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創(chuàng)新互聯(lián)Python教程：tracemalloc—-跟蹤內(nèi)存分配

tracemalloc —- 跟蹤內(nèi)存分配

3.4 新版功能.

源代碼： Lib/tracemalloc.py

tracemalloc 模塊是一個(gè)用于對(duì) python 已申請(qǐng)的內(nèi)存塊進(jìn)行debug的工具。它能提供以下信息:

回溯對(duì)象分配內(nèi)存的位置
按文件、按行統(tǒng)計(jì)python的內(nèi)存塊分配情況: 內(nèi)存塊總大小、數(shù)量以及塊平均大小。
對(duì)比兩個(gè)內(nèi)存快照的差異，以便排查內(nèi)存泄漏

To trace most memory blocks allocated by Python, the module should be started as early as possible by setting the PYTHONTRACEMALLOC environment variable to 1, or by using -X tracemalloc command line option. The tracemalloc.start() function can be called at runtime to start tracing Python memory allocations.

By default, a trace of an allocated memory block only stores the most recent frame (1 frame). To store 25 frames at startup: set the PYTHONTRACEMALLOC environment variable to 25, or use the -X tracemalloc=25 command line option.

例子

顯示前10項(xiàng)

顯示內(nèi)存分配最多的10個(gè)文件：

 
 
 
 
  
  
  
  import tracemalloc  
  
  
    
  
  
  tracemalloc.start()  
  
  
    
  
  
  # ... run your application ...  
  
  
    
  
  
  snapshot = tracemalloc.take_snapshot()  
  
  
  top_stats = snapshot.statistics('lineno')  
  
  
    
  
  
  print("[ Top 10 ]")  
  
  
  for stat in top_stats[:10]:  
  
  
   print(stat)

Python測(cè)試套件的輸出示例：

 
 
 
 
  
  
  
  [ Top 10 ]  
  
  
  :716: size=4855 KiB, count=39328, average=126 B  
  
  
  :284: size=521 KiB, count=3199, average=167 B  
  
  
  /usr/lib/Python3.4/collections/__init__.py:368: size=244 KiB, count=2315, average=108 B  
  
  
  /usr/lib/python3.4/unittest/case.py:381: size=185 KiB, count=779, average=243 B  
  
  
  /usr/lib/python3.4/unittest/case.py:402: size=154 KiB, count=378, average=416 B  
  
  
  /usr/lib/python3.4/abc.py:133: size=88.7 KiB, count=347, average=262 B  
  
  
  :1446: size=70.4 KiB, count=911, average=79 B  
  
  
  :1454: size=52.0 KiB, count=25, average=2131 B  
  
  
  :5: size=49.7 KiB, count=148, average=344 B  
  
  
  /usr/lib/python3.4/sysconfig.py:411: size=48.0 KiB, count=1, average=48.0 KiB

We can see that Python loaded 4855 KiB data (bytecode and constants) from modules and that the collections module allocated 244 KiB to build namedtuple types.

更多選項(xiàng)，請(qǐng)參見(jiàn) Snapshot.statistics()

計(jì)算差異

獲取兩個(gè)快照并顯示差異：

 
 
 
 
  
  
  
  import tracemalloc  
  
  
  tracemalloc.start()  
  
  
  # ... start your application ...  
  
  
    
  
  
  snapshot1 = tracemalloc.take_snapshot()  
  
  
  # ... call the function leaking memory ...  
  
  
  snapshot2 = tracemalloc.take_snapshot()  
  
  
    
  
  
  top_stats = snapshot2.compare_to(snapshot1, 'lineno')  
  
  
    
  
  
  print("[ Top 10 differences ]")  
  
  
  for stat in top_stats[:10]:  
  
  
   print(stat)

Example of output before/after running some tests of the Python test suite:

 
 
 
 
  
  
  
  [ Top 10 differences ]  
  
  
  :716: size=8173 KiB (+4428 KiB), count=71332 (+39369), average=117 B  
  
  
  /usr/lib/python3.4/linecache.py:127: size=940 KiB (+940 KiB), count=8106 (+8106), average=119 B  
  
  
  /usr/lib/python3.4/unittest/case.py:571: size=298 KiB (+298 KiB), count=589 (+589), average=519 B  
  
  
  :284: size=1005 KiB (+166 KiB), count=7423 (+1526), average=139 B  
  
  
  /usr/lib/python3.4/mimetypes.py:217: size=112 KiB (+112 KiB), count=1334 (+1334), average=86 B  
  
