"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

   3.5 sürümünde değişti: The "'.pyo'" file extension is no longer
   replaced with "'.py'".

   3.6 sürümünde değişti: 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.

      3.6 sürümünde değişti: "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.

   3.6 sürümünde değişti: 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.

   3.7 sürümünde değişti: 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.

   3.9 sürümünde değişti: 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())
