This document discusses techniques for improving the performance of Django projects handling high traffic volumes. It identifies common areas of concern like database performance, web server configuration, caching, and template rendering. It provides examples of optimizing ORM queries, implementing object caching with invalidation plans, profiling code to identify bottlenecks, and leveraging tools like Varnish, memcached, and database query profiling. The key lessons are to develop a caching strategy, use profiling to optimize problem areas, and consider alternative web server software or configurations to improve performance.

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

2. 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.

3. 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. 

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

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

6. 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.

7. 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..

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

9. 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.

10. Handling JOINs
class Category(models.Model):
name = models.CharField()
created_by = models.ForeignKey(User)
class Poll(models.Model):
name = models.CharField()
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)

11. 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.

12. Template Engines

13. 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..

14. 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.

15. 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):
name = models.CharField()
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())

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

17. 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):
if self.can(request):
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

18. http://localhost:8000/?prof

19. 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):
if self.can(request):
out = StringIO()
out.write('timetsqln')
total_time = 0
for query in reversed(sorted(connection.queries, key=lambda x: x['time'])):
total_time += float(query['time'])*1000
out.write('%st%sn' % (query['time'], query['sql']))
response.content = '<pre style=quot;white-space:pre-wrapquot;>%d queries executed in %.3f secondsnn%s</pre>' %
(len(connection.queries), total_time/1000, out.getvalue())
return response

20. http://localhost:8000/?dbprof

21. 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. 

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