Advanced Aspects of the Django Ecosystem: Haystack, Celery & Fabric

Advanced Aspects of
the Django Ecosystem
Haystack, Celery & Fabric
Simon Willison, @simonw
EuroPython, 21st June 2011
http://lanyrd.com/sftzq

Haystack

Top quality full-text search in seconds

Celery

Ofﬂine processing in an instant

Fabric

Automated deployments for lazy people

More Like This
Faceting
Stored (non-indexed) fields
Highlighting
Spelling Suggestions
Boost

Find the needle you're looking for. Download Documentation

Search doesn't have to be hard. Haystack lets you write your search code Sprinting to 1.1-final
Posted on 2010/11/16 by Daniel
once and choose the search engine you want it to run on. With a familiar API Though this site has sat out of
that should make any Djangonaut feel right at home and an architecture that date, there has been a lot of
work put into Haystack 1.1. As
allows you to swap things in and out as you need to, it's how search ought of writing, there are eight issues
to be. blocking the release. I aim to
have those down to zero by the
end of the week.
Haystack is BSD licensed , plays nicely with third-party app without needing
to modify the source and supports Solr , Whoosh and Xapian . Once those eight are done, I will
be releasing 1.1-final. The RC
process really didn't do much
Get started last time and this release has
been a long time in coming. This
1. Get the most recent source. release will feature:
2. Add haystack to your INSTALLED_APPS.
3. Create search_indexes.py files for your models. Vastly improved faceting
4. Setup the main SearchIndex via autodiscover. Whoosh 1.X support!
5. Include haystack.urls to your URLconf. Document & field boost
6. Search! support

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Cosmos home | All exhibition items | Search

Cosmos and Culture

We have 101 items, 137 people and organisations and
32 celestial bodies.

Explore how astronomy has changed the way we see our universe
—and ourselves—through this object-rich exhibition. From ancient
heritage to cutting edge technology, trace the history of people and
the stars through different stories drawn from around the world.

See more on the main cosmos site

Search for in Everything Search

Nocturnal — 1702 Rue, Warren de la Leo celestial body

item person
Islamic horary quadrant,
This nocturnal allows you to Kew Photoheliograph, 1700-99
tell the time at night. As the (SAC, designer, user) Horary quadrants determine
Earth turns, the stars appear — 1857 the time from the altitude of
to move across ... the Sun. This Islamic
This is the first instrument
that was purpose built for instrument may have been

Contact us | Sign up for our mailing lists |

Cosmos home | All exhibition items | Search

Search

Search for moon in Everything Search

Results See all
Moon celestial body Items

Moon People

Celestial bodies
Moon, Francis Graham person

Moon, Francis Graham

Diapositives of photographs taken with the
Kew Photoheliograph item
...Moon were taken with the Kew Photoheliograph, the
large instrument in the corner of this showcase. The Moon
image, on the right, was taken at Kew Observatory. The
Sun image, on the left, was taken d... read more

Print of New Discoveries on the Moon item

...Moon was inspired by the Great Moon Hoax of 1835.
The New York Sun reported that astronomer John Herschel

Model-oriented search

Deﬁne search_indexes.py (like admin.py) for
your application

Hook up default haystack search views

Write a quick search.html template

Run ./manage.py rebuild_index

from haystack import indexes
from haystack import site
from models import MuseumObject, Person, CelestialBody

class MuseumObjectIndex(indexes.SearchIndex):
text = indexes.CharField(document=True, model_attr='text')
def get_queryset(self):
return MuseumObject.objects.all()

site.register(MuseumObject, MuseumObjectIndex)

class PersonIndex(indexes.SearchIndex):
text = indexes.CharField(document=True, model_attr='name')
def get_queryset(self):
return Person.objects.all()

site.register(Person, PersonIndex)

(r'^search/', include('haystack.urls')),

<ul class="listing">
{% for result in page.object_list %}
! {% if result.model_name == "museumobject" %}
! <li class="item">
! ! <img src="{{ result.object.image_inline }}" width="75" alt="">
! ! <h3><a href="{{ result.object.get_absolute_url }}">{{ result.object.name }}</a>
! ! <span class="type-indicator">item</span></h3>
! ! <p>{% highlight result.text with request.GET.q %}</p>
! </li>
! {% endif %}
! {% if result.model_name == "person" %}
! <li class="person">
! ! <h3><a href="{{ result.object.get_absolute_url }}">{{ result.object.name }}</a>
! ! <span class="type-indicator">person</span></h3>
! ! <p>{% highlight result.text with request.GET.q %}</p>
! </li>
! {% endif %}
{% endfor %}
</ul>

