Hi, I worked on the implementation of the tracemalloc module and its PEP 454. I consider that this third version of the PEP is ready for a final review. What should be done to finish the PEP? HTML version of the PEP 454: http://www.python.org/dev/peps/pep-0454/ Full documentation of the tracemalloc module, with examples, a short tutorial and the documentation of the command line (python -m tracemalloc): http://www.haypocalc.com/tmp/tracemalloc/library/tracemalloc.html I addressed all points of my own TODO list, even minor tasks. I still have some minor tasks, but only on the implementation, not on the API of the module. The API is designed to process easily data collected by tracemalloc and to be extensible. It should now be easy to write a GUI to display these data for example. API changes between versions 2 and 3 of the PEP: * Add Metric class: in version 2, a snapshot had an "user_data" attribute which was not defined, not even its type. Metrics are now formalized, displayed and can be compared. They are used to track the process memory, number of Python objects, size of memory traced by tracemalloc, etc. The API also allows you to add your own metrics. * Rewrite the "scheduler" API. I removed the restriction of a single task at the same time. tracemalloc now supports multiple tasks which have a new trigger: threshold on the traced memory. I added a new Task class. DisplayTopTask and TakeSnapshotTask classes inherit from Task, to inherit its methods and attributes. It is now possible to only schedule a task "repeat" times. * Add DisplayTop.display() method, more convinient than having to create a DisplayTopTask instance. * Remove Frame, Trace and TraceStats classes for efficiency. tracemalloc handles millions of such objects. Creating and serialization millions of such small objects could take up to 30 seconds. I was not really convinced that a class is required to store only two fields, a tuple is just fine for a debug module. * (and many other minor changes) I optimized the implementation which is now faster and releases the memory earlier thanks to a new memory pool (only used by the module). PEP: 454 Title: Add a new tracemalloc module to trace Python memory allocations Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner 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 memory blocks allocated by Python. Rationale ========= Common debug tools tracing memory allocations read the C filename and line number. Using such tool to analyze Python memory allocations does not help because most memory block 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. The problem is 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 PEP 445, it becomes easy to setup an hook on Python memory allocators. A hook can inspect Python internals to retrieve the Python tracebacks. This PEP proposes to add a new ``tracemalloc`` module. It is a debug tool to trace memory blocks allocated by Python. The module provides the following information: * Compute the 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``), a ``-X`` command line option (``-X faulthandler`` and ``-X tracemalloc``). See the `documentation of the faulthandler module http://docs.python.org/dev/library/faulthandler.html`_. The tracemalloc module has been written for CPython. Other implementations of Python may not 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 also be called to start tracing Python memory allocations. By default, a trace of an allocated memory block only stores one frame. Use the ``set_traceback_limit()`` function to store more frames. Python memory blocks allocated in the ``tracemalloc`` module are also traced by default. Use ``add_exclude_filter(tracemalloc.__file__)`` to ignore these these memory allocations. At fork, the module is automatically disabled in the child process. Main Functions -------------- ``cancel_tasks()`` function: Cancel all scheduled tasks. See also the ``get_tasks()`` function. ``clear_traces()`` function: Clear all traces and statistics on Python memory allocations, and reset the ``get_arena_size()`` and ``get_traced_memory()`` counters. ``disable()`` function: Stop tracing Python memory allocations and cancel scheduled tasks. 