2款Python内存检测工具介绍和使用方法
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2023-11-16 12:25:34
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...
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。
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