.. _topics-leaks:

======================
Debugging memory leaks
======================

In Scrapy, objects such as requests, responses and items have a finite
lifetime: they are created, used for a while, and finally destroyed.

From all those objects, the Request is probably the one with the longest
lifetime, as it stays waiting in the Scheduler queue until it's time to process
it. For more info see :ref:`topics-architecture`.

As these Scrapy objects have a (rather long) lifetime, there is always the risk
of accumulating them in memory without releasing them properly and thus causing
what is known as a "memory leak".

To help debugging memory leaks, Scrapy provides a built-in mechanism for
tracking objects references called :ref:`trackref <topics-leaks-trackrefs>`,
and you can also use a third-party library called :ref:`muppy
<topics-leaks-muppy>` for more advanced memory debugging (see below for more
info). Both mechanisms must be used from the :ref:`Telnet Console
<topics-telnetconsole>`.

Common causes of memory leaks
=============================

It happens quite often (sometimes by accident, sometimes on purpose) that the
Scrapy developer passes objects referenced in Requests (for example, using the
:attr:`~scrapy.Request.cb_kwargs` or :attr:`~scrapy.Request.meta`
attributes or the request callback function) and that effectively bounds the
lifetime of those referenced objects to the lifetime of the Request. This is,
by far, the most common cause of memory leaks in Scrapy projects, and a quite
difficult one to debug for newcomers.

In big projects, the spiders are typically written by different people and some
of those spiders could be "leaking" and thus affecting the rest of the other
(well-written) spiders when they get to run concurrently, which, in turn,
affects the whole crawling process.

The leak could also come from a custom middleware, pipeline or extension that
you have written, if you are not releasing the (previously allocated) resources
properly. For example, allocating resources on :signal:`spider_opened`
but not releasing them on :signal:`spider_closed` may cause problems if
you're running :ref:`multiple spiders per process <run-multiple-spiders>`.

Too Many Requests?
------------------

By default Scrapy keeps the request queue in memory; it includes
:class:`~scrapy.Request` objects and all objects
referenced in Request attributes (e.g. in :attr:`~scrapy.Request.cb_kwargs`
and :attr:`~scrapy.Request.meta`).
While not necessarily a leak, this can take a lot of memory. Enabling
:ref:`persistent job queue <topics-jobs>` could help keeping memory usage
in control.

.. _topics-leaks-trackrefs:

Debugging memory leaks with ``trackref``
========================================

.. skip: start

:mod:`scrapy.utils.trackref` is a module provided by Scrapy to debug the most
common cases of memory leaks. It basically tracks the references to all live
Request, Response, Item, Spider and Selector objects.

You can enter the telnet console and inspect how many objects (of the classes
mentioned above) are currently alive using the ``prefs()`` function which is an
alias to the :func:`~scrapy.utils.trackref.print_live_refs` function:

.. code-block:: bash

    telnet localhost 6023

.. code-block:: pycon

    >>> prefs()
    Live References

    ExampleSpider                       1   oldest: 15s ago
    HtmlResponse                       10   oldest: 1s ago
    Selector                            2   oldest: 0s ago
    Request                           878   oldest: 7s ago

As you can see, that report also shows the "age" of the oldest object in each
class. If you're running multiple spiders per process chances are you can
figure out which spider is leaking by looking at the oldest request or response.
You can get the oldest object of each class using the
:func:`~scrapy.utils.trackref.get_oldest` function (from the telnet console).

Which objects are tracked?
--------------------------

The objects tracked by ``trackref`` are all from these classes (and all its
subclasses):

* :class:`scrapy.Request`
* :class:`scrapy.http.Response`
* :class:`scrapy.Item`
* :class:`scrapy.Selector`
* :class:`scrapy.Spider`

A real example
--------------

Let's see a concrete example of a hypothetical case of memory leaks.
Suppose we have some spider with a line similar to this one:

.. code-block:: python

    return Request(
        f"http://www.somenastyspider.com/product.php?pid={product_id}",
        callback=self.parse,
        cb_kwargs={"referer": response},
    )

That line is passing a response reference inside a request which effectively
ties the response lifetime to the requests' one, and that would definitely
cause memory leaks.

Let's see how we can discover the cause (without knowing it
a priori, of course) by using the ``trackref`` tool.

After the crawler is running for a few minutes and we notice its memory usage
has grown a lot, we can enter its telnet console and check the live
references:

.. code-block:: pycon

    >>> prefs()
    Live References

    SomenastySpider                     1   oldest: 15s ago
    HtmlResponse                     3890   oldest: 265s ago
    Selector                            2   oldest: 0s ago
    Request                          3878   oldest: 250s ago

The fact that there are so many live responses (and that they're so old) is
definitely suspicious, as responses should have a relatively short lifetime
compared to Requests. The number of responses is similar to the number
of requests, so it looks like they are tied in a some way. We can now go
and check the code of the spider to discover the nasty line that is
generating the leaks (passing response references inside requests).

