python 内存泄露问题定位
- 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。
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- 入门例子
del3.py
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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()
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结果
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$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 "+++++++++"
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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。
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2. 对象不删除，直接赋值内存是否会继续增长
对比1
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@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
a = 'a' * 1024 * 1024
a = 'a' * 1024
del a
print "+++++++++"
if name == 'main':
my_func()
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结果
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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 "+++++++++"
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对比2
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@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()
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结果
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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 "+++++++++"
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结论：是否 del对象没有影响，新赋的值会替代旧的值
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3. 对象赋值是否会增加同样的内存
对比1
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@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
b = a
del a
print "+++++++++"
if name == 'main':
my_func()
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结果
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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 "+++++++++"
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对比2
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@profile
def my_func():
a = 'a' * 1024 * 1024 * 1024;
b = a
del a
del b
print "+++++++++"
if name == 'main':
my_func()
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结果
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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 "+++++++++"
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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。
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4. 另一种等价的启动方式
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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()
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结果
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$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 "+++++++++"
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5. 非python内置对象例子
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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()
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结果
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$python del3.py
++++++
Filename: del3.py
Line # Mem usage Increment Line Contents
4 23.4844 MiB 0.0000 MiB @profile(precision=4)
5 def my_func():
6 1047.4922 MiB 1024.0078 MiB a = 'a' * 1024 * 1024 * 1024;
7 23.4883 MiB -1024.0039 MiB del a
8 23.4883 MiB 0.0000 MiB G = nx.Graph()
9 23.4883 MiB 0.0000 MiB G.add_node(1)
10 31.3359 MiB 7.8477 MiB G.add_nodes_from([i for i in range(10000)])
11 36.9219 MiB 5.5859 MiB G.add_nodes_from([i for i in range(10000, 20000)])
12 36.9219 MiB 0.0000 MiB G.add_edges_from([(1,2), (1,4), (2, 9), (4, 1), (3, 8)])
13 25.9219 MiB -11.0000 MiB del G
14 25.9258 MiB 0.0039 MiB print "++++++"
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6. 类怎么使用呢
del4.py
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from memory_profiler import profile
class people:
name = ''
age = 0
__weight = 0
def __init__(self,n,a,w):
self.name = n
self.age = a
self.__weight = w
@profile(precision=4)
def speak(self):
a = 'a' * 1024
b = 'b' * 1024 * 1024
print("%s is speaking: I am %d years old" % (self.name,self.age))
if name == 'main':
p = people('tom', 10, 30)
p.speak()
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结果
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$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))
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7. 随时间内存统计
test.py
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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()
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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
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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
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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)}))
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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。
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9. 优化实例
对比str & int
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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()
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用a[i] = i，结果
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+++++++++
+++++++++
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
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用a[str(i)] = i，结果
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+++++++++
+++++++++
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
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