[Python-Dev] PEP 454 (tracemalloc): new minimalist version

[Victor Stinner]

Hi,

I plan to push the new tracemalloc module this week-end, before the
next (and last) alpha version, except if someone complains :-) I
prefer to have one month before the first beta to have more time to
test the module. So if a major issue is raised, we may remove the
tracemalloc module before the final version.

Charles-François Natali reviewed my PEP 454 (tracemalloc). He
suggested me to split the module into two parts: keep only essential
parts in the tracemalloc Python (added to Python stdlib), and move
other tools to an external tool (hosted on PyPI). We may integrate
more functions (especially metrics) later (in Python 3.5), after they
are well tested externally. I updated the PEP and the implementation.
I will update the patch on the bug tracker later.

HTML version of the PEP:
http://www.python.org/dev/peps/pep-0454/


Since the previous version of the PEP, almost all changes are removed parts:

* Remove tasks: the tool now uses classic threads
* Remove functions to get metrics: the tool is now responsible to add
them to a snapshot
* Remove DisplayTop and DisplayTopTask class
* Remove TakeSnaphot class


PEP: 454
Title: Add a new tracemalloc module to trace Python memory allocations
Version: $Revision$
Last-Modified: $Date$
Author: Victor Stinner <victor.stinner at gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 3-September-2013
Python-Version: 3.4


Abstract
========

This PEP proposes to add a new ``tracemalloc`` module to trace memory
blocks allocated by Python.


Rationale
=========

Common debug tools tracing memory allocations record the C filename
and line number where the allocation occurs.  Using such tools to
analyze Python memory allocations does not help because most memory
blocks are allocated in the same C function, in ``PyMem_Malloc()`` for
example.

There are debug tools dedicated to the Python language like ``Heapy``
and ``PySizer``. These tools analyze objects type and/or content.
They are useful when most memory leaks are instances of the same type
and this type is only instantiated in a few functions. Problems arise
when the object type is very common like ``str`` or ``tuple``, and it
is hard to identify where these objects are instantiated.

Finding reference cycles is also a difficult problem.  There are
different tools to draw a diagram of all references.  These tools
cannot be used on large applications with thousands of objects because
the diagram is too huge to be analyzed manually.


Proposal
========

Using the customized allocation API from PEP 445, it becomes easy to
set up a hook on Python memory allocators. A hook can inspect Python
internals to retrieve Python tracebacks.

This PEP proposes to add a new ``tracemalloc`` module, as a debug tool
to trace memory blocks allocated by Python. The module provides the
following information:

* Computed differences between two snapshots to detect memory leaks
* Statistics on allocated memory blocks per filename and per line
  number: total size, number and average size of allocated memory blocks
* Traceback where a memory block was allocated

The API of the tracemalloc module is similar to the API of the
faulthandler module: ``enable()``, ``disable()`` and ``is_enabled()``
functions, an environment variable (``PYTHONFAULTHANDLER`` and
``PYTHONTRACEMALLOC``), and a ``-X`` command line option (``-X
faulthandler`` and ``-X tracemalloc``). See the
`documentation of the faulthandler module
<http://docs.python.org/3/library/faulthandler.html>`_.

The tracemalloc module has been written for CPython. Other
implementations of Python may not be able to provide it.


API
===

Main Functions
--------------

``clear_traces()`` function:

    Clear traces and statistics on Python memory allocations, and reset
    the ``get_traced_memory()`` counter.


``disable()`` function:

    Stop tracing Python memory allocations.

    See also ``enable()`` and ``is_enabled()`` functions.


``enable()`` function:

    Start tracing Python memory allocations.

    At fork, the module is automatically disabled in the child process.

    See also ``disable()`` and ``is_enabled()`` functions.


``get_stats()`` function:

    Get statistics on traced Python memory blocks as a dictionary
    ``{filename (str): {line_number (int): stats}}`` where *stats* in a
    ``(size: int, count: int)`` tuple, *filename* and *line_number* can
    be ``None``.

    Return an empty dictionary if the ``tracemalloc`` module is
    disabled.

    See also the ``get_traces()`` function.


``get_traced_memory()`` function:

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


``get_tracemalloc_memory()`` function:

    Get the memory usage in bytes of the ``tracemalloc`` module as a
    tuple: ``(size: int, free: int)``.

