Hi, Antoine Pitrou suggested me to write a PEP to discuss the API of the new tracemalloc module that I proposed to add to Python 3.4. Here you have.
If you prefer to read the HTML version: http://www.python.org/dev/peps/pep-0454/ See also the documentation of the current implementation of the module. http://hg.python.org/features/tracemalloc/file/tip/Doc/library/tracemalloc.rst The documentaion contains examples and a short "tutorial". PEP: 454 Title: Add a new tracemalloc module to trace Python memory allocations Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stin...@gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 3-September-2013 Python-Version: 3.4 Abstract ======== Add a new ``tracemalloc`` module to trace Python memory allocations. Rationale ========= Common debug tools tracing memory allocations read the C filename and number. Using such tool to analyze Python memory allocations does not help because most memory allocations are done in the same C function, ``PyMem_Malloc()`` for example. There are debug tools dedicated to the Python languages like ``Heapy`` and ``PySizer``. These projects analyze objects type and/or content. These tools are useful when the most memory leak are instances of the same type and this type in allocated only in a few functions. The problem is when the object type is very common like ``str`` or ``tuple``, and it is hard to identify where these objects are allocated. Finding reference cycles is also a difficult task. There are different tools to draw a diagram of all references. These tools cannot be used huge on large applications with thousands of objects because the diagram is too huge to be analyzed manually. Proposal ======== Using the PEP 445, it becomes easy to setup an hook on Python memory allocators. The hook can inspect the current Python frame to get the Python filename and line number. This PEP proposes to add a new ``tracemalloc`` module. It is a debug tool to trace memory allocations made by Python. The module provides the following information: * Statistics on Python memory allocations per Python filename and line number: size, number, and average size of allocations * Compute differences between two snapshots of Python memory allocations * Location of a Python memory allocation: size in bytes, Python filename and line number Command line options ==================== The ``python -m tracemalloc`` command can be used to analyze and compare snapshots. The command takes a list of snapshot filenames and has the following options. ``-g``, ``--group-per-file`` Group allocations per filename, instead of grouping per line number. ``-n NTRACES``, ``--number NTRACES`` Number of traces displayed per top (default: 10). ``--first`` Compare with the first snapshot, instead of comparing with the previous snapshot. ``--include PATTERN`` Only include filenames matching pattern *PATTERN*. The option can be specified multiple times. See ``fnmatch.fnmatch()`` for the syntax of patterns. ``--exclude PATTERN`` Exclude filenames matching pattern *PATTERN*. The option can be specified multiple times. See ``fnmatch.fnmatch()`` for the syntax of patterns. ``-S``, ``--hide-size`` Hide the size of allocations. ``-C``, ``--hide-count`` Hide the number of allocations. ``-A``, ``--hide-average`` Hide the average size of allocations. ``-P PARTS``, ``--filename-parts=PARTS`` Number of displayed filename parts (default: 3). ``--color`` Force usage of colors even if ``sys.stdout`` is not a TTY device. ``--no-color`` Disable colors if ``sys.stdout`` is a TTY device. API === To trace the most Python memory allocations, the module should be enabled as early as possible in your application by calling ``tracemalloc.enable()`` function, by setting the ``PYTHONTRACEMALLOC`` environment variable to ``1``, or by using ``-X tracemalloc`` command line option. Functions --------- ``enable()`` function: Start tracing Python memory allocations. ``disable()`` function: Stop tracing Python memory allocations and stop the timer started by ``start_timer()``. ``is_enabled()`` function: Get the status of the module: ``True`` if it is enabled, ``False`` otherwise. ``get_object_address(obj)`` function: Get the address of the memory block of the specified Python object. ``get_object_trace(obj)`` function: Get the trace of a Python object *obj* as a ``trace`` instance. Return ``None`` if the tracemalloc module did not save the location when the object was allocated, for example if the module was disabled. ``get_process_memory()`` function: Get the memory usage of the current process as a meminfo namedtuple with two attributes: * ``rss``: Resident Set Size in bytes * ``vms``: size of the virtual memory in bytes Return ``None`` if the platform is not supported. Use the ``psutil`` module if available. ``get_stats()`` function: Get statistics on Python memory allocations per Python filename and per Python line number. Return a dictionary ``{filename: str -> {line_number: int -> stats: line_stat}}`` where *stats* in a ``line_stat`` instance. *filename* and *line_number* can be ``None``. Return an empty dictionary if the tracemalloc module is disabled. ``get_traces(obj)`` function: Get all traces of a Python memory allocations. Return a dictionary ``{pointer: int -> trace}`` where *trace* is a ``trace`` instance. Return an empty dictionary if the ``tracemalloc`` module is disabled. ``start_timer(delay: int, func: callable, args: tuple=(), kwargs: dict={})`` function: Start a timer calling ``func(*args, **kwargs)`` every *delay* seconds. The timer is based on the Python memory allocator, it is not real time. *func* is called after at least *delay* seconds, it is not called exactly after *delay* seconds if no Python memory allocation occurred. If ``start_timer()`` is called twice, previous parameters are replaced. The timer has a resolution of 1 second. ``start_timer()`` is used by ``DisplayTop`` and ``TakeSnapshot`` to run regulary a task. ``stop_timer()`` function: Stop the timer started by ``start_timer()``. trace class ----------- ``trace`` class: This class represents debug information of an allocated memory block. ``size`` attribute: Size in bytes of the memory block. ``filename`` attribute: Name of the Python script where the memory block was allocated, ``None`` if unknown. ``lineno`` attribute: Line number where the memory block was allocated, ``None`` if unknown. line_stat class ---------------- ``line_stat`` class: Statistics on Python memory allocations of a specific line number. ``size`` attribute: Total size in bytes of all memory blocks allocated on the line. ``count`` attribute: Number of memory blocks allocated on the line. DisplayTop class ---------------- ``DisplayTop(count: int=10, file=sys.stdout)`` class: Display the list of the *count* biggest memory allocations into *file*. ``display()`` method: Display the top once. ``start(delay: int)`` method: Start a task using ``tracemalloc`` timer to display the top every *delay* seconds. ``stop()`` method: Stop the task started by the ``DisplayTop.start()`` method ``color`` attribute: If ``True``, ``display()`` uses color. The default value is ``file.isatty()``. ``compare_with_previous`` attribute: If ``True`` (default value), ``display()`` compares with the previous snapshot. If ``False``, compare with the first snapshot. ``filename_parts`` attribute: Number of displayed filename parts (int, default: ``3``). Extra parts are replaced with ``"..."``. ``group_per_file`` attribute: If ``True``, group memory allocations per Python filename. If ``False`` (default value), group allocation per Python line number. ``show_average`` attribute: If ``True`` (default value), ``display()`` shows the average size of allocations. ``show_count`` attribute: If ``True`` (default value), ``display()`` shows the number of allocations. ``show_size`` attribute: If ``True`` (default value), ``display()`` shows the size of allocations. ``user_data_callback`` attribute: Optional callback collecting user data (callable, default: ``None``). See ``Snapshot.create()``. Snapshot class -------------- ``Snapshot()`` class: Snapshot of Python memory allocations. Use ``TakeSnapshot`` to take regulary snapshots. ``create(user_data_callback=None)`` method: Take a snapshot. If *user_data_callback* is specified, it must be a callable object returning a list of ``(title: str, format: str, value: int)``. *format* must be ``'size'``. The list must always have the same length and the same order to be able to compute differences between values. Example: ``[('Video memory', 'size', 234902)]``. ``filter_filenames(patterns: list, include: bool)`` method: Remove filenames not matching any pattern of *patterns* if *include* is ``True``, or remove filenames matching a pattern of *patterns* if *include* is ``False`` (exclude). See ``fnmatch.fnmatch()`` for the syntax of a pattern. ``load(filename)`` classmethod: Load a snapshot from a file. ``write(filename)`` method: Write the snapshot into a file. ``pid`` attribute: Identifier of the process which created the snapshot (int). ``process_memory`` attribute: Result of the ``get_process_memory()`` function, can be ``None``. ``stats`` attribute: Result of the ``get_stats()`` function (dict). ``timestamp`` attribute: Creation date and time of the snapshot, ``datetime.datetime`` instance. ``user_data`` attribute: Optional list of user data, result of *user_data_callback* in ``Snapshot.create()`` (default: None). TakeSnapshot class ------------------ ``TakeSnapshot`` class: Task taking snapshots of Python memory allocations: write them into files. By default, snapshots are written in the current directory. ``start(delay: int)`` method: Start a task taking a snapshot every delay seconds. ``stop()`` method: Stop the task started by the ``TakeSnapshot.start()`` method. ``take_snapshot()`` method: Take a snapshot. ``filename_template`` attribute: Template (``str``) used to create a filename. The following variables can be used in the template: * ``$pid``: identifier of the current process * ``$timestamp``: current date and time * ``$counter``: counter starting at 1 and incremented at each snapshot The default pattern is ``'tracemalloc-$counter.pickle'``. ``user_data_callback`` attribute: Optional callback collecting user data (callable, default: ``None``). See ``Snapshot.create()``. Links ===== Python issues: * `#18874: Add a new tracemalloc module to trace Python memory allocations <http://bugs.python.org/issue18874>`_ 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 into the public domain. _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
