python性能测量工具cProfile使用解析

cProfile -s tottime your_program.py

ncalls tottime percall cumtime percall filename:lineno(function)
  66  0.001  0.000  11.850  0.180 base.py:228(micro_service)
  66  0.003  0.000  11.849  0.180 tools.py:557(micro_service)
 1056  0.001  0.000  11.073  0.010 connection.py:463(drain_events)
 1056  0.015  0.000  11.072  0.010 connection.py:466(blocking_read)
 1056  0.008  0.000  10.920  0.010 transport.py:233(read_frame)
 3168  0.014  0.000  10.908  0.003 transport.py:370(_read)
 3168  10.892  0.003  10.892  0.003 {method 'recv' of '_socket.socket' objects}
  66  0.001  0.000  9.814  0.149 rpc.py:350(__call__)
  66  0.001  0.000  8.395  0.127 rpc.py:329(result)

import cProfile
cp = cProfile.Profile()
cp.enable()
YOUR CODE
cp.disable()
cp.print_stats()

pip install line_profiler

@profile
def class_name()
	pass

kernprof -l -v your_code.py
-l 逐行分析
-v 立即查看结果

from cProfile import Profile as profile 
from pstats import Stats 
def (): 
	p = profile() 
	p.snapshot_stats() 
	p.enable() 
	
	p.disable() 
	p.print_stats(2) # 按照调用累加总耗时累加排序，即将最耗时的函数最优先
	p.dump_stats("call.log")