  
  /usr/lib/python3.4/http/server.py:848: size=96.0 KiB (+96.0 KiB), count=1 (+1), average=96.0 KiB  
  
  
  /usr/lib/python3.4/inspect.py:1465: size=83.5 KiB (+83.5 KiB), count=109 (+109), average=784 B  
  
  
  /usr/lib/python3.4/unittest/mock.py:491: size=77.7 KiB (+77.7 KiB), count=143 (+143), average=557 B  
  
  
  /usr/lib/python3.4/urllib/parse.py:476: size=71.8 KiB (+71.8 KiB), count=969 (+969), average=76 B  
  
  
  /usr/lib/python3.4/contextlib.py:38: size=67.2 KiB (+67.2 KiB), count=126 (+126), average=546 B

We can see that Python has loaded 8173 KiB of module data (bytecode and constants), and that this is 4428 KiB more than had been loaded before the tests, when the previous snapshot was taken. Similarly, the linecache module has cached 940 KiB of Python source code to format tracebacks, all of it since the previous snapshot.

If the system has little free memory, snapshots can be written on disk using the Snapshot.dump() method to analyze the snapshot offline. Then use the Snapshot.load() method reload the snapshot.

獲取一個(gè)內(nèi)存塊的溯源

一段找出程序中最大內(nèi)存塊溯源的代碼:

 
 
 
 
  
  
  
  import tracemalloc  
  
  
    
  
  
  # Store 25 frames  
  
  
  tracemalloc.start(25)  
  
  
    
  
  
  # ... run your application ...  
  
  
    
  
  
  snapshot = tracemalloc.take_snapshot()  
  
  
  top_stats = snapshot.statistics('traceback')  
  
  
    
  
  
  # pick the biggest memory block  
  
  
  stat = top_stats[0]  
  
  
  print("%s memory blocks: %.1f KiB" % (stat.count, stat.size / 1024))  
  
  
  for line in stat.traceback.format():  
  
  
   print(line)

一段Python單元測(cè)試的輸出案例(限制最大25層堆棧)

 
 
 
 
  
  
  
  903 memory blocks: 870.1 KiB  
  
  
   File "", line 716  
  
  
   File "", line 1036  
  
  
   File "", line 934  
  
  
   File "", line 1068  
  
  
   File "", line 619  
  
  
   File "", line 1581  
  
  
   File "", line 1614  
  
  
   File "/usr/lib/python3.4/doctest.py", line 101  
  
  
   import pdb  
  
  
   File "", line 284  
  
  
   File "", line 938  
  
  
   File "", line 1068  
  
  
   File "", line 619  
  
  
   File "", line 1581  
  
  
   File "", line 1614  
  
  
   File "/usr/lib/python3.4/test/support/__init__.py", line 1728  
  
  
   import doctest  
  
  
   File "/usr/lib/python3.4/test/test_pickletools.py", line 21  
  
  
   support.run_doctest(pickletools)  
  
  
   File "/usr/lib/python3.4/test/regrtest.py", line 1276  
  
  
   test_runner()  
  
  
   File "/usr/lib/python3.4/test/regrtest.py", line 976  
  
  
   display_failure=not verbose)  
  
  
   File "/usr/lib/python3.4/test/regrtest.py", line 761  
  
  
   match_tests=ns.match_tests)  
  
  
   File "/usr/lib/python3.4/test/regrtest.py", line 1563  
  
  
   main()  
  
  
   File "/usr/lib/python3.4/test/__main__.py", line 3  
  
  
   regrtest.main_in_temp_cwd()  
  
  
   File "/usr/lib/python3.4/runpy.py", line 73  
  
  
   exec(code, run_globals)  
  
  
   File "/usr/lib/python3.4/runpy.py", line 160  
  
  
   "__main__", fname, loader, pkg_name)

We can see that the most memory was allocated in the importlib module to load data (bytecode and constants) from modules: 870.1 KiB. The traceback is where the importlib loaded data most recently: on the import pdb line of the doctest module. The traceback may change if a new module is loaded.