    * *size*: total size of bytes allocated by the module,
      including *free* bytes
    * *free*: number of free bytes available to store data


``is_enabled()`` function:

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

    See also ``enable()`` and ``disable()`` functions.


Trace Functions
---------------

``get_traceback_limit()`` function:

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

    Use the ``set_traceback_limit()`` function to change the limit.


``get_object_address(obj)`` function:

    Get the address of the main memory block of the specified Python object.

    A Python object can be composed by multiple memory blocks, the
    function only returns the address of the main memory block.

    See also ``get_object_trace()`` and ``gc.get_referrers()`` functions.


``get_object_trace(obj)`` function:

    Get the trace of a Python object *obj* as a ``(size: int,
    traceback)`` tuple where *traceback* is a tuple of ``(filename: str,
    lineno: int)`` tuples, *filename* and *lineno* can be ``None``.

    The function only returns the trace of the main memory block of the
    object.  The *size* of the trace is smaller than the total size of
    the object if the object is composed by more than one memory block.

    Return ``None`` if the ``tracemalloc`` module did not trace the
    allocation of the object.

    See also ``get_object_address()``, ``get_trace()``,
    ``get_traces()``, ``gc.get_referrers()`` and ``sys.getsizeof()``
    functions.


``get_trace(address)`` function:

    Get the trace of a memory block as a ``(size: int, traceback)``
    tuple where *traceback* is a tuple of ``(filename: str, lineno:
    int)`` tuples, *filename* and *lineno* can be ``None``.

    Return ``None`` if the ``tracemalloc`` module did not trace the
    allocation of the memory block.

    See also ``get_object_trace()``, ``get_stats()`` and
    ``get_traces()`` functions.


``get_traces()`` function:

    Get traces of Python memory allocations as a dictionary ``{address
    (int): trace}`` where *trace* is a ``(size: int, traceback)`` and
    *traceback* is a list of ``(filename: str, lineno: int)``.
    *traceback* can be empty, *filename* and *lineno* can be None.

    Return an empty dictionary if the ``tracemalloc`` module is disabled.

    See also ``get_object_trace()``, ``get_stats()`` and ``get_trace()``
    functions.


``set_traceback_limit(nframe: int)`` function:

    Set the maximum number of frames stored in the traceback of a trace
    of a memory block.

    Storing the traceback of each memory allocation has an important
    overhead on the memory usage. Use the ``get_tracemalloc_memory()``
    function to measure the overhead and the ``add_filter()`` function
    to select which memory allocations are traced.

    Use the ``get_traceback_limit()`` function to get the current limit.


Filter Functions
----------------

``add_filter(filter)`` function:

    Add a new filter on Python memory allocations, *filter* is a
    ``Filter`` instance.

    All inclusive filters are applied at once, a memory allocation is
    only ignored if no inclusive filters match its trace. A memory
    allocation is ignored if at least one exclusive filter matchs its
    trace.

    The new filter is not applied on already collected traces. Use the
    ``clear_traces()`` function to ensure that all traces match the new
    filter.

``add_include_filter(filename: str, lineno: int=None, traceback:
bool=False)`` function:

    Add an inclusive filter: helper for the ``add_filter()`` method
    creating a ``Filter`` instance with the ``Filter.include`` attribute
    set to ``True``.

    Example: ``tracemalloc.add_include_filter(tracemalloc.__file__)``
    only includes memory blocks allocated by the ``tracemalloc`` module.


``add_exclude_filter(filename: str, lineno: int=None, traceback:
bool=False)`` function:

    Add an exclusive filter: helper for the ``add_filter()`` method
    creating a ``Filter`` instance with the ``Filter.include`` attribute
    set to ``False``.

    Example: ``tracemalloc.add_exclude_filter(tracemalloc.__file__)``
    ignores memory blocks allocated by the ``tracemalloc`` module.


``clear_filters()`` function:

    Reset the filter list.

    See also the ``get_filters()`` function.


``get_filters()`` function:

    Get the filters on Python memory allocations as list of ``Filter``
    instances.

    See also the ``clear_filters()`` function.


