example01.py
import cProfile
def is_prime(num):
for factor in range(2, int(num ** 0.5) + 1):
if num % factor == 0:
return False
return True
class PrimeIter:
def __init__(self, total):
self.counter = 0
self.current = 1
self.total = total
def __iter__(self):
return self
def __next__(self):
if self.counter < self.total:
self.current += 1
while not is_prime(self.current):
self.current += 1
self.counter += 1
return self.current
raise StopIteration()
cProfile.run('list(PrimeIter(10000))')执行结果：
114734 function calls in 0.573 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.006 0.006 0.573 0.573 <string>:1(<module>)
1 0.000 0.000 0.000 0.000 example.py:14(__init__)
1 0.000 0.000 0.000 0.000 example.py:19(__iter__)
10001 0.086 0.000 0.567 0.000 example.py:22(__next__)
104728 0.481 0.000 0.481 0.000 example.py:5(is_prime)
1 0.000 0.000 0.573 0.573 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
####line_profiler
给需要剖析时间性能的函数加上一个profile装饰器，这个函数每行代码的执行次数和时间都会被剖析。
example02.py
@profile
def is_prime(num):
for factor in range(2, int(num ** 0.5) + 1):
if num % factor == 0:
return False
return True
class PrimeIter:
def __init__(self, total):
self.counter = 0
self.current = 1
self.total = total
def __iter__(self):
return self
def __next__(self):
if self.counter < self.total:
self.current += 1
while not is_prime(self.current):
self.current += 1
self.counter += 1
return self.current
raise StopIteration()
list(PrimeIter(1000))安装和使用line_profiler三方库。
pip install line_profiler
kernprof -lv example.py
Wrote profile results to example02.py.lprof
Timer unit: 1e-06 s
Total time: 0.089513 s
File: example02.py
Function: is_prime at line 1
# Hits Time Per Hit % Time Line Contents
==============================================================
1 @profile
2 def is_prime(num):
3 86624 43305.0 0.5 48.4 for factor in range(2, int(num ** 0.5) + 1):
4 85624 42814.0 0.5 47.8 if num % factor == 0:
5 6918 3008.0 0.4 3.4 return False
6 1000 386.0 0.4 0.4 return True####memory_profiler
给需要剖析内存性能的函数加上一个profile装饰器，这个函数每行代码的内存使用情况都会被剖析。
example03.py
@profile
def eat_memory():
items = []
for _ in range(1000000):
items.append(object())
return items
eat_memory()安装和使用memory_profiler三方库。
pip install memory_profiler
python3 -m memory_profiler example.py
Filename: example03.py
Line # Mem usage Increment Line Contents
================================================
1 38.672 MiB 38.672 MiB @profile
2 def eat_memory():
3 38.672 MiB 0.000 MiB items = []
4 68.727 MiB 0.000 MiB for _ in range(1000000):
5 68.727 MiB 1.797 MiB items.append(object())
6 68.727 MiB 0.000 MiB return items