blocks|key|1100177|text|只要在google上输入"django-profiling“，你就会得到这些链接(还有更多)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1100178|http://code.djangoproject.com/wiki/ProfilingDjango|offset|length|1100179|http://code.google.com/p/django-profiling/|1100180|http://www.rkblog.rk.edu.pl/w/p/django-profiling-hotshot-and-kcachegrind/|1100181|就我个人而言，我使用的是中间件方法--即每个用户都可以切换存储在会话中的“分析”标志，如果我的分析中间件注意到设置了一个标志，它将使用Python的hotshot模块，如下所示：|1100182|def+process_view(self,+request,+view_func,+view_args,+view_kwargs):

+++++#+setup+things+here,+along+with:+settings.DEBUG=True+
+++++#+to+get+a+SQL+dump+in+connection.queries

+++++profiler+=+hotshot.Profile(fname)
+++++response+=+profiler.runcall(view_func,+request,+*view_args,+**view_kwargs)
+++++profiler.close()

+++++#+process+results

+++++return+response|code-block|syntax|javascript|1100183|编辑:+Konstantin提到的http://github.com/robhudson/django-debug-toolbar对于分析SQL查询来说是一件很好的事情--但是如果你的查询真的很慢(可能是因为有成百上千的查询)，那么你将疯狂地等待大量的时间，直到它被加载到浏览器中-然后由于缓慢而很难浏览。此外，django-debug-toolbar在设计上无法提供对AJAX请求内部结构的有用洞察。|1100184|EDIT2：django-extensions内置了一个很棒的分析命令：|1100185|https://github.com/django-extensions/django-extensions/blob/master/docs/runprofileserver.rst|1100186|只需这样做，就可以了：|1100187|$+mkdir+/tmp/my-profile-data
$+./manage.py+runprofileserver+--kcachegrind+--prof-path=/tmp/my-profile-data|1100188|entityMap|0|LINK|mutability|MUTABLE|url|1|2|3|http://docs.python.org/library/hotshot.html|4|http://github.com/robhudson/django-debug-toolbar|5|https://github.com/django-extensions/django-extensions|6^0|0|0|1E|0|0|0|16|1|0|0|21|2|0|22|7|3|0|0|H|1C|4|0|6|H|5|0|0|2K|6|0|0|0^^$0|@$1|2|3|4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1H|8|@]|9|@$D|1I|E|1J|1|1K]]|A|$]]|$1|F|3|G|5|6|7|1L|8|@]|9|@$D|1M|E|1N|1|1O]]|A|$]]|$1|H|3|I|5|6|7|1P|8|@]|9|@$D|1Q|E|1R|1|1S]]|A|$]]|$1|J|3|K|5|6|7|1T|8|@]|9|@$D|1U|E|1V|1|1W]]|A|$]]|$1|L|3|M|5|N|7|1X|8|@]|9|@]|A|$O|P]]|$1|Q|3|R|5|6|7|1Y|8|@]|9|@$D|1Z|E|20|1|21]]|A|$]]|$1|S|3|T|5|6|7|22|8|@]|9|@$D|23|E|24|1|25]]|A|$]]|$1|U|3|V|5|6|7|26|8|@]|9|@$D|27|E|28|1|29]]|A|$]]|$1|W|3|X|5|6|7|2A|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|N|7|2B|8|@]|9|@]|A|$O|P]]|$1|10|3|-4|5|6|7|2C|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|C]]|17|$5|13|14|15|A|$16|G]]|18|$5|13|14|15|A|$16|I]]|19|$5|13|14|15|A|$16|1A]]|1B|$5|13|14|15|A|$16|1C]]|1D|$5|13|14|15|A|$16|1E]]|1F|$5|13|14|15|A|$16|V]]]]

Just type "django-profiling" on google, you'll get these links (and more):

<a href="http://code.djangoproject.com/wiki/ProfilingDjango" rel="nofollow noreferrer">http://code.djangoproject.com/wiki/ProfilingDjango</a>

<a href="http://code.google.com/p/django-profiling/" rel="nofollow noreferrer">http://code.google.com/p/django-profiling/</a>

<a href="http://www.rkblog.rk.edu.pl/w/p/django-profiling-hotshot-and-kcachegrind/" rel="nofollow noreferrer">http://www.rkblog.rk.edu.pl/w/p/django-profiling-hotshot-and-kcachegrind/</a>

Personally I'm using the middleware approach - i.e. each user can toggle a "profiling" flag stored in a session, and if my profiling middleware notices that a flag has been set, it uses Python's <a href="http://docs.python.org/library/hotshot.html" rel="nofollow noreferrer">hotshot</a> module like this:

<pre><code>def process_view(self, request, view_func, view_args, view_kwargs):

 # setup things here, along with: settings.DEBUG=True 
 # to get a SQL dump in connection.queries

 profiler = hotshot.Profile(fname)
 response = profiler.runcall(view_func, request, *view_args, **view_kwargs)
 profiler.close()

 # process results

 return response
</code></pre>

EDIT: For profiling SQL queries <a href="http://github.com/robhudson/django-debug-toolbar" rel="nofollow noreferrer">http://github.com/robhudson/django-debug-toolbar</a> mentioned by Konstantin is a nice thing - but if your queries are really slow (probably because there are hundreds or thousands of them), then you'll be waiting insane amount of time until it gets loaded into a browser - and then it'll be hard to browse due to slowness. Also, django-debug-toolbar is by design unable to give useful insight into the internals of AJAX requests.

