8.8 KiB
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`.
System Message: ERROR/3 (<stdin>, line 10); backlink
Unknown interpreted text role "ref".As these Scrapy objects have a (rather long) lifetime there is always the risk accumulated 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:`Guppy <topics-leaks-guppy>` for more advanced memory debugging (see below for more info). Both mechanisms must be used from the :ref:`Telnet Console <topics-telnetconsole>`.
System Message: ERROR/3 (<stdin>, line 18); backlink
Unknown interpreted text role "ref".System Message: ERROR/3 (<stdin>, line 18); backlink
Unknown interpreted text role "ref".System Message: ERROR/3 (<stdin>, line 18); backlink
Unknown interpreted text role "ref".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.http.Request.meta` attribute 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.
System Message: ERROR/3 (<stdin>, line 28); backlink
Unknown interpreted text role "attr".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.
At the same time, it's hard to avoid the reasons that causes these leaks without restricting the power of the framework, so we have decided not to restrict the functionally but provide useful tools for debugging these leaks, which quite often consists in answer the question: which spider is leaking?.
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, if you're allocating resources on :signal:`domain_opened` but not releasing them on :signal:`domain_closed`.
System Message: ERROR/3 (<stdin>, line 45); backlink
Unknown interpreted text role "signal".System Message: ERROR/3 (<stdin>, line 45); backlink
Unknown interpreted text role "signal".Debugging memory leaks with trackref
trackref is a module provided by Scrapy to debug the most common cases of memory leaks. It basically tracks the references to all live Requests, Responses, Item and Selector objects.
To active the trackref module, enable the :setting:`TRACK_REFS` setting. It only imposes a minor performance impact so it should be OK for use it in production environments.
System Message: ERROR/3 (<stdin>, line 59); backlink
Unknown interpreted text role "setting".Once you have trackref enabled 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:
System Message: ERROR/3 (<stdin>, line 63); backlink
Unknown interpreted text role "func".telnet localhost 6023 >>> prefs() Live References HtmlResponse 10 oldest: 1s ago XPathSelector 2 oldest: 0s ago FormRequest 878 oldest: 7s ago
As you can see, that report also shows the "age" of the oldest object in each class.
If you do have leaks, 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 like this (from the telnet console).
System Message: ERROR/3 (<stdin>, line 80); backlink
Unknown interpreted text role "func".A real example
Let's see a concrete example of an hypothetical case of memory leaks.
Suppose we have some spider with a line similar to this one:
return Request("http://www.somenastyspider.com/product.php?pid=%d" % product_id,
callback=self.parse, meta={referer: response}")
That line is passing a response reference inside a request which effectively ties the response lifetime to the requests one, and that's would definitely cause memory leaks.
Let's see how we can discover which one is the nasty spider (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:
>>> prefs() Live References HtmlResponse 3890 oldest: 265s ago XPathSelector 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. So let's check the oldest response:
>>> from scrapy.utils.trackref import get_oldest
>>> r = get_oldest('HtmlResponse')
>>> r.url
'http://www.somenastyspider.com/product.php?pid=123'
There it is. By looking at the URL of the oldest response we can see it belongs to the somenastyspider.com spider. We can now go and check the code of that spider to discover the nasty line that is generating the leaks (passing response references inside requests).
System Message: ERROR/3 (<stdin>, line 127)
Unknown directive type "module".
.. 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.
System Message: ERROR/3 (<stdin>, line 133); backlink
Unknown interpreted text role "mod".Inherit from this class (instead of object) if you want to track live instances with the trackref module.
System Message: ERROR/3 (<stdin>, line 140)
Unknown directive type "function".
.. function:: print_live_refs(class_name)
Print a report of live references, grouped by class name.
System Message: ERROR/3 (<stdin>, line 144)
Unknown directive type "function".
.. function:: get_oldest(class_name)
Return the oldest object alive with the given class name, or ``None`` if
none is found. Use :func:`print_live_refs` first to get a list of all
tracked live objects.
Debugging memory leaks with Guppy
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 (Requests, Responses, Items, and Selectors). 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 Guppy library.
If you use setuptools, you can install Guppy with the following command:
easy_install guppy
The telnet console also comes with a built-in shortcut (hpy) for accessing Guppy heap objects. Here's an example to view all Python objects available in the heap using Guppy:
>>> x = hpy.heap()
>>> x.bytype
Partition of a set of 297033 objects. Total size = 52587824 bytes.
Index Count % Size % Cumulative % Type
0 22307 8 16423880 31 16423880 31 dict
1 122285 41 12441544 24 28865424 55 str
2 68346 23 5966696 11 34832120 66 tuple
3 227 0 5836528 11 40668648 77 unicode
4 2461 1 2222272 4 42890920 82 type
5 16870 6 2024400 4 44915320 85 function
6 13949 5 1673880 3 46589200 89 types.CodeType
7 13422 5 1653104 3 48242304 92 list
8 3735 1 1173680 2 49415984 94 _sre.SRE_Pattern
9 1209 0 456936 1 49872920 95 scrapy.http.headers.Headers
<1676 more rows. Type e.g. '_.more' to view.>
You can see that most space is used by dicts. Then, if you want to see from which attribute those dicts are referenced you could do:
>>> x.bytype[0].byvia
Partition of a set of 22307 objects. Total size = 16423880 bytes.
Index Count % Size % Cumulative % Referred Via:
0 10982 49 9416336 57 9416336 57 '.__dict__'
1 1820 8 2681504 16 12097840 74 '.__dict__', '.func_globals'
2 3097 14 1122904 7 13220744 80
3 990 4 277200 2 13497944 82 "['cookies']"
4 987 4 276360 2 13774304 84 "['cache']"
5 985 4 275800 2 14050104 86 "['meta']"
6 897 4 251160 2 14301264 87 '[2]'
7 1 0 196888 1 14498152 88 "['moduleDict']", "['modules']"
8 672 3 188160 1 14686312 89 "['cb_kwargs']"
9 27 0 155016 1 14841328 90 '[1]'
<333 more rows. Type e.g. '_.more' to view.>
As you can see, the Guppy module is very powerful, but also requires some deep knowledge about Python internals. For more info about Guppy, refer to the Guppy documentation.