# `tracemalloc` — Trace memory allocations

Adicionado na versão 3.4.

Código-fonte: 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

- Estatísticas sobre blocos de memória alocados por nome de arquivo e por número de linha: tamanho total, número e tamanho médio dos blocos de memória alocados

- Calcula as diferenças entre dois instantâneos para detectar vazamentos de memória

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.

## Exemplos

### Exibe o 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 `collections` module allocated `244 KiB` to build `namedtuple` types.

See `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 `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.

### 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 `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 `<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]
            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)

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 `Snapshot.statistics()` for more options.

#### 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=}")

Saída:

    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

### Funções

tracemalloc.clear_traces()

  Clear traces of memory blocks allocated by Python.

  See also `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.

  See also `get_traced_memory()`.

  Adicionado na versão 3.9.

tracemalloc.get_tracemalloc_memory()

  Get the memory usage in bytes of the `tracemalloc` module used to store traces of memory blocks. Return an `int`.

tracemalloc.is_tracing()

  `True` if the `tracemalloc` module is tracing Python memory allocations, `False` otherwise.

  See also `start()` and `stop()` functions.

tracemalloc.start(nframe: int = 1)

  Start tracing Python memory allocations: install hooks on Python memory allocators. Collected tracebacks of traces will be limited to nframe frames. By default, a trace of a memory block only stores the most recent frame: the limit is `1`. nframe must be greater or equal to `1`.

  You can still read the original number of total frames that composed the traceback by looking at the `Traceback.total_nframe` attribute.

  Storing more than `1` frame is only useful to compute statistics grouped by `'traceback'` or to compute cumulative statistics: see the `Snapshot.compare_to()` and `Snapshot.statistics()` methods.

  Storing more frames increases the memory and CPU overhead of the `tracemalloc` module. Use the `get_tracemalloc_memory()` function to measure how much memory is used by the `tracemalloc` module.

  The `PYTHONTRACEMALLOC` environment variable (`PYTHONTRACEMALLOC=NFRAME`) and the `-X` `tracemalloc=NFRAME` command line option can be used to start tracing at startup.

  See also `stop()`, `is_tracing()` and `get_traceback_limit()` functions.

tracemalloc.stop()

  Stop tracing Python memory allocations: uninstall hooks on Python memory allocators. Also clears all previously collected traces of memory blocks allocated by Python.

  Call `take_snapshot()` function to take a snapshot of traces before clearing them.

  See also `start()`, `is_tracing()` and `clear_traces()` functions.

tracemalloc.take_snapshot()

  Take a snapshot of traces of memory blocks allocated by Python. Return a new `Snapshot` instance.

  The snapshot does not include memory blocks allocated before the `tracemalloc` module started to trace memory allocations.

  Tracebacks of traces are limited to `get_traceback_limit()` frames. Use the nframe parameter of the `start()` function to store more frames.

  The `tracemalloc` module must be tracing memory allocations to take a snapshot, see the `start()` function.

  See also the `get_object_traceback()` function.

### DomainFilter

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

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

  Adicionado na versão 3.6.

  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.

### Filter

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

  Filter on traces of memory blocks.

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

  Exemplos:

  - `Filter(True, subprocess.__file__)` only includes traces of the `subprocess` module

  - `Filter(False, tracemalloc.__file__)` excludes traces of the `tracemalloc` module

  - `Filter(False, "<unknown>")` excludes empty tracebacks

  Alterado na versão 3.5: The `'.pyo'` file extension is no longer replaced with `'.py'`.

  Alterado na versão 3.6: Added the `domain` attribute.

  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 de um Traceback

  The `Traceback` class is a sequence of `Frame` instances.

  filename

    Filename (`str`).

  lineno

    Número da linha (`int`).

### Snapshot

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)

    Write the snapshot into a file.

    Use `load()` to reload the snapshot.

  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.

    Alterado na versão 3.6: `DomainFilter` instances are now also accepted in filters.

  classmethod load(filename)

    Load a snapshot from a file.

    See also `dump()`.

  statistics(key_type: str, cumulative: bool = False)

    Get statistics as a sorted list of `Statistic` instances grouped by key_type:

    key_type | description
    --- | ---
    `'filename'` | filename
    `'lineno'` | filename and line number
    `'traceback'` | traceback

    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.

### Statistic

class tracemalloc.Statistic

  Statistic on memory allocations.

  `Snapshot.statistics()` returns a list of `Statistic` instances.

  See also the `StatisticDiff` class.

  count

    Number of memory blocks (`int`).

  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.

### Trace

class tracemalloc.Trace

  Trace of a memory block.

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

  Alterado na versão 3.6: Added the `domain` attribute.

  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.

### Traceback

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 `"<unknown>"` 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.

  Alterado na versão 3.7: 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.

  Alterado na versão 3.9: 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.

    Exemplo:

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

    Saída:

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