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_tasks()`` function: Get the list of scheduled tasks, list of ``Task`` instances. ``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 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 all 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 filter 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. Metric Functions ---------------- The following functions can be used to add metrics to a snapshot, see the ``Snapshot.add_metric()`` method. ``get_allocated_blocks()`` function: Get the current number of allocated memory blocks. ``get_arena_size()`` function: Get the size in bytes of traced arenas. See also the ``get_pymalloc_stats()`` function. ``get_process_memory()`` function: Get the memory usage of the current process as a ``(rss: int, vms: int)`` tuple, *rss* is the "Resident Set Size" in bytes and *vms* is the size of the virtual memory in bytes Return ``None`` if the platform is not supported. ``get_pymalloc_stats()`` function: Get statistics on the ``pymalloc`` allocator as a dictionary. +---------------------+-------------------------------------------------------+ | Key | Description | +=====================+=======================================================+ | ``alignment`` | Alignment of addresses returned to the user. | +---------------------+-------------------------------------------------------+ | ``threshold`` | Small block threshold in bytes: pymalloc uses | | | PyMem_RawMalloc() for allocation greater than | | | threshold. | +---------------------+-------------------------------------------------------+ | ``nalloc`` | Number of times object malloc called | +---------------------+-------------------------------------------------------+ | ``arena_size`` | Arena size in bytes | +---------------------+-------------------------------------------------------+ | ``total_arenas`` | Number of calls to new_arena(): total number of | | | allocated arenas, including released arenas | +---------------------+-------------------------------------------------------+ | ``max_arenas`` | Maximum number of arenas | +---------------------+-------------------------------------------------------+ | ``arenas`` | Number of arenas currently allocated | +---------------------+-------------------------------------------------------+ | ``allocated_bytes`` | Number of bytes in allocated blocks | +---------------------+-------------------------------------------------------+ | ``available_bytes`` | Number of bytes in available blocks in used pools | +---------------------+-------------------------------------------------------+ | ``pool_size`` | Pool size in bytes | +---------------------+-------------------------------------------------------+ | ``free_pools`` | Number of unused pools | +---------------------+-------------------------------------------------------+ | ``pool_headers`` | Number of bytes wasted in pool headers | +---------------------+-------------------------------------------------------+ | ``quantization`` | Number of bytes in used and full pools wasted due to | | | quantization, i.e. the necessarily leftover space at | | | the ends of used and full pools. | +---------------------+-------------------------------------------------------+ | ``arena_alignment`` | Number of bytes for arena alignment padding | +---------------------+-------------------------------------------------------+ The function is not available if Python is compiled without ``pymalloc``. See also ``get_arena_size()`` and ``sys._debugmallocstats()`` 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 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 ``get_unicode_interned()`` function: Get the size in bytes and the length of the dictionary of Unicode interned strings as a ``(size: int, length: int)`` tuple. The size is the size of the dictionary, excluding the size of strings. DisplayTop ---------- ``DisplayTop()`` class: Display the top of allocated memory blocks. ``display(count=10, group_by="line", cumulative=False, file=None, callback=None)`` method: Take a snapshot and display the top *count* biggest allocated memory blocks grouped by *group_by*. *callback* is an optional callable object which can be used to add metrics to a snapshot. It is called with only one parameter: the newly created snapshot instance. Use the ``Snapshot.add_metric()`` method to add new metric. Return the snapshot, a ``Snapshot`` instance. ``display_snapshot(snapshot, count=10, group_by="line", cumulative=False, file=None)`` method: Display a snapshot of memory blocks allocated by Python, *snapshot* is a ``Snapshot`` instance. ``display_top_diff(top_diff, count=10, file=None)`` method: Display differences between two ``GroupedStats`` instances, *top_diff* is a ``StatsDiff`` instance. ``display_top_stats(top_stats, count=10, file=None)`` method: Display the top of allocated memory blocks grouped by the ``GroupedStats.group_by`` attribute of *top_stats*, *top_stats* is a ``GroupedStats`` instance. ``average`` attribute: If ``True`` (default value), display the average size of memory blocks. ``color`` attribute: If ``True``, always use colors. If ``False``, never use colors. The default value is ``None``: use colors if the *file* parameter is a TTY device. ``compare_to_previous`` attribute: If ``True`` (default value), compare to the previous snapshot. If ``False``, compare to the first snapshot. ``filename_parts`` attribute: Number of displayed filename parts (int, default: ``3``). Extra parts are replaced with ``'...'