tracemalloc — Trace memory allocations — Python 3.14.3 documentation
Description: Source code: 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, St...

# `tracemalloc` — Trace memory allocations

Added in version 3.4.

Source code: 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 `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.

## 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 `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=}")

Output:

    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

### Functions

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()`.

  Added in version 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).

  Added in version 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'`.

  Examples:

  - `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

  Changed in version 3.5: The `'.pyo'` file extension is no longer replaced with `'.py'`.

  Changed in version 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 of a traceback.

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

  filename

    Filename (`str`).

  lineno

    Line number (`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.

    Changed in version 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.

  Changed in version 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.

  Changed in version 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.

  Changed in version 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.

    Example:

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

    Output:

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