Post on 17-Oct-2014
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Scaling Django Web AppsMike Malone
djangocon 2009Thursday, September 10, 2009
Thursday, September 10, 2009
Thursday, September 10, 2009
http://www.flickr.com/photos/kveton/2910536252/Thursday, September 10, 2009
Thursday, September 10, 2009
djangocon 2009
Pownce
• Large scale
• Hundreds of requests/sec
• Thousands of DB operations/sec
• Millions of user relationships
• Millions of notes
• Terabytes of static data
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
Scalability
Thursday, September 10, 2009
djangocon 2009
Scalability
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• Speed / Performance
• Generally affected by language choice
• Achieved by adopting a particular technology
Scalability is NOT:
Thursday, September 10, 2009
djangocon 2009
import time
def application(environ, start_response): time.sleep(10) start_response('200 OK', [('content-type', 'text/plain')]) return ('Hello, world!',)
A Scalable Application
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Thursday, September 10, 2009
djangocon 2009
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),)
A High Performance Application
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djangocon 2009
Scalability
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A scalable system doesn’t need to change when the size of the problem changes.
Thursday, September 10, 2009
djangocon 2009
Scalability
• Accommodate increased usage
• Accommodate increased data
• Maintainable
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
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.
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djangocon 2009
Vertical Scalability
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Sky scrapers are special. Normal buildings don’t need 10 floor foundations. Just build!
- Cal Henderson
“
Thursday, September 10, 2009
djangocon 2009
Horizontal Scalability
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The ability to increase a system’s capacity by adding more processing units (servers)
Thursday, September 10, 2009
djangocon 2009
Horizontal Scalability
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It’s how large apps are scaled.
Thursday, September 10, 2009
djangocon 2009
Horizontal Scalability
• 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
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Thursday, September 10, 2009
Caching
Thursday, September 10, 2009
djangocon 2009
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
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djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
Caching
• Cache backends:
• Memcached
• Database caching
• Filesystem caching
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djangocon 2009
Caching
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Use Memcache.
Thursday, September 10, 2009
djangocon 2009
Sessions
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Use Memcache.
Thursday, September 10, 2009
djangocon 2009
Sessions
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Or Tokyo Cabinethttp://github.com/ericflo/django-tokyo-sessions/
Thanks @ericflo
Thursday, September 10, 2009
djangocon 2009
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
Caching
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Basic caching comes free with Django:
Thursday, September 10, 2009
djangocon 2009
from django.core.cache import cachefrom 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)
Caching
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Invalidate when a model is saved or deleted:
Thursday, September 10, 2009
djangocon 2009
Caching
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• 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
Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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• Memcached’s atomic increment and decrement operations are useful for maintaining counts
• They were added to the Django cache API in Django 1.1
Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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• 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
Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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• 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
Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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• 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 :(
Thursday, September 10, 2009
djangocon 2009
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)
The Memcache Backend
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Thursday, September 10, 2009
djangocon 2009
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 isinstance(value, unicode): value = value.encode('utf-8') if timeout is None: timeout = self.default_timeout return self._cache.add(smart_str(key), value, timeout)
The Memcache Backend
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Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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• 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
Thursday, September 10, 2009
djangocon 2009
from django.core.cache import cachefrom django.utils.encoding import smart_strimport 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 isinstance(value, unicode): value = value.encode('utf-8') if timeout is None: timeout = self.default_timeout return self._cache.set(smart_str(key), value, timeout, min_compress_len) cache.__class__ = CacheClass
Monkey Patching core.cache
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djangocon 2009
Advanced Caching
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• 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
Thursday, September 10, 2009
djangocon 2009
Advanced Caching
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All this and more at:
http://github.com/mmalone/django-caching/
Thursday, September 10, 2009
djangocon 2009
Caching
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Now you’ve made life easier for your DB server,next thing to fall over: your app server.
Thursday, September 10, 2009
Load Balancing
Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
Load Balancing
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App Servers
Database
Load Balancer
Spread work between multiple nodes in a cluster using a load balancer.
• Hardware or software• Layer 7 or Layer 4
Thursday, September 10, 2009
djangocon 2009
Load Balancing
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• 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
Thursday, September 10, 2009
djangocon 2009
Load Balancing
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• Most of these are layer 7 proxies, and some software balancers do cool things
• Caching
• Re-proxying
• Authentication
• URL rewriting
Thursday, September 10, 2009
djangocon 2009
Load Balancing
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A common setup for large operations is to use redundant layer 4 hardware balancers in front of a pool of layer 7 software balancers.
