FM / library

/usr/share/doc/python3-docs/html/_sources/library/

/usr/share/doc/python3-docs/html/_sources/library

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Edit: /usr/share/doc/python3-docs/html/_sources/library/tracemalloc.rst.txt (22089B)

:mod:`tracemalloc` --- Trace memory allocations
===============================================

.. module:: tracemalloc
   :synopsis: Trace memory allocations.

.. versionadded:: 3.4

**Source code:** :source:`Lib/tracemalloc.py`

--------------

The tracemalloc module is a debug tool to trace memory blocks allocated by
Python. It provides the following information:

* Traceback where an object was allocated
* Statistics on allocated memory blocks per filename and per line number:
  total size, number and average size of allocated memory blocks
* Compute the differences between two snapshots to detect memory leaks

To trace most memory blocks allocated by Python, the module should be started
as early as possible by setting the :envvar:`PYTHONTRACEMALLOC` environment
variable to ``1``, or by using :option:`-X` ``tracemalloc`` command line
option. The :func:`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
:envvar:`PYTHONTRACEMALLOC` environment variable to ``25``, or use the
:option:`-X` ``tracemalloc=25`` command line option.

Examples
--------

Display the top 10
^^^^^^^^^^^^^^^^^^

Display the 10 files allocating the most memory::

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)

Example of output of the Python test suite::

[ Top 10 ]
    <frozen importlib._bootstrap>:716: size=4855 KiB, count=39328, average=126 B
    <frozen importlib._bootstrap>: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
    <frozen importlib._bootstrap>:1446: size=70.4 KiB, count=911, average=79 B
    <frozen importlib._bootstrap>:1454: size=52.0 KiB, count=25, average=2131 B
    <string>: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 :mod:`collections` module allocated ``244 KiB`` to build
:class:`~collections.namedtuple` types.

See :meth:`Snapshot.statistics` for more options.

Compute differences
^^^^^^^^^^^^^^^^^^^

Take two snapshots and display the differences::

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 ]
    <frozen importlib._bootstrap>: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
    <frozen importlib._bootstrap>: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 :mod:`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 :meth:`Snapshot.dump` method to analyze the snapshot offline. Then use the
:meth:`Snapshot.load` method reload the snapshot.

Get the traceback of a memory block
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Code to display the traceback of the biggest memory block::

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)

Example of output of the Python test suite (traceback limited to 25 frames)::

903 memory blocks: 870.1 KiB
      File "<frozen importlib._bootstrap>", line 716
      File "<frozen importlib._bootstrap>", line 1036
      File "<frozen importlib._bootstrap>", line 934
      File "<frozen importlib._bootstrap>", line 1068
      File "<frozen importlib._bootstrap>", line 619
      File "<frozen importlib._bootstrap>", line 1581
      File "<frozen importlib._bootstrap>", line 1614
      File "/usr/lib/python3.4/doctest.py", line 101
        import pdb
      File "<frozen importlib._bootstrap>", line 284
      File "<frozen importlib._bootstrap>", line 938
      File "<frozen importlib._bootstrap>", line 1068
      File "<frozen importlib._bootstrap>", line 619
      File "<frozen importlib._bootstrap>", line 1581
      File "<frozen importlib._bootstrap>", 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 :mod:`importlib` module to
load data (bytecode and constants) from modules: ``870.1 KiB``. The traceback is
where the :mod:`importlib` loaded data most recently: on the ``import pdb``
line of the :mod:`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 ``<frozen importlib._bootstrap>`` and ``<unknown>`` files::

import linecache
    import os
    import tracemalloc

def display_top(snapshot, key_type='lineno', limit=10):
        snapshot = snapshot.filter_traces((
            tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
            tracemalloc.Filter(False, "<unknown>"),
        ))
        top_stats = snapshot.statistics(key_type)

print("Top %s lines" % limit)
        for index, stat in enumerate(top_stats[:limit], 1):
            frame = stat.traceback[0]
            # replace "/path/to/module/file.py" with "module/file.py"
            filename = os.sep.join(frame.filename.split(os.sep)[-2:])
            print("#%s: %s:%s: %.1f KiB"
                  % (index, 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)

Example of output of the Python test suite::

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: <string>: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

See :meth:`Snapshot.statistics` for more options.

API
---

Functions
^^^^^^^^^

.. function:: clear_traces()

Clear traces of memory blocks allocated by Python.