Scaling Django Dc09

Scaling Django Web Apps
Mike Malone

djangocon 2009
Thursday, September 10, 2009

http://www.ﬂickr.com/photos/kveton/2910536252/

Pownce

• Large scale
• Hundreds of requests/sec

• Thousands of DB operations/sec

• Millions of user relationships

• Millions of notes

• Terabytes of static data

djangocon 2009 6

Pownce

• Encountered and eliminated many common
scaling bottlenecks
• Real world example of scaling a Django app
• Django provides a lot for free

• I’ll be focusing on what you have to build yourself,
and the rare places where Django got in the way

djangocon 2009 7

Scalability


Scalability

Scalability is NOT:
• Speed / Performance
• Generally affected by language choice
• Achieved by adopting a particular technology

djangocon 2009 9

A Scalable Application
import time

def application(environ, start_response):
time.sleep(10)
start_response('200 OK', [('content-type', 'text/plain')])
return ('Hello, world!',)

djangocon 2009 10

A High Performance Application
def application(environ, start_response):
remote_addr = environ['REMOTE_ADDR']
f = open('access-log', 'a+')
f.write(remote_addr + "n")
f.flush()
f.seek(0)
hits = sum(1 for l in f.xreadlines()
if l.strip() == remote_addr)
f.close()
start_response('200 OK', [('content-type', 'text/plain')])
return (str(hits),)

djangocon 2009 11

Scalability

A scalable system doesn’t need to change when the
size of the problem changes.

djangocon 2009 12

Scalability

• Accommodate increased usage
• Accommodate increased data
• Maintainable

djangocon 2009 13

Scalability

• Two kinds of scalability
• Vertical scalability: buying more powerful
hardware, replacing what you already own

• Horizontal scalability: buying additional
hardware, supplementing what you already own

djangocon 2009 14

Vertical Scalability

• Costs don’t scale linearly (server that’s twice is
fast is more than twice as much)
• Inherently limited by current technology
• But it’s easy! If you can get away with it, good
for you.

djangocon 2009 15

Vertical Scalability

“ Sky scrapers are special. Normal
buildings don’t need 10 ﬂoor
foundations. Just build!
- Cal Henderson

djangocon 2009 16

Horizontal Scalability

The ability to increase a system’s capacity by adding
more processing units (servers)

djangocon 2009 17


It’s how large apps are scaled.

djangocon 2009 18


• A lot more work to design, build, and maintain
• Requires some planning, but you don’t have to
do all the work up front
• You can scale progressively...
• Rest of the presentation is roughly in order

djangocon 2009 19

Caching


Caching

• Several levels of caching available in Django
• Per-site cache: caches every page that doesn’t have
GET or POST parameters

• Per-view cache: caches output of an individual view

• Template fragment cache: caches fragments of a
template

• None of these are that useful if pages are
heavily personalized

djangocon 2009 21

Caching

• Low-level Cache API
• Much more ﬂexible, allows you to cache at any
granularity

• At Pownce we typically cached
• Individual objects

• Lists of object IDs

• Hard part is invalidation
djangocon 2009 22

Caching

• Cache backends:
• Memcached

• Database caching

• Filesystem caching

djangocon 2009 23

Caching

Use Memcache.

djangocon 2009 24

Sessions

Use Memcache.

djangocon 2009 25

Sessions

Or Tokyo Cabinet
http://github.com/ericﬂo/django-tokyo-sessions/
Thanks @ericﬂo

djangocon 2009 26

Caching
Basic caching comes free with Django:
from django.core.cache import cache

class UserProfile(models.Model):
...
def get_social_network_profiles(self):
cache_key = ‘networks_for_%s’ % self.user.id
profiles = cache.get(cache_key)
if profiles is None:
profiles = self.user.social_network_profiles.all()
cache.set(cache_key, profiles)
return profiles

djangocon 2009 27

Caching
Invalidate when a model is saved or deleted:
from django.db.models import signals

def nuke_social_network_cache(self, instance, **kwargs):
cache_key = ‘networks_for_%s’ % self.instance.user_id
cache.delete(cache_key)

signals.post_save.connect(nuke_social_network_cache,
sender=SocialNetworkProfile)
signals.post_delete.connect(nuke_social_network_cache,
sender=SocialNetworkProfile)

djangocon 2009 28

Caching

• Invalidate post_save, not pre_save
• Still a small race condition
• Simple solution, worked for Pownce:
• Instead of deleting, set the cache key to None for a
short period of time