Hardware Balancers
Software Balancers
App Servers
Thursday, September 10, 2009
djangocon 2009
Load Balancing
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• 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
Thursday, September 10, 2009
djangocon 2009
Perlbal Reproxying
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Perlbal reproxying is a really cool, and really poorlydocumented feature.
Thursday, September 10, 2009
djangocon 2009
Perlbal Reproxying
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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
Thursday, September 10, 2009
djangocon 2009
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)
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djangocon 2009
def download(request, filename): # Check auth, do your thing response = HttpResponse() response[‘X-REPROXY-URL’] = ‘%s/%s’ % (FILE_SERVER, filename) return response
Perlbal Reproxying
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Plus, it’s really easy:
Thursday, September 10, 2009
djangocon 2009
Load Balancing
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Best way to reduce load on your app servers: don’t use them to do hard stuff.
Thursday, September 10, 2009
Queuing
Thursday, September 10, 2009
djangocon 2009
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)
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Thursday, September 10, 2009
djangocon 2009
Queuing
• Lots of open source options for queuing
• Ghetto Queue (MySQL + Cron)
• this is the official name.
• Gearman
• TheSchwartz
• RabbitMQ
• Apache ActiveMQ
• ZeroMQ
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
from django.core.management import setup_environfrom mysite import settings
setup_environ(settings)
Django Standalone Scripts
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Consumers need to setup the Django environment
Thursday, September 10, 2009
THE DATABASE!
Thursday, September 10, 2009
djangocon 2009
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...
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
Denormalization
Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
Denormalization
• Start with a normalized database
• Selectively denormalize things as they become bottlenecks
• Denormalized counts, copied fields, etc. can be updated in signal handlers
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Thursday, September 10, 2009
Replication
Thursday, September 10, 2009
djangocon 2009
Replication
• Typical web app is 80 to 90% reads
• Adding read capacity will get you a long way
• MySQL Master-Slave replication
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Read & Write
Read only
Thursday, September 10, 2009
djangocon 2009
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...
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Thursday, September 10, 2009
djangocon 2009
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
Replication
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1. Create a custom database wrapper by subclassing DatabaseWrapper
Thursday, September 10, 2009
djangocon 2009
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
Replication
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2. Custom QuerySet that uses primary DB for writes
Thursday, September 10, 2009
djangocon 2009
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)
Replication
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3. Custom Manager that uses your custom QuerySet
Thursday, September 10, 2009
djangocon 2009
Replication
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http://github.com/mmalone/django-multidb/
Example on github:
Thursday, September 10, 2009
djangocon 2009
Replication
• Goal:
• Read-what-you-write consistency for writer
• Eventual consistency for everyone else
• Slave lag screws things up
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Thursday, September 10, 2009
djangocon 2009
Replication
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What happens when you become write saturated?
Thursday, September 10, 2009
Federation
Thursday, September 10, 2009
djangocon 2009
Federation
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• Start with Vertical Partitioning: split tables that aren’t joined across database servers
• Actually pretty easy
• Except not with Django
Thursday, September 10, 2009
djangocon 2009
Federation
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django.db.models.base
FAIL!
Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
Profiling, Monitoring & Measuring
Thursday, September 10, 2009
djangocon 2009
>>> 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,))
Know your SQL
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Thursday, September 10, 2009
djangocon 2009
>>> 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
Know your SQL
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Thursday, September 10, 2009
djangocon 2009
>>> 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"'}]
Know your SQL
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Thursday, September 10, 2009
djangocon 2009
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
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Thursday, September 10, 2009
djangocon 2009
Measuring
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Django Debug Toolbar
http://github.com/robhudson/django-debug-toolbar/
Thursday, September 10, 2009
djangocon 2009
Monitoring
• Ganglia
• Munin
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You can’t improve what you don’t measure.
Thursday, September 10, 2009
djangocon 2009
Measuring & Monitoring
• Measure
• Server load, CPU usage, I/O
• Database QPS
• Memcache QPS, hit rate, evictions
• Queue lengths
• Anything else interesting
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Thursday, September 10, 2009
All done... Questions?Contact me at mjmalone@gmail.com or @mjmalone
Thursday, September 10, 2009