1 stringbench is a set of performance tests comparing byte string 2 operations with unicode operations. The two string implementations 3 are loosely based on each other and sometimes the algorithm for one is 4 faster than the other. 5 6 These test set was started at the Need For Speed sprint in Reykjavik 7 to identify which string methods could be sped up quickly and to 8 identify obvious places for improvement. 9 10 Here is an example of a benchmark 11 12 13 @bench('"Andrew".startswith("A")', 'startswith single character', 1000) 14 def startswith_single(STR): 15 s1 = STR("Andrew") 16 s2 = STR("A") 17 s1_startswith = s1.startswith 18 for x in _RANGE_1000: 19 s1_startswith(s2) 20 21 The bench decorator takes three parameters. The first is a short 22 description of how the code works. In most cases this is Python code 23 snippet. It is not the code which is actually run because the real 24 code is hand-optimized to focus on the method being tested. 25 26 The second parameter is a group title. All benchmarks with the same 27 group title are listed together. This lets you compare different 28 implementations of the same algorithm, such as "t in s" 29 vs. "s.find(t)". 30 31 The last is a count. Each benchmark loops over the algorithm either 32 100 or 1000 times, depending on the algorithm performance. The output 33 time is the time per benchmark call so the reader needs a way to know 34 how to scale the performance. 35 36 These parameters become function attributes. 37 38 39 Here is an example of the output 40 41 42 ========== count newlines 43 38.54 41.60 92.7 ...text.with.2000.newlines.count("\n") (*100) 44 ========== early match, single character 45 1.14 1.18 96.8 ("A"*1000).find("A") (*1000) 46 0.44 0.41 105.6 "A" in "A"*1000 (*1000) 47 1.15 1.17 98.1 ("A"*1000).index("A") (*1000) 48 49 The first column is the run time in milliseconds for byte strings. 50 The second is the run time for unicode strings. The third is a 51 percentage; byte time / unicode time. It's the percentage by which 52 unicode is faster than byte strings. 53 54 The last column contains the code snippet and the repeat count for the 55 internal benchmark loop. 56 57 The times are computed with 'timeit.py' which repeats the test more 58 and more times until the total time takes over 0.2 seconds, returning 59 the best time for a single iteration. 60 61 The final line of the output is the cumulative time for byte and 62 unicode strings, and the overall performance of unicode relative to 63 bytes. For example 64 65 4079.83 5432.25 75.1 TOTAL 66 67 However, this has no meaning as it evenly weights every test. 68 69