• Instead of using set to cache objects, use add, which
fails if there’s already something stored for the key

djangocon 2009 29

Advanced Caching

• Memcached’s atomic increment and decrement
operations are useful for maintaining counts
• They were added to the Django cache API in
Django 1.1

djangocon 2009 30

Advanced Caching

• You can still use them if you poke at the
internals of the cache object a bit
• cache._cache is the underlying cache object
try:
result = cache._cache.incr(cache_key, delta)
except ValueError: # nonexistent key raises ValueError
# Do it the hard way, store the result.
return result

djangocon 2009 31

Advanced Caching

• Other missing cache API
• delete_multi & set_multi

• append: add data to existing key after existing data

• prepend: add data to existing key before existing
data

• cas: store this data, but only if no one has edited it
since I fetched it

djangocon 2009 32

Advanced Caching

• It’s often useful to cache objects ‘forever’ (i.e.,
until you explicitly invalidate them)
• User and UserProﬁle
• fetched almost every request

• rarely change

• But Django won’t let you
• IMO, this is a bug :(
djangocon 2009 33

The Memcache Backend
class CacheClass(BaseCache):
def __init__(self, server, params):
BaseCache.__init__(self, params)
self._cache = memcache.Client(server.split(';'))

def add(self, key, value, timeout=0):
if isinstance(value, unicode):
value = value.encode('utf-8')
return self._cache.add(smart_str(key), value,
timeout or self.default_timeout)

djangocon 2009 34

The Memcache Backend
class CacheClass(BaseCache):
def __init__(self, server, params):
BaseCache.__init__(self, params)
self._cache = memcache.Client(server.split(';'))

def add(self, key, value, timeout=None):
if timeout is None:
timeout = self.default_timeout
return self._cache.add(smart_str(key), value,
timeout)

djangocon 2009 35

Advanced Caching
• Typical setup has memcached running on web
servers
• Pownce web servers were I/O and memory
bound, not CPU bound
• Since we had some spare CPU cycles, we
compressed large objects before caching them
• The Python memcache library can do this
automatically, but the API is not exposed

djangocon 2009 36

Monkey Patching core.cache
from django.utils.encoding import smart_str
import inspect as i

if 'min_compress_len' in i.getargspec(cache._cache.set)[0]:
class CacheClass(cache.__class__):
def set(self, key, value, timeout=None,
min_compress_len=150000):
if timeout is None:
timeout = self.default_timeout
return self._cache.set(smart_str(key), value,
timeout, min_compress_len)
cache.__class__ = CacheClass

djangocon 2009 37

Advanced Caching

• Useful tool: automagic single object cache
• Use a manager to check the cache prior to any
single object get by pk
• Invalidate assets on save and delete
• Eliminated several hundred QPS at Pownce

djangocon 2009 38

Advanced Caching

All this and more at:

http://github.com/mmalone/django-caching/

djangocon 2009 39

Caching

Now you’ve made life easier for your DB server,
next thing to fall over: your app server.

djangocon 2009 40

Load Balancing


Load Balancing
• Out of the box, Django uses a shared nothing
architecture
• App servers have no single point of contention

• Responsibility pushed down the stack (to DB)

• This makes scaling the app layer trivial: just add
another server

djangocon 2009 42

Load Balancing
Spread work between multiple
nodes in a cluster using a load
balancer.
Load Balancer
• Hardware or software
• Layer 7 or Layer 4
App Servers

Database

djangocon 2009 43

Load Balancing
• Hardware load balancers
• Expensive, like $35,000 each, plus maintenance
contracts

• Need two for failover / high availability

• Software load balancers
• Cheap and easy, but more difﬁcult to eliminate as a
single point of failure

• Lots of options: Perlbal, Pound, HAProxy,Varnish,
Nginx

djangocon 2009 44

Load Balancing
• Most of these are layer 7 proxies, and some
software balancers do cool things
• Caching

• Re-proxying

• Authentication

• URL rewriting

djangocon 2009 45

Load Balancing
A common setup for large
operations is to use
redundant layer 4 hardware Hardware Balancers
balancers in front of a pool of
layer 7 software balancers. Software
Balancers

