2款Python内存检测工具介绍和使用方法
去年自己写过一个程序时，不太确定自己的内存使用量，就想找写工具来打印程序或函数的内存使用量。
这里将上次找到的2个内存检测工具的基本用法记录一下，今后分析Python程序内存使用量时也是需要的。
memory_profiler模块（与psutil一起使用）
注：psutil这模块，我太喜欢了，它实现了很多Linux命令的主要功能，如：ps, top, lsof, netstat, ifconfig, who, df, kill, free 等等。
示例代码（https://github.com/smilejay/python/blob/master/py2014/mem_profile.py）：
复制代码 代码如下:
#!/usr/bin/env python
'''
Created on May 31, 2014
@author: Jay <smile665@gmail.com>
@description: use memory_profiler module for profiling programs/functions.
'''
from memory_profiler import profile
from memory_profiler import memory_usage
import time
@profile
def my_func():
a = [1] * (10 ** 6)
b = [2] * (2 * 10 ** 7)
del b
return a
def cur_python_mem():
mem_usage = memory_usage(-1, interval=0.2, timeout=1)
return mem_usage
def f(a, n=100):
time.sleep(1)
b = [a] * n
time.sleep(1)
return b
if __name__ == '__main__':
a = my_func()
print cur_python_mem()
print ""
print memory_usage((f, (1,), {'n': int(1e6)}), interval=0.5)
运行上面的代码，输出结果为：
复制代码 代码如下:
jay@Jay-Air:~/workspace/python.git/py2014 $python mem_profile.py
Filename: mem_profile.py
Line # Mem usage Increment Line Contents
================================================
15 8.0 MiB 0.0 MiB @profile
16 def my_func():
17 15.6 MiB 7.6 MiB a = [1] * (10 ** 6)
18 168.2 MiB 152.6 MiB b = [2] * (2 * 10 ** 7)
19 15.6 MiB -152.6 MiB del b
20 15.6 MiB 0.0 MiB return a
[15.61328125, 15.6171875, 15.6171875, 15.6171875, 15.6171875]
[15.97265625, 16.00390625, 16.00390625, 17.0546875, 23.63671875, 23.63671875, 23.640625]
Guppy (使用了Heapy）
Guppy is an umbrella package combining Heapy and GSL with support utilities such as the Glue module that keeps things together.
示例代码（https://github.com/smilejay/python/blob/master/py2014/try_guppy.py）：
复制代码 代码如下:
#!/usr/bin/env python
'''
Created on May 31, 2014
@author: Jay <smile665@gmail.com>
@description: just try to use Guppy-PE (useing Heapy) for memory profiling.
'''
from guppy import hpy
a = [8] * (10 ** 6)
h = hpy()
print h.heap()
print h.heap().more
print h.heap().more.more
注意其中，要输出更多信息的.more用法。
运行上面的程序，输出结果为：
复制代码 代码如下:
jay@Jay-Air:~/workspace/python.git/py2014 $python try_guppy.py
Partition of a set of 26963 objects. Total size = 11557848 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 177 1 8151560 71 8151560 71 list
1 12056 45 996840 9 9148400 79 str
2 5999 22 488232 4 9636632 83 tuple
3 324 1 283104 2 9919736 86 dict (no owner)
4 68 0 216416 2 10136152 88 dict of module
5 199 1 210856 2 10347008 90 dict of type
6 1646 6 210688 2 10557696 91 types.CodeType
7 1610 6 193200 2 10750896 93 function
8 199 1 177008 2 10927904 95 type
9 124 0 135328 1 11063232 96 dict of class
<91 more rows. Type e.g. '_.more' to view.>
Index Count % Size % Cumulative % Kind (class / dict of class)
10 1045 4 83600 1 11148456 96 __builtin__.wrapper_descriptor
11 109 0 69688 1 11218144 97 dict of guppy.etc.Glue.Interface
12 389 1 34232 0 11252376 97 __builtin__.weakref
13 427 2 30744 0 11283120 97 types.BuiltinFunctionType
14 411 2 29592 0 11312712 98 __builtin__.method_descriptor
15 25 0 26200 0 11338912 98 dict of guppy.etc.Glue.Share
16 108 0 25056 0 11363968 98 __builtin__.set
17 818 3 19632 0 11383600 98 int
18 66 0 18480 0 11402080 98 dict of guppy.etc.Glue.Owner
19 16 0 17536 0 11419616 99 dict of abc.ABCMeta
<81 more rows. Type e.g. '_.more' to view.>
（后面省略了部分输出）
另外，还有一个叫“PySizer”的也是做memory profiling的，不过没怎么维护了。
