iSharkFly-Open
/
python-peps
mirror of https://github.com/python/peps.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
python-peps/pep-0454.txt

540 lines
18 KiB
Plaintext
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

PEP: 454
Title: Add a new tracemalloc module to trace Python memory allocations
Version: $Revision$
Last-Modified: $Date$
Author: Victor Stinner <victor.stinner@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
=========
Classic generic tools like Valgrind can get the C traceback where a
memory block was allocated. 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. Moreover, Python
has an allocator for small object called "pymalloc" which keeps free
blocks for efficiency. This is not well handled by these tools.
There are debug tools dedicated to the Python language like ``Heapy``
``Pympler`` and ``Meliae`` which lists all live objects using the
garbage module (functions like ``gc.get_objects()``,
``gc.get_referrers()`` and ``gc.get_referents()``), compute their size
(ex: using ``sys.getsizeof()``) and group objects by type. These tools
provide a better estimation of the memory usage of an application. 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. The idea of getting the current
traceback comes from the faulthandler module. The faulthandler dumps
the traceback of all Python threads on a crash, here is the idea is to
get the traceback of the current Python thread when a memory block is
allocated by Python.
This PEP proposes to add a new ``tracemalloc`` module, a debug tool
to trace memory blocks allocated by Python. The module 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
* Computed differences between two snapshots to detect memory leaks
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 idea of tracing memory allocations is not new. It was first
implemented in the PySizer project in 2005. PySizer was implemented
differently: the traceback was stored in frame objects and some Python
types were linked the trace with the name of object type. PySizer patch
on CPython adds a overhead on performances and memory footprint, even if
the PySizer was not used. tracemalloc attachs a traceback to the
underlying layer, to memory blocks, and has no overhead when the module
is disabled.
The tracemalloc module has been written for CPython. Other
implementations of Python may not be able to provide it.
API
===
To trace most memory blocks allocated by Python, the module should be
enabled as early as possible by setting the ``PYTHONTRACEMALLOC``
environment variable to ``1``, or by using ``-X tracemalloc`` command
line option. The ``tracemalloc.enable()`` 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. The ``set_traceback_limit()``
function can be used at runtime to set the limit.
By default, Python memory blocks allocated in the ``tracemalloc`` module
are ignored using a filter. Use ``clear_filters()`` to trace also these
memory allocations.
Main Functions
--------------
``reset()`` function:
Clear traces of memory blocks allocated by Python.
See also ``disable()``.
``disable()`` function:
Stop tracing Python memory allocations and clear traces of memory
blocks allocated by Python.
See also ``enable()`` and ``is_enabled()`` functions.
``enable()`` function:
Start tracing Python memory allocations.
See also ``disable()`` and ``is_enabled()`` functions.
``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 used to
store traces of memory blocks. Return an ``int``.
``is_enabled()`` function:
``True`` if the ``tracemalloc`` module is tracing Python memory
allocations, ``False`` otherwise.
See also ``disable()`` and ``enable()`` functions.
Trace Functions
---------------
When Python allocates a memory block, ``tracemalloc`` attachs a "trace" to
it to store information on it: its size in bytes and the traceback where the
allocation occured.
The following functions give access to these traces. A trace is a ``(size: int,
traceback)`` tuple. *size* is the size of the memory block in bytes.
*traceback* is a tuple of frames sorted from the most recent to the oldest
frame, limited to ``get_traceback_limit()`` frames. A frame is
a ``(filename: str, lineno: int)`` tuple.
If ``tracemalloc`` failed to get the whole traceback, the traceback may be
empty, truncated or contain ``"<unknown>"`` filename and line number 0.
Example of a trace: ``(32, (('x.py', 7), ('x.py', 11)))``. The memory block
has a size of 32 bytes and was allocated at ``x.py:7``, line called from line
``x.py:11``.
``get_object_traceback(obj)`` function:
Get the traceback where the Python object *obj* was allocated.
Return a tuple of ``(filename: str, lineno: int)`` tuples.
Return ``None`` if the ``tracemalloc`` module is disabled or did not
trace the allocation of the object.
See also ``gc.get_referrers()`` and ``sys.getsizeof()`` functions.
``get_traceback_limit()`` function:
Get the maximum number of frames stored in the traceback of a trace.