App
Servers

djangocon 2009 46

Load Balancing

• At Pownce, we used a single Perlbal balancer
• Easily handled all of our trafﬁc (hundreds of
simultaneous connections)

• A SPOF, but we didn’t have $100,000 for black box
solutions, and weren’t worried about service
guarantees beyond three or four nines

• Plus there were some neat features that we took
advantage of

djangocon 2009 47

Perlbal Reproxying

Perlbal reproxying is a really cool, and really poorly
documented feature.

djangocon 2009 48

Perlbal Reproxying
1. Perlbal receives request
2. Redirects to App Server
1. App server checks auth (etc.)
2. Returns HTTP 200 with X-
Reproxy-URL header set to
internal ﬁle server URL
3. File served from ﬁle server via
Perlbal

djangocon 2009 49

Perlbal Reproxying

• Completely transparent to end user
• Doesn’t keep large app server instance around
to serve ﬁle
• Users can’t access ﬁles directly (like they could
with a 302)

djangocon 2009 50

Perlbal Reproxying
Plus, it’s really easy:
def download(request, filename):
# Check auth, do your thing
response = HttpResponse()
response[‘X-REPROXY-URL’] = ‘%s/%s’ % (FILE_SERVER, filename)
return response

djangocon 2009 51

Load Balancing

Best way to reduce load on your app servers: don’t
use them to do hard stuff.

djangocon 2009 52

Queuing


Queuing
• A queue is simply a bucket that holds messages
until they are removed for processing by clients
• Many expensive operations can be queued and
performed asynchronously
• User experience doesn’t have to suffer
• Tell the user that you’re running the job in the
background (e.g., transcoding)

• Make it look like the job was done real-time (e.g.,
note distribution)

djangocon 2009 54

Queuing
• Lots of open source options for queuing
• Ghetto Queue (MySQL + Cron)
• this is the ofﬁcial name.

• Gearman

• TheSchwartz

• RabbitMQ

• Apache ActiveMQ

• ZeroMQ
djangocon 2009 55

Queuing
• Lots of fancy features: brokers, exchanges,
routing keys, bindings...
• Don’t let that crap get you down, this is really
simple stuff

• Biggest decision: persistence
• Does your queue need to be durable and
persistent, able to survive a crash?

• This requires logging to disk which slows things
down, so don’t do it unless you have to

djangocon 2009 56

Queuing

• Pownce used a simple ghetto queue built on
MySQL / cron
• Problematic if you have multiple consumers pulling
jobs from the queue

• No point in reinventing the wheel, there are
dozens of battle-tested open source queues to
choose from

djangocon 2009 57

Django Standalone Scripts
Consumers need to setup the Django environment

from django.core.management import setup_environ
from mysite import settings

setup_environ(settings)

djangocon 2009 58

THE DATABASE!


The Database

• Til now we’ve been talking about
• Shared nothing

• Pushing problems down the stack

• But we have to store a persistent and
consistent view of our application’s state
somewhere
• Enter, the database...
djangocon 2009 60

CAP Theorem

• Three properties of a shared-data system
• Consistency: all clients see the same data

• Availability: all clients can see some version of
the data

• Partition Tolerance: system properties hold
even when the system is partitioned & messages
are lost

• But you can only have two
djangocon 2009 61

CAP Theorem

• Big long proof... here’s my version.
• Empirically, seems to make sense.
• Eric Brewer
• Professor at University of California, Berkeley

• Co-founder and Chief Scientist of Inktomi

• Probably smarter than me

djangocon 2009 62

CAP Theorem

• The relational database systems we all use were
built with consistency as their primary goal
• But at scale our system needs to have high
availability and must be partitionable
• The RDBMS’s consistency requirements get in our
way

• Most sharding / federation schemes are kludges
that trade consistency for availability & partition
tolerance

djangocon 2009 63

The Database

• There are lots of non-relational databases
coming onto the scene
• CouchDB

• Cassandra

• Tokyo Cabinet

• But they’re not that mature, and they aren’t easy
to use with Django

djangocon 2009 64

Denormalization


Denormalization

• Django encourages normalized data, which is
usually good
• But at scale you need to denormalize
• Corollary: joins are evil

• Django makes it really easy to do joins using the
ORM, so pay attention

djangocon 2009 66

Denormalization

• Start with a normalized database
• Selectively denormalize things as they become
bottlenecks
• Denormalized counts, copied ﬁelds, etc. can be
updated in signal handlers

djangocon 2009 67

Replication


Replication

• Typical web app is 80 to 90% reads
• Adding read capacity will get you a long way
• MySQL Master-Slave replication