EDIT2: <a href="https://github.com/django-extensions/django-extensions" rel="nofollow noreferrer">django-extensions</a> has a great profiling command built in:

<a href="https://github.com/django-extensions/django-extensions/blob/master/docs/runprofileserver.rst" rel="nofollow noreferrer">https://github.com/django-extensions/django-extensions/blob/master/docs/runprofileserver.rst</a> 

Just do this and voila:

<pre><code>$ mkdir /tmp/my-profile-data
$ ./manage.py runprofileserver --kcachegrind --prof-path=/tmp/my-profile-data
</code></pre>

blocks|key|1100226|text|当视图不是HTML时，例如JSON，可以使用简单的中间件方法进行分析。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1100227|这里有几个例子：|1100228|https://gist.github.com/1229685+-捕获视图中的所有sql调用|offset|length|1100229|https://gist.github.com/1229681+-分析用于创建视图的所有方法调用|1100230|entityMap|0|LINK|mutability|MUTABLE|url|https://gist.github.com/1229685|1|https://gist.github.com/1229681^0|0|0|0|V|0|0|0|V|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|V|8|@]|9|@$F|W|G|X|1|Y]]|A|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$F|10|G|11|1|12]]|A|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

When the views are not HTML, for example JSON, use simple middleware methods for profiling.

Here are a couple examples:

<a href="https://gist.github.com/1229685" rel="nofollow">https://gist.github.com/1229685</a> - capture all sql calls went into the view

<a href="https://gist.github.com/1229681" rel="nofollow">https://gist.github.com/1229681</a> - profile all method calls used to create the view

blocks|key|1100423|text|要分析数据访问(这通常是瓶颈所在)，请查看django-live-profiler。与Django+Debug+Toolbar不同，它同时收集所有请求的数据，您可以在生产中运行它，而不会产生太多的性能开销，也不会暴露您的应用程序内部。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1100424|​|1100425|📷|atomic|1100426|1100427|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/InviteBox/django-live-profiler|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/70b9f818007b8841584e91c11db06a12.png|imageAlt^0|L|K|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|I|3|E|5|6|7|16|8|@]|9|@]|C|$]]|$1|J|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|S|N|T|C|$U|V|W|-4]]]]

For profiling data access (which is where the bottleneck is most of the time) check out <a href="https://github.com/InviteBox/django-live-profiler" rel="noreferrer" title="django-live-profiler">django-live-profiler</a>. Unlike Django Debug Toolbar it collects data across all requests simultaneously and you can run it in production without too much performance overhead or exposing your app internals.

<img src="https://i.stack.imgur.com/L98eb.png" alt="Check out this screenshot">

blocks|key|1100468|text|无耻的插头在这里，但我最近为此目的制作了https://github.com/django-silk/silk。它有点类似于django工具栏，但具有历史记录、代码分析和对一切的更细粒度控制。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1100469|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/django-silk/silk^0|K|Z|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Shameless plug here, but I recently made <a href="https://github.com/django-silk/silk" rel="noreferrer">https://github.com/django-silk/silk</a> for this purpose. It's somewhat similar to django toolbar but with history, code profiling and more fine grained control over everything.

blocks|key|1315488|text|您可以使用line_profiler。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1315489|它允许显示代码的逐行分析，并在每行旁边显示时间(当一行被多次点击时，时间也会求和)。|1315490|​|1315491|📷|atomic|1315492|1315493|它与非Django+python代码一起使用，但实际上在Django上使用它有一个小技巧：https://stackoverflow.com/a/68163807/1937033|1315494|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pyutils/line_profiler|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/11ef19e3b1890175880dac28539f1404.png|imageAlt|2|https://stackoverflow.com/a/68163807/1937033^0|5|D|0|0|0|0|0|1|1|0|0|19|18|2|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|6|7|17|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|18|8|@]|9|@]|C|$]]|$1|H|3|I|5|J|7|19|8|@]|9|@$A|1A|B|1B|1|1C]]|C|$]]|$1|K|3|G|5|6|7|1D|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|1E|8|@]|9|@$A|1F|B|1G|1|1H]]|C|$]]|$1|N|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|W|R|X|C|$Y|Z|10|-4]]|11|$5|Q|R|S|C|$T|12]]]]

You can use <a href="https://github.com/pyutils/line_profiler" rel="nofollow noreferrer">line_profiler</a>.
It allows to display a line-by-line analysis of your code with the time alongside of each line (When a line is hit several times, the time is summed up also).
<a href="https://i.stack.imgur.com/Qtj0Q.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Qtj0Q.png" alt="enter image description here" /></a>
It's used with not-Django python code but there's a little trick to use it on Django in fact: <a href="https://stackoverflow.com/a/68163807/1937033">https://stackoverflow.com/a/68163807/1937033</a>

My django application has become painfully slow on the production. Probably it is due to some complex or unindexed queries. 

Is there any django-ish way to profile my application?

Profiling Django

中间件

Django

Python

JSON

我的django应用程序在生产环境中变得非常慢。这可能是由于一些复杂或未索引的查询造成的。有没有什么django式的方法来分析我的应用程序？

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