By default, a trace of an allocated memory block only stores the
most recent frame: the limit is ``1``.
Use the ``set_traceback_limit()`` function to change the limit.
``get_traces()`` function:
Get traces of memory blocks allocated by Python. Return a list of
``(size: int, traceback: tuple)`` tuples. *traceback* is a tuple of
``(filename: str, lineno: int)`` tuples.
The list of traces do not include memory blocks allocated before the
``tracemalloc`` module was enabled and memory blocks ignored by
filters (see ``get_filters()()``).
Return an empty list if the ``tracemalloc`` module is disabled.
See also the ``get_object_traceback()`` function.
``set_traceback_limit(nframe: int)`` function:
Set the maximum number of frames stored in the traceback of a trace.
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.
The ``PYTHONTRACEMALLOC`` environment variable and the ``-X``
``tracemalloc=NFRAME`` command line option can be used to set a
limit at startup.
``take_snapshot()`` function:
Take a snapshot of traces of memory blocks allocated by Python.
Tracebacks of traces are limited to ``traceback_limit`` frames. Use
``set_traceback_limit()`` to store more frames.
The ``tracemalloc`` module must be enabled to take a snapshot, see
the the ``enable()`` function.
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
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
``reset()`` function to ensure that all traces match the new filter.
``clear_filters()`` function:
Clear the filter list.
See also the ``get_filters()`` function.
``get_filters()`` function:
Get the filters on Python memory allocations. Return a list of
``Filter`` instances.
By default, there is one exclusive filter to ignore Python memory
blocks allocated by the ``tracemalloc`` module.
See also the ``clear_filters()`` function.
Filter
------
``Filter(include: bool, filename_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.
The ``'*'`` joker character can be used in *filename_pattern* to
match any substring, including 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 ``'\'``.
For example, use ``Filter(False, "<unknown>")`` to exclude empty
tracebacks.
``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``) of the filter. If *lineno* is ``None``, the
filter matches any line number.
``filename_pattern`` attribute:
Filename pattern (``str``) of the filter.
``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(key_type: str, stats: dict, cumulative: bool)`` class:
Statistics of allocated memory blocks grouped by *key_type* as a
dictionary.
The ``Snapshot.group_by()`` method creates a ``GroupedStats``
instance.
``compare_to(old_stats: GroupedStats, sort=True)`` method:
Compare statistics to an older ``GroupedStats`` instance. Return a
list of ``Statistic`` instances.
The result is sorted from the biggest to the smallest by
``abs(size_diff)``, *size*, ``abs(count_diff)``, *count* and then by
*key*. Set the *sort* parameter to ``False`` to get the list
unsorted.
See also the ``statistics()`` method.
``statistics(sort=True)`` method:
Get statistics as a list of ``Statistic`` instances.
``Statistic.size_diff`` and ``Statistic.count_diff`` attributes are
set to zero.
The result is sorted from the biggest to the smallest by
``abs(size_diff)``, *size*, ``abs(count_diff)``, *count* and then by
*key*. Set the *sort* parameter to ``False`` to get the list
unsorted.
See also the ``compare_to()`` method.
``cumulative`` attribute:
If ``True``, size and count of memory blocks of all frames of the
traceback of a trace were cumulated, not only the most recent frame.
``key_type`` attribute:
Determine how memory allocations were grouped: see
``Snapshot.group_by()`` for the available values.
``stats`` attribute:
Dictionary ``{key: [size: int, count: int]}`` where the type of
*key* depends on the ``key_type`` attribute, *size* is the total
size of memory blocks and *count* is the number of memory blocks.
See the ``Snapshot.group_by()`` method.
Snapshot
--------
``Snapshot(timestamp: datetime.datetime, traceback_limit: int, traces: dict=None)`` class:
Snapshot of traces of memory blocks allocated by Python.
The ``take_snapshot()`` function create a snapshot instance.
``apply_filters(filters)`` method:
Apply filters on the ``traces`` dictionary, *filters* is a list of
``Filter`` instances.
``dump(filename)`` method:
Write the snapshot into a file.
Use ``load()`` to reload the snapshot.
``load(filename)`` classmethod:
Load a snapshot from a file.
See also ``dump()``.
