Django Con High Performance Django

David Cramer http://www.davidcramer.net/ http://www.ibegin.com/ High Performance Django

Curse
Peak daily traffic of approx. 15m pages, 150m hits.
Average monthly traffic 120m pages, 6m uniques.
Python, MySQL, Squid, memcached, mod_python, lighty.
Most developers came strictly from PHP (myself included).
12 web servers, 4 database servers, 2 squid caches.

iBegin
Massive amounts of data, 100m+ rows.
Python, PHP, MySQL, mod_wsgi.
Small team of developers.
Complex database partitioning/synchronization tasks.
Attempting to not branch off of Django. 

Areas of Concern
Database (ORM)
Webserver (Resources, Handling Millions of Reqs)
Caching (Invalidation, Cache Dump)
Template Rendering (Logic Separation)
Profiling

Tools of the Trade
Webserver (Apache, Nginx, Lighttpd)
Object Cache (memcached)
Database (MySQL, PostgreSQL, …)
Page Cache (Squid, Nginx, Varnish)
Load Balancing (Nginx, Perlbal)

How We Did It
“ Primary” web servers serving Django using mod_python.
Media servers using Django on lighttpd.
Static served using additional instances of lighttpd.
Load balancers passing requests to multiple Squids.
Squids passing requests to multiple web servers.

Lessons Learned
Don’t be afraid to experiment. You’re not limited to a one.
mod_wsgi is a huge step forward from mod_python.
Serving static files using different software can help.
Send proper HTTP headers where they are needed.
Use services like S3, Akamai, Limelight, etc..

Webserver Software
Python Scripts
Apache (wsgi, mod_py, fastcgi)
Lighttpd (fastcgi)
Nginx (fastcgi)
Reverse Proxies
Nginx
Squid
Varnish
Static Content
Apache
Lighttpd
Tinyhttpd
Nginx
Software Load Balancers
Nginx
Perlbal

Database (ORM)
Won’t make your queries efficient. Make your own indexes.
select_related() can be good, as well as bad.
Inherited ordering (Meta: ordering) will get you.
Hundreds of queries on a page is never a good thing.
Know when to not use the ORM.

Handling JOINs
class Category(models.Model):
name = models.CharField()
created_by = models.ForeignKey(User)
class Poll(models.Model):
category = models.ForeignKey(Category)
created_by = models.ForeignKey(User)
# We need to output a page listing all Poll's with
# their name and category's name.
def a_bad_example(request):
# We have just caused Poll to JOIN with User and Category,
# which will also JOIN with User a second time.
my_polls = Poll.objects.all().select_related()
return render_to_response('polls.html', locals(), request)
def a_good_example(request):
# Use select_related explicitly in each case.
poll = Poll.objects.all().select_related('category')
return render_to_response('polls.html', locals(), request)

Template Rendering
Sandboxed engines are typically slower by nature.
Keep logic in views and template tags.
Be aware of performance in loops, and groupby (regroup).
Loaded templates can be cached to avoid disk reads.
Switching template engines is easy, but may not give you any worthwhile performance gain.

Template Engines

Using Django with Jinja
from jinja.contrib.djangosupport import render_to_response
from models import MyModel
def myview(request):
my_object_list = MyModel.objects.all()
# Both the context, and request parameters are optional.
# If you pass request it will execute your context processors.
return render_to_response(‘template/name.html’, locals(), request)
from jinja.contrib.djangosupport import register, convert_django_filter
def truncatechars(length=30):
def wrapped(env, context, value):
if len(value) > length:
value = value[0:length-3] + '...'
return value
return wrapped
register.filter(truncatechars)
from django.contrib.humanize.templatetags.humanize import intcomma
register.filter(convert_django_filter(intcomma), 'intcomma')

Caching
Two flavors of caching: object cache and browser cache.
Django provides built-in support for both.
Invalidation is a headache without a well thought out plan.
Caching isn’t a solution for slow loading pages or improper indexes.
Use a reverse proxy in between the browser and your web servers: Squid, Varnish, Nginx, etc..

Cache With a Plan
Build your pages to use proper cache headers.
Create a plan for object cache expiration, and invalidation.
For typical web apps you can serve the same cached page for both anonymous and authenticated users.
Contain commonly used querysets in managers for transparent caching and invalidation.

Cache Commonly Used Items
def my_context_processor(request):
# We access object_list every time we use our context processors so
# it makes sense to cache this, no?
cache_key = ‘mymodel:all’
object_list = cache.get(cache_key)
if object_list is None:
object_list = MyModel.objects.all()
cache.set(cache_key, object_list)
return {‘object_list’: object_list}
# Now that we are caching the object list we are going to want to invalidate it
class MyModel(models.Model):
def save(self, *args, **kwargs):
super(MyModel, self).save(*args, **kwargs)
# save it before you update the cache
cache.set(‘mymodel:all’, MyModel.objects.all())

Profiling Code
Finding the bottleneck can be time consuming.
Tools exist to help identify common problematic areas.
cProfile/Profile Python modules.
PDB (Python Debugger)

Profiling Code With cProfile
import sys
try: import cProfile as profile
except ImportError: import profile
try: from cStringIO import StringIO
except ImportError: import StringIO
from django.conf import settings
class ProfilerMiddleware(object):
def can(self, request):
return settings.DEBUG and 'prof' in request.GET and (not settings.INTERNAL_IPS or request.META['REMOTE_ADDR'] in settings.INTERNAL_IPS)
def process_view(self, request, callback, callback_args, callback_kwargs):
if self.can(request):
self.profiler = profile.Profile()
args = (request,) + callback_args
return self.profiler.runcall(callback, *args, **callback_kwargs)
def process_response(self, request, response):
self.profiler.create_stats()
out = StringIO()
old_stdout, sys.stdout = sys.stdout, out
self.profiler.print_stats(1)
sys.stdout = old_stdout
response.content = '<pre>%s</pre>' % out.getvalue()
return response

http://localhost:8000/?prof

Profiling Database Queries
from django.db import connection
class DatabaseProfilerMiddleware(object):
def can(self, request):
return settings.DEBUG and 'dbprof' in request.GET
and (not settings.INTERNAL_IPS or
request.META['REMOTE_ADDR'] in settings.INTERNAL_IPS)
def process_response(self, request, response):
out = StringIO()
out.write('time sql ')
total_time = 0
for query in reversed(sorted(connection.queries, key=lambda x: x['time'])):
total_time += float(query['time'])*1000
out.write('%s %s ' % (query['time'], query['sql']))
response.content = '<pre style="white-space:pre-wrap">%d queries executed in %.3f seconds %s</pre>' % (len(connection.queries), total_time/1000, out.getvalue())
return response

http://localhost:8000/?dbprof

Summary
Database efficiency is the typical problem in web apps.
Develop and deploy a caching plan early on.
Use profiling tools to find your problematic areas. Don’t pre-optimize unless there is good reason.
Find someone who knows more than me to configure your server software. 

Slides and code available online at: http://www.davidcramer.net/djangocon Thanks!

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Django Con High Performance Django

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