PGO and python -OO (no LTO) build and a dedicated UUID.from_int constructor:

+----------------------------------------+---------+-----------------------+
| Benchmark                              | ref     | final                 |
+========================================+=========+=======================+
| uuid1(node, None)                      | 1.20 us | 1.16 us: 1.04x faster |
+----------------------------------------+---------+-----------------------+
| uuid1(None, clock_seq)                 | 1.16 us | 1.14 us: 1.02x faster |
+----------------------------------------+---------+-----------------------+
| uuid3(NAMESPACE_DNS, os.urandom(16))   | 1.13 us | 809 ns: 1.40x faster  |
+----------------------------------------+---------+-----------------------+
| uuid3(NAMESPACE_DNS, os.urandom(1024)) | 2.08 us | 1.73 us: 1.20x faster |
+----------------------------------------+---------+-----------------------+
| uuid4()                                | 1.16 us | 885 ns: 1.31x faster  |
+----------------------------------------+---------+-----------------------+
| uuid5(NAMESPACE_DNS, os.urandom(16))   | 1.15 us | 832 ns: 1.39x faster  |
+----------------------------------------+---------+-----------------------+
| uuid5(NAMESPACE_DNS, os.urandom(1024)) | 1.57 us | 1.27 us: 1.24x faster |
+----------------------------------------+---------+-----------------------+
| uuid8()                                | 952 ns  | 694 ns: 1.37x faster  |
+----------------------------------------+---------+-----------------------+
| Geometric mean                         | (ref)   | 1.21x faster          |
+----------------------------------------+---------+-----------------------+

Benchmark hidden because not significant (1): uuid1()

The above benchmarks keep constants as is since constant folding would remove the inefficiency of recomputing 1 << const everytime. With a hardcoded 1 << const, the numbers are (almost) identical.

I did not change the UUIDv1 generation because I observed that it would be worse in the uuid.uuid1() form (but 50% faster when either the node or the clock sequence is given, but this is likely not the usual call form).

Benchmark script
import os
import random
import uuid

import pyperf

if __name__ == '__main__':
    runner = pyperf.Runner()
    runner.bench_func('uuid1()', uuid.uuid1)
    node = random.getrandbits(48)
    runner.bench_func('uuid1(node, None)', uuid.uuid1, node)
    clock_seq = random.getrandbits(14)
    runner.bench_func('uuid1(None, clock_seq)', uuid.uuid1, None, clock_seq)

    ns = uuid.NAMESPACE_DNS
    runner.bench_func('uuid3(NAMESPACE_DNS, os.urandom(16))',
                      uuid.uuid3, ns, os.urandom(16))
    runner.bench_func('uuid3(NAMESPACE_DNS, os.urandom(1024))',
                      uuid.uuid3, ns, os.urandom(1024))

    runner.bench_func('uuid4()', uuid.uuid4)

    ns = uuid.NAMESPACE_DNS
    runner.bench_func('uuid5(NAMESPACE_DNS, os.urandom(16))',
                      uuid.uuid5, ns, os.urandom(16))
    runner.bench_func('uuid5(NAMESPACE_DNS, os.urandom(1024))',
                      uuid.uuid5, ns, os.urandom(1024))

    runner.bench_func('uuid8()', uuid.uuid8)

I'll submit a PR and we can decide what to keep and what to remove for maintainibility purposes. Note that the uuid module has been improved a lot performance-wise especially in terms of import time but I believe that constructing UUIDs objects via their dedicated functions.

Linked PRs

@picnixz picnixz added type-feature A feature request or enhancement performance Performance or resource usage stdlib Python modules in the Lib dir labels Dec 21, 2024
@picnixz picnixz self-assigned this Dec 21, 2024
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Labels
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