``group_by(key_type: str, cumulative: bool=False)`` method:
Group statistics by *key_type*. Return a ``GroupedStats`` instance.
Key types:
===================== ======================== ================================================
key_type description type
===================== ======================== ================================================
``'filename'`` filename ``str``
``'lineno'`` filename and line number ``(filename: str, lineno: int)``
``'traceback'`` traceback tuple of ``(filename: str, lineno: int)`` tuples
===================== ======================== ================================================
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 set to ``False`` if
*key_type* is ``'traceback'``, or if the ``traceback_limit``
attribute is less than ``2``.
``traceback_limit`` attribute:
Maximum number of frames stored in the traceback of ``traces``,
result of the ``get_traceback_limit()`` function.
``traces`` attribute:
Traces of all memory blocks allocated by Python, result of the
``get_traces()`` function: list of ``(size: int, traceback: tuple)``
tuples, *traceback* is a tuple of ``(filename: str, lineno: int)``
tuples.
``timestamp`` attribute:
Creation date and time of the snapshot, ``datetime.datetime``
instance.
Statistic
---------
``Statistic(key, size, size_diff, count, count_diff)`` class:
Statistic on memory allocations.
``size_diff`` and ``count_diff`` attributes are the difference
between two ``GroupedStats`` instance.
``GroupedStats.compare_to()`` and ``GroupedStats.statistics()``
return a list of ``Statistic`` instances.
``key`` attribute:
Key identifying the statistic. The key type depends on
``GroupedStats.key_type``, see the ``Snapshot.group_by()`` method.
``count`` attribute:
Number of memory blocks (``int``).
``count_diff`` attribute:
Difference of number of memory blocks (``int``).
``size`` attribute:
Total size of memory blocks in bytes (``int``).
``size_diff`` attribute:
Difference of total size of memory blocks in bytes (``int``).
Prior Work
==========
* `Python Memory Validator
<http://www.softwareverify.com/python/memory/index.html>`_ (2005-2013):
commercial Python memory validator developed by Software Verification.
It uses the Python Reflection API.
* `PySizer <http://pysizer.8325.org/>`_: Google Summer of Code 2005 project by
Nick Smallbone.
* `Heapy
<http://guppy-pe.sourceforge.net/>`_ (2006-2013):
part of the Guppy-PE project written by Sverker Nilsson.
* Draft PEP: `Support Tracking Low-Level Memory Usage in CPython
<http://svn.python.org/projects/python/branches/bcannon-sandboxing/PEP.txt>`_
(Brett Canon, 2006)
* Muppy: project developed in 2008 by Robert Schuppenies.
* `asizeof <http://code.activestate.com/recipes/546530/>`_:
a pure Python module to estimate the size of objects by Jean
Brouwers (2008).
* `Heapmonitor <http://www.scons.org/wiki/LudwigHaehne/HeapMonitor>`_:
It provides facilities to size individual objects and can track all objects
of certain classes. It was developed in 2008 by Ludwig Haehne.
* `Pympler <http://code.google.com/p/pympler/>`_ (2008-2011):
project based on asizeof, muppy and HeapMonitor
* `objgraph <http://mg.pov.lt/objgraph/>`_ (2008-2012)
* `Dozer <https://pypi.python.org/pypi/Dozer>`_: WSGI Middleware version
of the CherryPy memory leak debugger, written by Marius Gedminas (2008-2013)
* `Meliae
<https://pypi.python.org/pypi/meliae>`_:
Python Memory Usage Analyzer developed by John A Meinel since 2009
* `gdb-heap <https://fedorahosted.org/gdb-heap/>`_: gdb script written in
Python by Dave Malcom (2010-2011) to analyze the usage of the heap memory
* `memory_profiler <https://pypi.python.org/pypi/memory_profiler>`_:
written by Fabian Pedregosa (2011-2013)
* `caulk <https://github.com/smartfile/caulk/>`_: written by Ben Timby in 2012
See also `Pympler Related Work
<http://pythonhosted.org/Pympler/related.html>`_.
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>`_
Copyright
=========
This document has been placed in the public domain.
..
Local Variables:
mode: indented-text
indent-tabs-mode: nil
sentence-end-double-space: t
fill-column: 70
coding: utf-8
End:
Reference in New Issue View Git Blame Copy Permalink