``. ``metrics`` attribute: If ``True`` (default value), display metrics: see ``Snapshot.metrics``. ``previous_top_stats`` attribute: Previous ``GroupedStats`` instance, or first ``GroupedStats`` instance if ``compare_to_previous`` is ``False``, used to display the differences between two snapshots. ``size`` attribute: If ``True`` (default value), display the size of memory blocks. DisplayTopTask -------------- ``DisplayTopTask(count=10, group_by="line", cumulative=False, file=sys.stdout, callback=None)`` class: Task taking temporary snapshots and displaying the top *count* memory allocations grouped by *group_by*. ``DisplayTopTask`` is based on the ``Task`` class and so inherit all attributes and methods, especially: * ``Task.cancel()`` * ``Task.schedule()`` * ``Task.set_delay()`` * ``Task.set_memory_threshold()`` Modify the ``display_top`` attribute to customize the display. ``display()`` method: Take a snapshot and display the top ``count`` biggest allocated memory blocks grouped by ``group_by`` using the ``display_top`` attribute. Return the snapshot, a ``Snapshot`` instance. ``callback`` attribute: *callback* is an optional callable object which can be used to add metrics to a snapshot. It is called with only one parameter: the newly created snapshot instance. Use the ``Snapshot.add_metric()`` method to add new metric. ``count`` attribute: Maximum number of displayed memory blocks. ``cumulative`` attribute: If ``True``, cumulate size and count of memory blocks of all frames of each trace, not only the most recent frame. The default value is ``False``. The option is ignored if the traceback limit is less than ``2``, see the ``get_traceback_limit()`` function. ``display_top`` attribute: Instance of ``DisplayTop``. ``file`` attribute: The top is written into *file*. ``group_by`` attribute: Determine how memory allocations are grouped: see ``Snapshot.top_by()`` for the available values. 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 :attr`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: * ``'int'``: a number * ``'percent'``: percentage, ``1.0`` means ``100%`` * ``'size'``: a size in bytes Snapshot -------- ``Snapshot(timestamp: datetime.datetime, pid: int, traces: dict=None, stats: dict=None, metrics: dict=None)`` class: Snapshot of traces and statistics on memory blocks allocated by Python. Use ``TakeSnapshotTask`` to take regulary snapshots. ``add_gc_metrics()`` method: Add a metric on garbage collector: * ``gc.objects``: total number of Python objects See the ``gc`` module. ``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``. ``add_process_memory_metrics()`` method: Add metrics on the process memory: * ``process_memory.rss``: Resident Set Size * ``process_memory.vms``: Virtual Memory Size These metrics are only available if the ``get_process_memory()`` function is available on the platform. ``add_pymalloc_metrics()`` method: Add metrics on the Python memory allocator (``pymalloc``): * ``pymalloc.blocks``: number of allocated memory blocks * ``pymalloc.size``: size of ``pymalloc`` arenas * ``pymalloc.max_size``: maximum size of ``pymalloc`` arenas * ``pymalloc.allocated``: number of allocated bytes * ``pymalloc.free``: number of free bytes * ``pymalloc.fragmentation``: fragmentation percentage of the arenas These metrics are only available if Python is compiled in debug mode, except ``pymalloc.blocks`` which is always available. ``add_tracemalloc_metrics()`` method: Add metrics on the ``tracemalloc`` module: * ``tracemalloc.traced.size``: size of memory blocks traced by the ``tracemalloc`` module * ``tracemalloc.traced.max_size``: maximum size of memory blocks traced by the ``tracemalloc`` module * ``tracemalloc.traces``: number of traces of Python memory blocks * ``tracemalloc.module.size``: total size of bytes allocated by the ``tracemalloc`` module, including free bytes * ``tracemalloc.module.free``: number of free bytes available for the ``tracemalloc`` module * ``tracemalloc.module.fragmentation``: percentage of fragmentation of the memory allocated by the ``tracemalloc`` module * ``tracemalloc.arena_size``: size of traced arenas ``tracemalloc.traces`` metric is only present if the snapshot was created with traces. ``add_unicode_metrics()`` method: Add metrics on the Unicode interned strings: * ``unicode_interned.size``: size of the dictionary, excluding size of strings * ``unicode_interned.len``: length of the dictionary ``apply_filters(filters)`` method: Apply filters on the ``traces`` and ``stats`` dictionaries, *filters* is a list of ``Filter`` instances. ``create(traces=False, metrics=True)`` 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``. If *metrics* is ``True``, fill ``Snapshot.metrics`` with metrics using the following methods: * ``add_gc_metrics`` * ``add_process_memory_metrics`` * ``add_pymalloc_metrics`` * ``add_tracemalloc_metrics`` * ``add_unicode_metrics`` If *metrics* is ``False``, ``Snapshot.metrics`` is set to an empty dictionary. 