Sometimes extra information about live objects can be helpful.
Let's check the oldest response:

.. code-block:: pycon

    >>> from scrapy.utils.trackref import get_oldest
    >>> r = get_oldest("HtmlResponse")
    >>> r.url
    'http://www.somenastyspider.com/product.php?pid=123'

If you want to iterate over all objects, instead of getting the oldest one, you
can use the :func:`scrapy.utils.trackref.iter_all` function:

.. code-block:: pycon

    >>> from scrapy.utils.trackref import iter_all
    >>> [r.url for r in iter_all("HtmlResponse")]
    ['http://www.somenastyspider.com/product.php?pid=123',
    'http://www.somenastyspider.com/product.php?pid=584',
    ...]

Too many spiders?
-----------------

If your project has too many spiders executed in parallel,
the output of ``prefs()`` can be difficult to read.
For this reason, that function has a ``ignore`` argument which can be used to
ignore a particular class (and all its subclasses). For
example, this won't show any live references to spiders:

.. code-block:: pycon

    >>> from scrapy.spiders import Spider
    >>> prefs(ignore=Spider)

.. module:: scrapy.utils.trackref
   :synopsis: Track references of live objects

scrapy.utils.trackref module
----------------------------

Here are the functions available in the :mod:`~scrapy.utils.trackref` module.

.. autoclass:: object_ref

.. autofunction:: print_live_refs(ignore=NoneType)

.. autofunction:: get_oldest

.. autofunction:: iter_all

.. skip: end

.. _topics-leaks-muppy:

Debugging memory leaks with muppy
=================================

``trackref`` provides a very convenient mechanism for tracking down memory
leaks, but it only keeps track of the objects that are more likely to cause
memory leaks. However, there are other cases where the memory leaks could come
from other (more or less obscure) objects. If this is your case, and you can't
find your leaks using ``trackref``, you still have another resource: the muppy
library.

You can use muppy from `Pympler`_.

.. _Pympler: https://pypi.org/project/Pympler/

If you use ``pip``, you can install muppy with the following command::

    pip install Pympler

Here's an example to view all Python objects available in
the heap using muppy:

.. skip: start
.. code-block:: pycon

    >>> from pympler import muppy
    >>> all_objects = muppy.get_objects()
    >>> len(all_objects)
    28667
    >>> from pympler import summary
    >>> suml = summary.summarize(all_objects)
    >>> summary.print_(suml)
                                   types |   # objects |   total size
    ==================================== | =========== | ============
                             <class 'str |        9822 |      1.10 MB
                            <class 'dict |        1658 |    856.62 KB
                            <class 'type |         436 |    443.60 KB
                            <class 'code |        2974 |    419.56 KB
              <class '_io.BufferedWriter |           2 |    256.34 KB
                             <class 'set |         420 |    159.88 KB
              <class '_io.BufferedReader |           1 |    128.17 KB
              <class 'wrapper_descriptor |        1130 |     88.28 KB
                           <class 'tuple |        1304 |     86.57 KB
                         <class 'weakref |        1013 |     79.14 KB
      <class 'builtin_function_or_method |         958 |     67.36 KB
               <class 'method_descriptor |         865 |     60.82 KB
                     <class 'abc.ABCMeta |          62 |     59.96 KB
                            <class 'list |         446 |     58.52 KB
                             <class 'int |        1425 |     43.20 KB

.. skip: end

For more info about muppy, refer to the `muppy documentation`_.

.. _muppy documentation: https://pythonhosted.org/Pympler/muppy.html

.. _topics-leaks-without-leaks:

Leaks without leaks
===================

Sometimes, you may notice that the memory usage of your Scrapy process will
only increase, but never decrease. Unfortunately, this could happen even
though neither Scrapy nor your project are leaking memory. This is due to a
(not so well) known problem of Python, which may not return released memory to
the operating system in some cases. For more information on this issue see:

* `Python Memory Management <https://www.evanjones.ca/python-memory.html>`_
* `Python Memory Management Part 2 <https://www.evanjones.ca/python-memory-part2.html>`_
* `Python Memory Management Part 3 <https://www.evanjones.ca/python-memory-part3.html>`_

The improvements proposed by Evan Jones, which are detailed in `this paper`_,
got merged in Python 2.5, but this only reduces the problem, it doesn't fix it
completely. To quote the paper:

    *Unfortunately, this patch can only free an arena if there are no more
    objects allocated in it anymore. This means that fragmentation is a large
    issue. An application could have many megabytes of free memory, scattered
    throughout all the arenas, but it will be unable to free any of it. This is
    a problem experienced by all memory allocators. The only way to solve it is
    to move to a compacting garbage collector, which is able to move objects in
    memory. This would require significant changes to the Python interpreter.*

.. _this paper: https://www.evanjones.ca/memoryallocator/

To keep memory consumption reasonable you can split the job into several
smaller jobs or enable :ref:`persistent job queue <topics-jobs>`
and stop/start spider from time to time.
