Mercurial > cpython-lru_cache-weakref

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Add use_weakref option to functools.lru_cache The use_weakref option makes lru_cache create weak references to the function arguments when creating the cache key, which prevents them from staying in the memory forever even after all other references to them are gone. This is a proof of concept for http://bugs.python.org/issue19859
author Radomir Dopieralski <devel@sheep.art.pl>
date Tue, 03 Dec 2013 16:02:48 +0100
parents 704630a9c5d5
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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1 stringbench is a set of performance tests comparing byte string
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2 operations with unicode operations. The two string implementations
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3 are loosely based on each other and sometimes the algorithm for one is
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4 faster than the other.
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5
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6 These test set was started at the Need For Speed sprint in Reykjavik
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7 to identify which string methods could be sped up quickly and to
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8 identify obvious places for improvement.
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9
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10 Here is an example of a benchmark
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13 @bench('"Andrew".startswith("A")', 'startswith single character', 1000)
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14 def startswith_single(STR):
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15 s1 = STR("Andrew")
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16 s2 = STR("A")
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17 s1_startswith = s1.startswith
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18 for x in _RANGE_1000:
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19 s1_startswith(s2)
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21 The bench decorator takes three parameters. The first is a short
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22 description of how the code works. In most cases this is Python code
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23 snippet. It is not the code which is actually run because the real
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24 code is hand-optimized to focus on the method being tested.
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26 The second parameter is a group title. All benchmarks with the same
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27 group title are listed together. This lets you compare different
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28 implementations of the same algorithm, such as "t in s"
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29 vs. "s.find(t)".
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31 The last is a count. Each benchmark loops over the algorithm either
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32 100 or 1000 times, depending on the algorithm performance. The output
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33 time is the time per benchmark call so the reader needs a way to know
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34 how to scale the performance.
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35
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36 These parameters become function attributes.
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39 Here is an example of the output
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42 ========== count newlines
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43 38.54 41.60 92.7 ...text.with.2000.newlines.count("\n") (*100)
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44 ========== early match, single character
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45 1.14 1.18 96.8 ("A"*1000).find("A") (*1000)
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46 0.44 0.41 105.6 "A" in "A"*1000 (*1000)
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47 1.15 1.17 98.1 ("A"*1000).index("A") (*1000)
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49 The first column is the run time in milliseconds for byte strings.
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50 The second is the run time for unicode strings. The third is a
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51 percentage; byte time / unicode time. It's the percentage by which
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52 unicode is faster than byte strings.
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54 The last column contains the code snippet and the repeat count for the
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55 internal benchmark loop.
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56
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57 The times are computed with 'timeit.py' which repeats the test more
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58 and more times until the total time takes over 0.2 seconds, returning
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59 the best time for a single iteration.
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61 The final line of the output is the cumulative time for byte and
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62 unicode strings, and the overall performance of unicode relative to
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63 bytes. For example
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65 4079.83 5432.25 75.1 TOTAL
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66
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67 However, this has no meaning as it evenly weights every test.
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68