Pretty top

Code to display the 10 lines allocating the most memory with a pretty output, ignoring and files:

 
 
 
 
  
  
  
  import linecache  
  
  
  import os  
  
  
  import tracemalloc  
  
  
    
  
  
  def display_top(snapshot, key_type='lineno', limit=10):  
  
  
   snapshot = snapshot.filter_traces((  
  
  
   tracemalloc.Filter(False, ""),  
  
  
   tracemalloc.Filter(False, ""),  
  
  
   ))  
  
  
   top_stats = snapshot.statistics(key_type)  
  
  
    
  
  
   print("Top %s lines" % limit)  
  
  
   for index, stat in enumerate(top_stats[:limit], 1):  
  
  
   frame = stat.traceback[0]  
  
  
   print("#%s: %s:%s: %.1f KiB"  
  
  
   % (index, frame.filename, frame.lineno, stat.size / 1024))  
  
  
   line = linecache.getline(frame.filename, frame.lineno).strip()  
  
  
   if line:  
  
  
   print(' %s' % line)  
  
  
    
  
  
   other = top_stats[limit:]  
  
  
   if other:  
  
  
   size = sum(stat.size for stat in other)  
  
  
   print("%s other: %.1f KiB" % (len(other), size / 1024))  
  
  
   total = sum(stat.size for stat in top_stats)  
  
  
   print("Total allocated size: %.1f KiB" % (total / 1024))  
  
  
    
  
  
  tracemalloc.start()  
  
  
    
  
  
  # ... run your application ...  
  
  
    
  
  
  snapshot = tracemalloc.take_snapshot()  
  
  
  display_top(snapshot)

Python測(cè)試套件的輸出示例：

 
 
 
 
  
  
  
  Top 10 lines  
  
  
  #1: Lib/base64.py:414: 419.8 KiB  
  
  
   _b85chars2 = [(a + b) for a in _b85chars for b in _b85chars]  
  
  
  #2: Lib/base64.py:306: 419.8 KiB  
  
  
   _a85chars2 = [(a + b) for a in _a85chars for b in _a85chars]  
  
  
  #3: collections/__init__.py:368: 293.6 KiB  
  
  
   exec(class_definition, namespace)  
  
  
  #4: Lib/abc.py:133: 115.2 KiB  
  
  
   cls = super().__new__(mcls, name, bases, namespace)  
  
  
  #5: unittest/case.py:574: 103.1 KiB  
  
  
   testMethod()  
  
  
  #6: Lib/linecache.py:127: 95.4 KiB  
  
  
   lines = fp.readlines()  
  
  
  #7: urllib/parse.py:476: 71.8 KiB  
  
  
   for a in _hexdig for b in _hexdig}  
  
  
  #8: :5: 62.0 KiB  
  
  
  #9: Lib/_weakrefset.py:37: 60.0 KiB  
  
  
   self.data = set()  
  
  
  #10: Lib/base64.py:142: 59.8 KiB  
  
  
   _b32tab2 = [a + b for a in _b32tab for b in _b32tab]  
  
  
  6220 other: 3602.8 KiB  
  
  
  Total allocated size: 5303.1 KiB

更多選項(xiàng)，請(qǐng)參見(jiàn) Snapshot.statistics()

Record the current and peak size of all traced memory blocks

The following code computes two sums like 0 + 1 + 2 + ... inefficiently, by creating a list of those numbers. This list consumes a lot of memory temporarily. We can use get_traced_memory() and reset_peak() to observe the small memory usage after the sum is computed as well as the peak memory usage during the computations:

 
 
 
 
  
  
  
  import tracemalloc  
  
  
    
  
  
  tracemalloc.start()  
  
  
    
  
  
  # Example code: compute a sum with a large temporary list  
  
  
  large_sum = sum(list(range(100000)))  
  
  
    
  
  
  first_size, first_peak = tracemalloc.get_traced_memory()  
  
  
    
  
  
  tracemalloc.reset_peak()  
  
  
    
  
  
  # Example code: compute a sum with a small temporary list  
  
  
  small_sum = sum(list(range(1000)))  
  
  
    
  
  
  second_size, second_peak = tracemalloc.get_traced_memory()  
  
  
    
  
  
  print(f"{first_size=}, {first_peak=}")  
  
  
  print(f"{second_size=}, {second_peak=}")

輸出:

 
 
 
 
  
  
  
  first_size=664, first_peak=3592984  
  
  
  second_size=804, second_peak=29704

Using reset_peak() ensured we could accurately record the peak during the computation of small_sum, even though it is much smaller than the overall peak size of memory blocks since the start() call. Without the call to reset_peak(), second_peak would still be the peak from the computation large_sum (that is, equal to first_peak). In this case, both peaks are much higher than the final memory usage, and which suggests we could optimise (by removing the unnecessary call to list, and writing sum(range(...))).

