python 内存监控模块之memory_profiler

0. memory_profiler是干嘛的

This is a python module for monitoring memory consumption of a process as well as line-by-line analysis of memory consumption for python programs. It is a pure python module and has the psutil module as optional (but highly recommended) dependencies.

memory_profiler是监控python进程的神器，它可以分析出每一行代码所增减的内存状况。

1. 入门例子

#del3.py

import time
@profile
def my_func():
a = [1] * (10 ** 6)
b = [2] * (2 * 10 ** 7)
time.sleep(10)
del b
del a
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

$python -m memory_profiler del3.py
+++++++++
Filename: del3.py

Line #    Mem usage    Increment   Line Contents
================================================
2   10.293 MiB    0.000 MiB   @profile
3                             def my_func():
4   17.934 MiB    7.641 MiB       a = [1] * (10 ** 6)
5  170.523 MiB  152.590 MiB       b = [2] * (2 * 10 ** 7)
6  170.527 MiB    0.004 MiB       time.sleep(10)
7   17.938 MiB -152.590 MiB       del b
8   10.305 MiB   -7.633 MiB       del a
9   10.309 MiB    0.004 MiB       print "+++++++++"

代码执行一遍，然后给出具体代码在某一步占用的内存，通过内存加减可以看出某个对象的大小。

2. 对象不删除，直接赋值内存是否会继续增长

#对比1

@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
a = 'a' * 1024 * 1024
a = 'a' * 1024
del a
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

Line #    Mem usage    Increment   Line Contents
================================================
1   10.293 MiB    0.000 MiB   @profile
2                             def my_func():
3 1034.301 MiB 1024.008 MiB       a = 'a' * 1024 * 1024 * 1024;
4   11.285 MiB -1023.016 MiB       a = 'a' * 1024 * 1024
5   11.285 MiB    0.000 MiB       a = 'a' * 1024
6   11.285 MiB    0.000 MiB       del a
7   11.289 MiB    0.004 MiB       print "+++++++++"

#对比2

@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
del a
a = 'a' * 1024 * 1024
del a
a = 'a' * 1024
del a
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

Line #    Mem usage    Increment   Line Contents
================================================
1   10.293 MiB    0.000 MiB   @profile
2                             def my_func():
3 1034.301 MiB 1024.008 MiB       a = 'a' * 1024 * 1024 * 1024;
4   10.297 MiB -1024.004 MiB       del a
5   11.285 MiB    0.988 MiB       a = 'a' * 1024 * 1024
6   11.285 MiB    0.000 MiB       del a
7   11.285 MiB    0.000 MiB       a = 'a' * 1024
8   11.285 MiB    0.000 MiB       del a
9   11.289 MiB    0.004 MiB       print "+++++++++"

结论：是否 del对象没有影响，新赋的值会替代旧的值

3. 对象赋值是否会增加同样的内存

#对比1

@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
b = a
del a
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

Line #    Mem usage    Increment   Line Contents
================================================
1   10.293 MiB    0.000 MiB   @profile
2                             def my_func():
3 1034.301 MiB 1024.008 MiB       a = 'a' * 1024 * 1024 * 1024;
4 1034.301 MiB    0.000 MiB       b = a
5 1034.301 MiB    0.000 MiB       del a
6 1034.305 MiB    0.004 MiB       print "+++++++++"

#对比2

@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
b = a
del a
del b
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

Line #    Mem usage    Increment   Line Contents
================================================
1   10.297 MiB    0.000 MiB   @profile
2                             def my_func():
3 1034.305 MiB 1024.008 MiB       a = 'a' * 1024 * 1024 * 1024;
4 1034.305 MiB    0.000 MiB       b = a
5 1034.305 MiB    0.000 MiB       del a
6   10.301 MiB -1024.004 MiB       del b
7   10.305 MiB    0.004 MiB       print "+++++++++"

结论，把a赋值给b，内存没有增加。但是只删除其中一个对象的时候，内存不会减。

4. 另一种等价的启动方式

from memory_profiler import profile
@profile(precision=4)
def my_func():
a = 'a' * 1024 * 1024 * 1024;
del a
a = 'a' * 1024 * 1024
del a
a = 'a' * 1024
del a
print "+++++++++"

if __name__ == '__main__':
my_func()

结果

$python -m memory_profiler del3.py
+++++++++
Filename: del3.py

Line #    Mem usage    Increment   Line Contents
================================================
2  10.3867 MiB   0.0000 MiB   @profile(precision=4)
3                             def my_func():
4 1034.3945 MiB 1024.0078 MiB       a = 'a' * 1024 * 1024 * 1024;
5  10.3906 MiB -1024.0039 MiB       del a
6  11.3789 MiB   0.9883 MiB       a = 'a' * 1024 * 1024
7  11.3789 MiB   0.0000 MiB       del a
8  11.3789 MiB   0.0000 MiB       a = 'a' * 1024
9  11.3789 MiB   0.0000 MiB       del a
10  11.3828 MiB   0.0039 MiB       print "+++++++++"

5. 非python内置对象例子

from memory_profiler import profile
import networkx as nx

@profile(precision=4)
def my_func():
a = 'a' * 1024 * 1024 * 1024;
del a
G = nx.Graph()
G.add_node(1)
G.add_nodes_from([i for i in range(10000)])
G.add_nodes_from([i for i in range(10000, 20000)])
G.add_edges_from([(1,2), (1,4), (2, 9), (4, 1), (3, 8)])
del G
print "++++++"

if __name__ == '__main__':
my_func()