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 ``(str, int)`` ``'address'`` memory block address ``int`` ===================== ======================== ============== 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``. ``pid`` attribute: Identifier of the process which created the snapshot, result of ``os.getpid()``. ``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. Task ---- ``Task(func, *args, **kw)`` class: Task calling ``func(*args, **kw)``. When scheduled, the task is called when the traced memory is increased or decreased by more than *threshold* bytes, or after *delay* seconds. ``call()`` method: Call ``func(*args, **kw)`` and return the result. ``cancel()`` method: Cancel the task. Do nothing if the task is not scheduled. ``get_delay()`` method: Get the delay in seconds. If the delay is ``None``, the timer is disabled. ``get_memory_threshold()`` method: Get the threshold of the traced memory. When scheduled, the task is called when the traced memory is increased or decreased by more than *threshold* bytes. The memory threshold is disabled if *threshold* is ``None``. See also the ``set_memory_threshold()`` method and the ``get_traced_memory()`` function. ``schedule(repeat: int=None)`` method: Schedule the task *repeat* times. If *repeat* is ``None``, the task is rescheduled after each call until it is cancelled. If the method is called twice, the task is rescheduled with the new *repeat* parameter. The task must have a memory threshold or a delay: see ``set_delay()`` and ``set_memory_threshold()`` methods. The ``tracemalloc`` must be enabled to schedule a task: see the ``enable`` function. The task is cancelled if the ``call()`` method raises an exception. The task can be cancelled using the ``cancel()`` method or the ``cancel_tasks()`` function. ``set_delay(seconds: int)`` method: Set the delay in seconds before the task will be called. Set the delay to ``None`` to disable the timer. The timer is based on the Python memory allocator, it is not real time. The task is called after at least *delay* seconds, it is not called exactly after *delay* seconds if no Python memory allocation occurred. The timer has a resolution of 1 second. The task is rescheduled if it was scheduled. ``set_memory_threshold(size: int)`` method: Set the threshold of the traced memory. When scheduled, the task is called when the traced memory is increased or decreased by more than *threshold* bytes. Set the threshold to ``None`` to disable it. The task is rescheduled if it was scheduled. See also the ``get_memory_threshold()`` method and the ``get_traced_memory()`` function. ``func`` attribute: Function, callable object. ``func_args`` attribute: Function arguments, ``tuple``. ``func_kwargs`` attribute: Function keyword arguments, ``dict``. It can be ``None``. TakeSnapshotTask ---------------- ``TakeSnapshotTask(filename_template: str="tracemalloc-$counter.pickle", traces: bool=False, metrics: bool=True, callback: callable=None)`` class: Task taking snapshots of Python memory allocations and writing them into files. ``TakeSnapshotTask`` is based on the ``Task`` class and so inherit all attributes and methods, especially: * ``Task.cancel()`` * ``Task.schedule()`` * ``Task.set_delay()`` * ``Task.set_memory_threshold()`` ``take_snapshot()`` method: Take a snapshot and write it into a file. Return ``(snapshot, filename)`` where *snapshot* is a ``Snapshot`` instance and filename type is ``str``. ``callback`` attribute: *callback* is an optional callable object which can be used to add metrics to a snapshot. It is called with only one parameter: the newly created snapshot instance. Use the ``Snapshot.add_metric()`` method to add new metric. ``filename_template`` attribute: Template to create a filename. The template supports the following variables: * ``$pid``: identifier of the current process * ``$timestamp``: current date and time * ``$counter``: counter starting at 1 and incremented at each snapshot, formatted as 4 decimal digits The default template is ``'tracemalloc-$counter.pickle'``. ``metrics`` attribute: Parameter passed to the ``Snapshot.create()`` function. ``traces`` attribute: Parameter passed to the ``Snapshot.create()`` function. 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 into the public domain.