API

函數(shù)

tracemalloc.clear_traces()

清空 Python 所分配的內(nèi)存塊的追蹤數(shù)據(jù)。

另見(jiàn) stop().

tracemalloc.get_object_traceback(obj)

Get the traceback where the Python object obj was allocated. Return a Traceback instance, or None if the tracemalloc module is not tracing memory allocations or did not trace the allocation of the object.

See also gc.get_referrers() and sys.getsizeof() functions.

tracemalloc.get_traceback_limit()

Get the maximum number of frames stored in the traceback of a trace.

The tracemalloc module must be tracing memory allocations to get the limit, otherwise an exception is raised.

The limit is set by the start() function.

tracemalloc.get_traced_memory()

Get the current size and peak size of memory blocks traced by the tracemalloc module as a tuple: (current: int, peak: int).

tracemalloc.reset_peak()

Set the peak size of memory blocks traced by the tracemalloc module to the current size.

Do nothing if the tracemalloc module is not tracing memory allocations.

This function only modifies the recorded peak size, and does not modify or clear any traces, unlike clear_traces(). Snapshots taken with take_snapshot() before a call to reset_peak() can be meaningfully compared to snapshots taken after the call.

域過(guò)濾器

class tracemalloc.DomainFilter(inclusive: bool, domain: int)

Filter traces of memory blocks by their address space (domain).

3.6 新版功能.

inclusive
If inclusive is True (include), match memory blocks allocated in the address space domain.
If inclusive is False (exclude), match memory blocks not allocated in the address space domain.
domain
Address space of a memory block (int). Read-only property.

過(guò)濾器

class tracemalloc.Filter(inclusive: bool, filename_pattern: str, lineno: int = None, all_frames: bool = False, domain: int = None)

對(duì)內(nèi)存塊的跟蹤進(jìn)行篩選。

See the fnmatch.fnmatch() function for the syntax of filename_pattern. The '.pyc' file extension is replaced with '.py'.

示例：

Filter(True, subprocess.__file__) 只包括 subprocess 模塊的追蹤數(shù)據(jù)
Filter(False, tracemalloc.__file__) 排除了 tracemalloc 模塊的追蹤數(shù)據(jù)
Filter(False, "") 排除了空的回溯信息

在 3.5 版更改: '.pyo' 文件擴(kuò)展名不會(huì)再被替換為 '.py'。

在 3.6 版更改: 增加了 domain 屬性。

domain
Address space of a memory block (int or None).
tracemalloc uses the domain 0 to trace memory allocations made by Python. C extensions can use other domains to trace other resources.
inclusive
If inclusive is True (include), only match memory blocks allocated in a file with a name matching filename_pattern at line number lineno.
If inclusive is False (exclude), ignore memory blocks allocated in a file with a name matching filename_pattern at line number lineno.
lineno
Line number (int) of the filter. If lineno is None, the filter matches any line number.
filename_pattern
Filename pattern of the filter (str). Read-only property.
all_frames
If all_frames is True, all frames of the traceback are checked. If all_frames is False, only the most recent frame is checked.
This attribute has no effect if the traceback limit is 1. See the get_traceback_limit() function and Snapshot.traceback_limit attribute.

Frame

class tracemalloc.Frame

Frame of a traceback.

The Traceback class is a sequence of Frame instances.

filename
文件名（字符串）
lineno
行號(hào)（整形）

快照

class tracemalloc.Snapshot

Snapshot of traces of memory blocks allocated by Python.

The take_snapshot() function creates a snapshot instance.

compare_to(old_snapshot: Snapshot, key_type: str, cumulative: bool = False)
Compute the differences with an old snapshot. Get statistics as a sorted list of StatisticDiff instances grouped by key_type.
See the Snapshot.statistics() method for key_type and cumulative parameters.
The result is sorted from the biggest to the smallest by: absolute value of StatisticDiff.size_diff, StatisticDiff.size, absolute value of StatisticDiff.count_diff, Statistic.count and then by StatisticDiff.traceback.
dump(filename)
將快照寫入文件
使用 load() 重載快照。
filter_traces(filters)
Create a new Snapshot instance with a filtered traces sequence, filters is a list of DomainFilter and Filter instances. If filters is an empty list, return a new Snapshot instance with a copy of the traces.
All inclusive filters are applied at once, a trace is ignored if no inclusive filters match it. A trace is ignored if at least one exclusive filter matches it.
在 3.6 版更改: DomainFilter instances are now also accepted in filters.
classmethod load(filename)
從文件載入快照。
另見(jiàn) dump().
statistics(key_type: str, cumulative: bool = False)
獲取 Statistic 信息列表，按 key_type 分組排序：

key_type

description

‘filename’