Filter
------

``Filter(include: bool, pattern: str, lineno: int=None, traceback:
bool=False)`` class:

    Filter to select which memory allocations are traced. Filters can be
    used to reduce the memory usage of the ``tracemalloc`` module, which
    can be read using the ``get_tracemalloc_memory()`` function.

``match(filename: str, lineno: int)`` method:

    Return ``True`` if the filter matchs the filename and line number,
    ``False`` otherwise.

``match_filename(filename: str)`` method:

    Return ``True`` if the filter matchs the filename, ``False`` otherwise.

``match_lineno(lineno: int)`` method:

    Return ``True`` if the filter matchs the line number, ``False``
    otherwise.

``match_traceback(traceback)`` method:

    Return ``True`` if the filter matchs the *traceback*, ``False``
    otherwise.

    *traceback* is a tuple of ``(filename: str, lineno: int)`` tuples.

``include`` attribute:

    If *include* is ``True``, only trace memory blocks allocated in a
    file with a name matching filename ``pattern`` at line number
    ``lineno``.

    If *include* is ``False``, ignore memory blocks allocated in a file
    with a name matching filename ``pattern`` at line number ``lineno``.

``lineno`` attribute:

    Line number (``int``). If is is ``None`` or less than ``1``, it
    matches any line number.

``pattern`` attribute:

    The filename *pattern* can contain one or many ``*`` joker
    characters which match any substring, including an empty string. The
    ``.pyc`` and ``.pyo`` file extensions are replaced with ``.py``. On
    Windows, the comparison is case insensitive and the alternative
    separator ``/`` is replaced with the standard separator ``\``.

``traceback`` attribute:

    If *traceback* is ``True``, all frames of the traceback are checked.
    If *traceback* is ``False``, only the most recent frame is checked.

    This attribute is ignored if the traceback limit is less than ``2``.
    See the ``get_traceback_limit()`` function.


GroupedStats
------------

``GroupedStats(timestamp: datetime.datetime, stats: dict, group_by:
str, cumulative=False, metrics: dict=None)`` class:

    Top of allocated memory blocks grouped by *group_by* as a
    dictionary.

    The ``Snapshot.top_by()`` method creates a ``GroupedStats``
    instance.

``compare_to(old_stats: GroupedStats=None)`` method:

    Compare to an older ``GroupedStats`` instance.  Return a
    ``StatsDiff`` instance.

    The ``StatsDiff.differences`` list is not sorted: call the
    ``StatsDiff.sort()`` method to sort the list.

    ``None`` values are replaced with an empty string for filenames or
    zero for line numbers, because ``str`` and ``int`` cannot be
    compared to ``None``.

``cumulative`` attribute:

    If ``True``, cumulate size and count of memory blocks of all frames
    of the traceback of a trace, not only the most recent frame.

``metrics`` attribute:

    Dictionary storing metrics read when the snapshot was created:
    ``{name (str): metric}`` where *metric* type is ``Metric``.

``group_by`` attribute:

    Determine how memory allocations were grouped: see
    ``Snapshot.top_by()`` for the available values.

``stats`` attribute:

    Dictionary ``{key: stats}`` where the *key* type depends on the
    ``group_by`` attribute and *stats* is a ``(size: int, count: int)``
    tuple.

    See the ``Snapshot.top_by()`` method.

``timestamp`` attribute:

    Creation date and time of the snapshot, ``datetime.datetime``
    instance.


Metric
------

``Metric(name: str, value: int, format: str)`` class:

    Value of a metric when a snapshot is created.

``name`` attribute:

    Name of the metric.

``value`` attribute:

    Value of the metric.

``format`` attribute:

    Format of the metric (``str``).


Snapshot
--------

``Snapshot(timestamp: datetime.datetime, traces: dict=None, stats:
dict=None)`` class:

    Snapshot of traces and statistics on memory blocks allocated by Python.

``add_metric(name: str, value: int, format: str)`` method:

    Helper to add a ``Metric`` instance to ``Snapshot.metrics``.  Return
    the newly created ``Metric`` instance.

    Raise an exception if the name is already present in
    ``Snapshot.metrics``.


``apply_filters(filters)`` method:

    Apply filters on the ``traces`` and ``stats`` dictionaries,
    *filters* is a list of ``Filter`` instances.


``create(traces=False)`` classmethod:

    Take a snapshot of traces and/or statistics of allocated memory blocks.