结果

$python del3.py
++++++
Filename: del3.py

Line #    Mem usage    Increment   Line Contents
================================================
4  23.4844 MiB   0.0000 MiB   @profile(precision=4)
5                             def my_func():
6 1047.4922 MiB 1024.0078 MiB       a = 'a' * 1024 * 1024 * 1024;
7  23.4883 MiB -1024.0039 MiB       del a
8  23.4883 MiB   0.0000 MiB       G = nx.Graph()
9  23.4883 MiB   0.0000 MiB       G.add_node(1)
10  31.3359 MiB   7.8477 MiB       G.add_nodes_from([i for i in range(10000)])
11  36.9219 MiB   5.5859 MiB       G.add_nodes_from([i for i in range(10000, 20000)])
12  36.9219 MiB   0.0000 MiB       G.add_edges_from([(1,2), (1,4), (2, 9), (4, 1), (3, 8)])
13  25.9219 MiB -11.0000 MiB       del G
14  25.9258 MiB   0.0039 MiB       print "++++++"

6. 类怎么使用呢

#del4.py

from memory_profiler import profile

class people:
name = ''
age = 0
__weight = 0

def __init__(self,n,a,w):
self.name = n
self.age = a
self.__weight = w

@profile(precision=4)
def speak(self):
a = 'a' * 1024
b = 'b' * 1024 * 1024
print("%s is speaking: I am %d years old" % (self.name,self.age))

if __name__ == '__main__':
p = people('tom', 10, 30)
p.speak()

结果

$python del4.py
tom is speaking: I am 10 years old
Filename: del4.py

Line #    Mem usage    Increment   Line Contents
================================================
13   9.4219 MiB   0.0000 MiB       @profile(precision=4)
14                                 def speak(self):
15   9.4258 MiB   0.0039 MiB           a = 'a' * 1024
16  10.4297 MiB   1.0039 MiB           b = 'b' * 1024 * 1024
17  10.4336 MiB   0.0039 MiB           print("%s is speaking: I am %d years old" % (self.name,self.age))

7. 随时间内存统计

#test.py

import time

@profile
def test1():
n = 10000
a = [1] * n
time.sleep(1)
return a

@profile
def test2():
n = 100000
b = [1] * n
time.sleep(1)
return b

if __name__ == "__main__":
test1()
test2()

test.py 里有两个两个待分析的函数（@profile标识），为了形象地看出内存随时间的变化，每个函数内sleep 1s，执行

mprof run test.py

如果执行成功，结果这样

$ mprof run test.py
mprof: Sampling memory every 0.1s
running as a Python program...

结果会生成一个.dat文件，如"mprofile_20160716170529.dat"，里面记录了内存随时间的变化，可用下面的命令以图片的形式展示出来：

mprof plot

8. API

memory_profiler提供很多包给第三方代码，如

>>> from memory_profiler import memory_usage
>>> mem_usage = memory_usage(-1, interval=.2, timeout=1)
>>> print(mem_usage)
[7.296875, 7.296875, 7.296875, 7.296875, 7.296875]

memory_usage(proc=-1, interval=.2, timeout=None)返回一段时间的内存值，其中proc=-1表示此进程，这里可以指定特定的进程号；interval=.2表示监控的时间间隔是0.2秒；timeout=1表示总共的时间段为1秒。那结果就返回5个值。

如果要返回一个函数的内存消耗，示例

def f(a, n=100):
import time
time.sleep(2)
b = [a] * n
time.sleep(1)
return b

from memory_profiler import memory_usage
print memory_usage((f, (2,), {'n' : int(1e6)}))

这里执行了 f(1, n=int(1e6)) ，并返回在执行此函数时的内存消耗。

9. 优化实例

对比str & int

from datetime import datetime
@profile
def my_func():
beg = datetime.now()
a = {}
for i in range(1000000):
a[i] = i
#a[str(i)] = i
print "+++++++++"
del a
print "+++++++++"
end = datetime.now()
print "time:", end - beg

if __name__ == '__main__':
my_func()

用a[i] = i，结果

+++++++++
+++++++++
time: 0:06:14.790899
Filename: int.py

Line #    Mem usage    Increment   Line Contents
================================================
2   14.727 MiB    0.000 MiB   @profile
3                             def my_func():
4   14.734 MiB    0.008 MiB       beg = datetime.now()
5   14.734 MiB    0.000 MiB       a = {}
6   94.031 MiB   79.297 MiB       for i in range(1000000):
7   94.031 MiB    0.000 MiB           a[i] = i
8                                     #a[str(i)] = i
9   86.402 MiB   -7.629 MiB       print "+++++++++"
10   38.398 MiB  -48.004 MiB       del a
11   38.398 MiB    0.000 MiB       print "+++++++++"
12   38.398 MiB    0.000 MiB       end = datetime.now()
13   38.406 MiB    0.008 MiB       print "time:", end - beg

用a[str(i)] = i，结果

+++++++++
+++++++++
time: 0:06:00.288052
Filename: int.py

Line #    Mem usage    Increment   Line Contents
================================================
2   14.723 MiB    0.000 MiB   @profile
3                             def my_func():
4   14.730 MiB    0.008 MiB       beg = datetime.now()
5   14.730 MiB    0.000 MiB       a = {}
6  140.500 MiB  125.770 MiB       for i in range(1000000):
7                                     #a[i] = i
8  140.500 MiB    0.000 MiB           a[str(i)] = i
9  132.871 MiB   -7.629 MiB       print "+++++++++"
10   38.539 MiB  -94.332 MiB       del a
11   38.539 MiB    0.000 MiB       print "+++++++++"
12   38.539 MiB    0.000 MiB       end = datetime.now()
13   38.547 MiB    0.008 MiB       print "time:", end - beg