Read & Write

Read only

djangocon 2009 69

Replication

• Django doesn’t make it easy to use multiple
database connections, but it is possible

• Some caveats
• Slave lag interacts with caching in weird ways

• You can only save to your primary DB (the one
you conﬁgure in settings.py)
• Unless you get really clever...

djangocon 2009 70

Replication
1. Create a custom database wrapper by subclassing DatabaseWrapper
class SlaveDatabaseWrapper(DatabaseWrapper):
def _cursor(self, settings):
if not self._valid_connection():
kwargs = {
'conv': django_conversions,
'charset': 'utf8',
'use_unicode': True,
}
kwargs = pick_random_slave(settings.SLAVE_DATABASES)
self.connection = Database.connect(**kwargs)
...
cursor = CursorWrapper(self.connection.cursor())
return cursor

djangocon 2009 71

Replication
2. Custom QuerySet that uses primary DB for writes
class MultiDBQuerySet(QuerySet):
...
def update(self, **kwargs):
slave_conn = self.query.connection
self.query.connection = default_connection
super(MultiDBQuerySet, self).update(**kwargs)
self.query.connection = slave_conn

djangocon 2009 72

Replication
3. Custom Manager that uses your custom QuerySet
class SlaveDatabaseManager(db.models.Manager):
def get_query_set(self):
return MultiDBQuerySet(self.model,
query=self.create_query())

def create_query(self):
return db.models.sql.Query(self.model, connection)

djangocon 2009 73

Replication

Example on github:
http://github.com/mmalone/django-multidb/

djangocon 2009 74

http://bit.ly/multidb

Replication

• Goal:
• Read-what-you-write consistency for writer

• Eventual consistency for everyone else

• Slave lag screws things up

djangocon 2009 76

Replication

What happens when you become
write saturated?

djangocon 2009 77

Federation


Federation

• Start with Vertical Partitioning: split tables that
aren’t joined across database servers
• Actually pretty easy

• Except not with Django

djangocon 2009 79

Federation
django.db.models.base

FAIL!

djangocon 2009 80

Federation

• At some point you’ll need to split a single table
across databases (e.g., user table)
• Auto-increment PKs won’t work
• It’d be nice to have a UUIDField for PKs

• You can probably build this yourself

djangocon 2009 81

Proﬁling, Monitoring &
Measuring


Know your SQL

>>> Article.objects.filter(pk=3).query.as_sql()
('SELECT "app_article"."id", "app_article"."name",
"app_article"."author_id" FROM "app_article" WHERE
"app_article"."id" = %s ', (3,))

djangocon 2009 83

Know your SQL
>>> import sqlparse
>>> def pp_query(qs):
... t = qs.query.as_sql()
... sql = t[0] % t[1]
... print sqlparse.format(sql, reindent=True,
keyword_case='upper')
...
>>> pp_query(Article.objects.filter(pk=3))
SELECT "app_article"."id",
"app_article"."name",
"app_article"."author_id"
FROM "app_article"
WHERE "app_article"."id" = 3

djangocon 2009 84

Know your SQL

>>> from django.db import connection
>>> connection.queries
[{'time': '0.001', 'sql': u'SELECT "app_article"."id",
"app_article"."name", "app_article"."author_id" FROM
"app_article"'}]

djangocon 2009 85

Know your SQL

• It’d be nice if a lightweight stacktrace could be
done in QuerySet.__init__
• Stick the result in connection.queries

• Now we know where the query originated

djangocon 2009 86

Measuring

Django Debug Toolbar

http://github.com/robhudson/django-debug-toolbar/

djangocon 2009 87

Monitoring

You can’t improve what you don’t measure.
• Ganglia
• Munin

djangocon 2009 88

Measuring & Monitoring

• Measure
• Server load, CPU usage, I/O

• Database QPS

• Memcache QPS, hit rate, evictions

• Queue lengths

• Anything else interesting

djangocon 2009 89

All done... Questions?
Contact me at mjmalone@gmail.com or @mjmalone


Scaling Django Dc09

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (8)

Similar to Scaling Django Dc09

Similar to Scaling Django Dc09 (20)

Recently uploaded

Recently uploaded (20)

Scaling Django Dc09