    If *traces* is ``True``, ``get_traces()`` is called and its result
    is stored in the ``Snapshot.traces`` attribute. This attribute
    contains more information than ``Snapshot.stats`` and uses more
    memory and more disk space. If *traces* is ``False``,
    ``Snapshot.traces`` is set to ``None``.

    Tracebacks of traces are limited to ``traceback_limit`` frames. Call
    ``set_traceback_limit()`` before calling ``Snapshot.create()`` to
    store more frames.

    The ``tracemalloc`` module must be enabled to take a snapshot. See
    the the ``enable()`` function.

``get_metric(name, default=None)`` method:

    Get the value of the metric called *name*. Return *default* if the
    metric does not exist.


``load(filename, traces=True)`` classmethod:

    Load a snapshot from a file.

    If *traces* is ``False``, don't load traces.


``top_by(group_by: str, cumulative: bool=False)`` method:

    Compute top statistics grouped by *group_by* as a ``GroupedStats``
    instance:

    =====================  ========================
================================
    group_by               description               key type
    =====================  ========================
================================
    ``'filename'``         filename                  ``str``
    ``'line'``             filename and line number  ``(filename: str,
lineno: int)``
    ``'address'``          memory block address      ``int``
    ``'traceback'``        traceback                 ``(address: int,
traceback)``
    =====================  ========================
================================

    The ``traceback`` type is a tuple of ``(filename: str, lineno:
    int)`` tuples, *filename* and *lineno* can be ``None``.

    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* parameter is ignored if *group_by*
    is ``'address'`` or if the traceback limit is less than ``2``.


``write(filename)`` method:

    Write the snapshot into a file.


``metrics`` attribute:

    Dictionary storing metrics read when the snapshot was created:
    ``{name (str): metric}`` where *metric* type is ``Metric``.

``stats`` attribute:

    Statistics on traced Python memory, result of the ``get_stats()``
    function.

``traceback_limit`` attribute:

    Maximum number of frames stored in a trace of a memory block
    allocated by Python.

``traces`` attribute:

    Traces of Python memory allocations, result of the ``get_traces()``
    function, can be ``None``.

``timestamp`` attribute:

    Creation date and time of the snapshot, ``datetime.datetime``
    instance.


StatsDiff
---------

``StatsDiff(differences, old_stats, new_stats)`` class:

    Differences between two ``GroupedStats`` instances.

    The ``GroupedStats.compare_to()`` method creates a ``StatsDiff``
    instance.

``sort()`` method:

    Sort the ``differences`` list from the biggest difference to the
    smallest difference. Sort by ``abs(size_diff)``, *size*,
    ``abs(count_diff)``, *count* and then by *key*.

``differences`` attribute:

    Differences between ``old_stats`` and ``new_stats`` as a list of
    ``(size_diff, size, count_diff, count, key)`` tuples. *size_diff*,
    *size*, *count_diff* and *count* are ``int``. The key type depends
    on the ``GroupedStats.group_by`` attribute of ``new_stats``: see the
    ``Snapshot.top_by()`` method.

``old_stats`` attribute:

    Old ``GroupedStats`` instance, can be ``None``.

``new_stats`` attribute:

    New ``GroupedStats`` instance.


Links
=====

tracemalloc:

* `#18874: Add a new tracemalloc module to trace Python
  memory allocations <http://bugs.python.org/issue18874>`_
* `pytracemalloc on PyPI
  <https://pypi.python.org/pypi/pytracemalloc>`_

Similar projects:

* `Meliae: Python Memory Usage Analyzer
  <https://pypi.python.org/pypi/meliae>`_
* `Guppy-PE: umbrella package combining Heapy and GSL
  <http://guppy-pe.sourceforge.net/>`_
* `PySizer <http://pysizer.8325.org/>`_: developed for Python 2.4
* `memory_profiler <https://pypi.python.org/pypi/memory_profiler>`_
* `pympler <http://code.google.com/p/pympler/>`_
* `Dozer <https://pypi.python.org/pypi/Dozer>`_: WSGI Middleware version
  of the CherryPy memory leak debugger
* `objgraph <http://mg.pov.lt/objgraph/>`_
* `caulk <https://github.com/smartfile/caulk/>`_

Copyright
=========

This document has been placed in the public domain.


Victor