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Python benchmarking for humans and dragons

Project description

Python benchmarking for humans and dragons.

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Features

  • Unittest-style benchmark setup (TestCase -> BenchBatch)
  • setUp/tearDown are excluded from timing
  • Precise even for very fast benchmarks by running them for at least 1ms or 16 times, whichever takes longer
  • Timing down to the nanosecond
  • Benchmarks in a batch are run interleaved to reduce jitter from random load
  • Manual GC to prevent interference with the benchmarks
  • Results are saved into a human-readable json file and used as baseline for future tests
  • Just a few milliseconds overhead

Requirements

  • Python 2.6+ / 3.2+

Usage example

import nozdormu

class MyBenchBatch(nozdormu.BenchBatch):
    def bench_one(self):
        pass

    def bench_two(self):
        pass

class AnActualBenchBatch(nozdormu.BenchBatch):
    def setUp(self):
        import random
        self.r = random

    def bench_list_creation(self):
        l = []
        for i in range(100):
            l.append(i)

    def bench_random_addition(self):
        l = []
        for i in range(100):
            l.append(self.r.randint(0, 100))

    def bench_import_math(self):
        import math

if __name__ == '__main__':
    nozdormu.main()

yields

Starting benchmark session

  Running Batch: AnActualBenchBatch
    bench_random_addition: 152μs (2ms / 16 runs) (-6μs / 3.6%)
    bench_list_creation: 8μs (1ms / 127 runs) (-85ns / 1.1%)
    bench_import_math: 954ns (1ms / 1049 runs) (new)
  Batch finished, time: 12ms

  Running Batch: MyBenchBatch
    bench_one: 236ns (1ms / 4243 runs) (-13ns / 5.4%)
    bench_two: 232ns (1ms / 4305 runs) (-6ns / 2.7%)
  Batch finished, time: 9ms

Benchmarking finished
2 batches, 5 benchmarks
total time: 23ms

with some Cucumber-inspired colouring if your terminal supports that.

Usage

As you can see above, there are few things for you to do. The general structure is very similar to unittests. First import nozdormu, then subclass nozdormu.BenchBatch as often as you need to. Each batch can hold as many benchmarks as you need it to.

To get executed, benchmarks have to start with ‘bench’ (like unittests have to start with ‘test’), and just like in unittests, you can override the class methods setUp and tearDown for preparations and/or mocking. Both these functions are run before and after each benchmark execution and will be excluded from the benchmark timing (but included in the total time).

Benchmarks that take less than 1ms will be executed repeatedly until they accumulate at least 1ms of total runtime. This happens on a per-batch basis and the benchmarks of a batch will rotate until they all ran long enough. This should reduce jitter from other system load for these extremely fast benchmarks.

Acknowledgements

Ideas and inspiration by:

  • Python’s unittest and timeit modules
  • GRB’s readygo
  • Cucumber

Project details


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