文件名

‘lineno’

文件名和行號(hào)

‘traceback’

回溯

If cumulative is True, cumulate size and count of memory blocks of all frames of the traceback of a trace, not only the most recent frame. The cumulative mode can only be used with key_type equals to 'filename' and 'lineno'.
The result is sorted from the biggest to the smallest by: Statistic.size, Statistic.count and then by Statistic.traceback.
traceback_limit
Maximum number of frames stored in the traceback of traces: result of the get_traceback_limit() when the snapshot was taken.
traces
Traces of all memory blocks allocated by Python: sequence of Trace instances.
The sequence has an undefined order. Use the Snapshot.statistics() method to get a sorted list of statistics.

key_type	description
`‘filename’`	文件名
`‘lineno’`	文件名和行號(hào)
`‘traceback’`	回溯

統(tǒng)計(jì)

class tracemalloc.Statistic

統(tǒng)計(jì)內(nèi)存分配

Snapshot.statistics() 返回 Statistic 實(shí)例的列表。.

參見(jiàn) StatisticDiff 類。

count
內(nèi)存塊數(shù)（整形）。
size
Total size of memory blocks in bytes (int).
traceback
Traceback where the memory block was allocated, Traceback instance.

StatisticDiff

class tracemalloc.StatisticDiff

Statistic difference on memory allocations between an old and a new Snapshot instance.

Snapshot.compare_to() returns a list of StatisticDiff instances. See also the Statistic class.

count
Number of memory blocks in the new snapshot (int): 0 if the memory blocks have been released in the new snapshot.
count_diff
Difference of number of memory blocks between the old and the new snapshots (int): 0 if the memory blocks have been allocated in the new snapshot.
size
Total size of memory blocks in bytes in the new snapshot (int): 0 if the memory blocks have been released in the new snapshot.
size_diff
Difference of total size of memory blocks in bytes between the old and the new snapshots (int): 0 if the memory blocks have been allocated in the new snapshot.
traceback
Traceback where the memory blocks were allocated, Traceback instance.

跟蹤

class tracemalloc.Trace

Trace of a memory block.

The Snapshot.traces attribute is a sequence of Trace instances.

在 3.6 版更改: 增加了 domain 屬性。

domain
Address space of a memory block (int). Read-only property.
tracemalloc uses the domain 0 to trace memory allocations made by Python. C extensions can use other domains to trace other resources.
size
Size of the memory block in bytes (int).
traceback
Traceback where the memory block was allocated, Traceback instance.

回溯

class tracemalloc.Traceback

Sequence of Frame instances sorted from the oldest frame to the most recent frame.

A traceback contains at least 1 frame. If the tracemalloc module failed to get a frame, the filename "" at line number 0 is used.

When a snapshot is taken, tracebacks of traces are limited to get_traceback_limit() frames. See the take_snapshot() function. The original number of frames of the traceback is stored in the Traceback.total_nframe attribute. That allows to know if a traceback has been truncated by the traceback limit.

The Trace.traceback attribute is an instance of Traceback instance.

在 3.7 版更改: Frames are now sorted from the oldest to the most recent, instead of most recent to oldest.

total_nframe
Total number of frames that composed the traceback before truncation. This attribute can be set to None if the information is not available.

在 3.9 版更改: The Traceback.total_nframe attribute was added.

format(limit=None, most_recent_first=False)

Format the traceback as a list of lines. Use the linecache module to retrieve lines from the source code. If limit is set, format the limit most recent frames if limit is positive. Otherwise, format the abs(limit) oldest frames. If most_recent_first is True, the order of the formatted frames is reversed, returning the most recent frame first instead of last.

Similar to the traceback.format_tb() function, except that format() does not include newlines.

示例:

   
   
   
   
    
    
    
    print("Traceback (most recent call first):")    
    
    
    for line in traceback:    
    
    
     print(line)

輸出:

   
   
   
   
    
    
    
    Traceback (most recent call first):    
    
    
     File "test.py", line 9    
    
    
     obj = Object()    
    
    
     File "test.py", line 12    
    
    
     tb = tracemalloc.get_object_traceback(f())

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