Python日学壹技：性能分析
导读
相信日常使用Python作为生产力的读者，一定会存在想要分析代码中每一行的运行时间与变量占用内存大小的需求，本文主要分析两个模块，用于分析每行代码的内存使用情况和运行时间情况。
内存使用
- memory-profiler [1]
安装
pip install memory-profiler
使用方法一
- 在需要分析的函数上，添加装饰器@profile
@profile
def test1():
c=0
for item in xrange(100000):
c+=1
print (c)
- 使用下面的命令运行
python -m memory_profiler memory_profiler_test.py
使用方法二
from memory_profiler import profile
@profile(precision=4,stream=open('memory_profiler.log','w+'))
# @profile
def test1():
c=0
for item in xrange(100000):
c+=1
print c
# 直接运行即可
结果
Filename: memory_profiler_test.py
Line # Mem usage Increment Line Contents
================================================
5 21.492 MiB 21.492 MiB @profile
6 def test1():
7 21.492 MiB 0.000 MiB c=0
8 21.492 MiB 0.000 MiB for item in xrange(100000):
9 21.492 MiB 0.000 MiB c+=1
10 21.492 MiB 0.000 MiB print c
- Mem usage: 内存占用情况
- Increment: 执行该行代码后新增的内存
运行时间
- line-profiler [2]
安装
pip install line-profiler
使用
- 在需要分析的函数上，添加装饰器@profile
@profile
def slow_function(a, b, c):
...
- 运行
python -m line_profiler script_to_profile.py.lprof
结果
Pystone(1.1) time for 50000 passes = 2.48
This machine benchmarks at 20161.3 pystones/second
Wrote profile results to pystone.py.lprof
Timer unit: 1e-06 s
File: pystone.py
Function: Proc2 at line 149
Total time: 0.606656 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
149 @profile
150 def Proc2(IntParIO):
151 50000 82003 1.6 13.5 IntLoc = IntParIO + 10
152 50000 63162 1.3 10.4 while 1:
153 50000 69065 1.4 11.4 if Char1Glob == 'A':
154 50000 66354 1.3 10.9 IntLoc = IntLoc - 1
155 50000 67263 1.3 11.1 IntParIO = IntLoc - IntGlob
156 50000 65494 1.3 10.8 EnumLoc = Ident1
157 50000 68001 1.4 11.2 if EnumLoc == Ident1:
158 50000 63739 1.3 10.5 break
159 50000 61575 1.2 10.1 return IntParIO
- 每列含义
> - Line #: The line number in the file.
> - Hits: The number of times that line was executed.
> - Time: The total amount of time spent executing the line in the timer's units. In the header information before the tables, you will see a line "Timer unit:" giving the conversion factor to seconds. It may be different on different systems.
> - Per Hit: The average amount of time spent executing the line once in the timer's units.
> - % Time: The percentage of time spent on that line relative to the total amount of recorded time spent in the function.
> - Line Contents: The actual source code. Note that this is always read from disk when the formatted results are viewed, *not* when the code was executed. If you have edited the file in the meantime, the lines will not match up, and the formatter may not even be able to locate the function for display.
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参考资料
[1]memory-profiler: https://pypi.org/project/memory-profiler/
[2]line-profiler: https://github.com/rkern/line_profiler
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