Pick your backend
Whoosh - pure Python

For sites with no write trafﬁc

Solr - Java web service application server

Best bet for medium-huge sites

Xapian - embedded C library

Haven’t tried this myself

apache > lucene > solr

Search the site with Solr Search
Main Wiki Powered by Lucid Imagination
Last Published: Sat, 04 Jun 2011 12:23:42 GMT
About
Welcome
Who We Are
Welcome to Solr
Documentation
PDF
Resources
What Is Solr?
Related Projects
Get Started
News
May 2011 - Solr 3.2 Released
March 2011 - Solr 3.1 Released
25 June 2010 - Solr 1.4.1 Released
7 May 2010 - Apache Lucene Eurocon 2010 Coming to Prague May 18-21
10 November 2009 - Solr 1.4 Released
20 August 2009 - Solr's first book is published!
18 August 2009 - Lucene at US ApacheCon
09 February 2009 - Lucene at ApacheCon Europe 2009 in Amsterdam
19 December 2008 - Solr Logo Contest Results
03 October 2008 - Solr Logo Contest
15 September 2008 - Solr 1.3.0 Available
28 August 2008 - Lucene/Solr at ApacheCon New Orleans
03 September 2007 - Lucene at ApacheCon Atlanta
06 June 2007: Release 1.2 available
17 January 2007: Solr graduates from Incubator
22 December 2006: Release 1.1.0 available
15 August 2006: Solr at ApacheCon US
21 April 2006: Solr at ApacheCon
21 February 2006: nightly builds
17 January 2006: Solr Joins Apache Incubator

What Is Solr?

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Search
We found 3 results for “django”

FILTER BY
django Search type
Sessions 3

Your current filters are…
TYPE: Sessions TOPIC: NoSQL PLACE: United States Clear all filters
FILTER BY
topic
NoSQL and Django Panel
EVENT DjangoCon US 2010 NoSQL 3
TIME 9th September 2010 09:00-10:00
SPEAKERS Jacob Burch Django 2

Cassandra 1
Step Away From That Database
EVENT DjangoCon US 2010
TIME 8th September 2010 11:20-12:00 FILTER BY
SPEAKERS Andrew Godwin
place
Apache Cassandra in Action United States 3

EVENT Strata 2011 Multnomah 2
TIME 1st February 2011 13:30-17:00
Oregon 2
SPEAKERS Jonathan Ellis
Portland 2
Santa Clara 1
California 1

class BookIndex(indexes.SearchIndex):
text = indexes.CharField(document=True, use_template=True)
speakers = indexes.MultiValueField()
topics = indexes.MultiValueField()

def prepare_speakers(self, obj):
return [a.user.t_id for a in obj.authors.exclude(
user = None
).select_related('user')]

def prepare_topics(self, obj):
return list(obj.topics.values_list('pk', ﬂat=True))

search/indexes/
books/book_text.txt
{{ object.title }}
{{ object.tagline }}
{% for author in object.authors.all %}
{{ author.display_name }}
{{ author.user.t_screen_name }}
{% endfor %}
{% for topic in object.topics.all %}
{{ topic.name_en }}
{% endfor %}

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calendar conferences coverage profile

search

Your contacts' calendar
yours 24 contacts 182

Simon
We've found 182 conferences your Twitter contacts are Willison
interested in. Your profile
page
TODAY Café Scientifique: Exploring Attend
21
the dark side of star Track
formation with the Herschel From our blog
Space Observatory Welcoming Sophie
United Kingdom / Brighton Barrett to team
21st June 2011 Lanyrd
Astronomy Science Today we have a very special
announcement (and for once,
4 contacts tracking
it's not a new feature!) We
would like to welcome the
super-wonderful Sophie Barrett
to the Lanyrd team.

21 Usability Professionals' Attend Session schedules in
Association – International Track your calendar
Conference You can now subscribe to event
schedules in your calendar of
United States / Atlanta
choice. Stay up to date at the
21st–24th June 2011 event with the schedule in the
Usability User Experience pocket where you need it.

1 contact speaking and 3 contacts tracking
Venues (and venue
maps)

sqs = SearchQuerySet()
sqs = sqs.models(Conference)

or_string = ' OR '.join(contact_ids)
sqs = sqs.narrow('attendees:(%s)' % or_string)

Staying fresh
Search engines usually don’t like accepting
writes too frequently

RealTimeSearchIndex for low trafﬁc sites

./manage.py update_index --age=6 (hours)

Uses index.get_updated_ﬁeld()

Roll your own (message queue or similar...)

Smarter indexing
class Article(models.Model):
needs_indexing = models.BooleanField(
default = True, db_index = True
)
...
def save(self, *args, **kwargs):
self.needs_indexing = True
super(Article, self).save(*args, **kwargs)

index = site.get_index(model)
updated_pks = []

objects = index.load_all_queryset().ﬁlter(
needs_indexing=True
)[:100]
if not objects:
return

for object in objects:
updated_pks.append(object.pk)
index.update_object(object)

index.load_all_queryset().ﬁlter(
pk__in = updated_pks
).update(needs_indexing = False)

Replication

Solr Master

Solr Slave Solr Slave Solr Slave

nginx + Solr
replication trick
upstream solrmaster { server {
server 10.68.43.214:8080; listen 8983;
} location /solr/update {
upstream solrslaves { proxy_pass http://solrmaster;
server 10.68.43.214:8080; }
server 10.193.138.80:8080; location /solr/select {
server 10.204.143.106:8080; proxy_pass http://solrslaves;
} }
}

Home Download Community Documentation Code

Background Processing Distributed
Asynchronous/Synchronous Concurrency
Background Processing Distributed
Periodic Tasks Retries
Asynchronous/Synchronous Concurrency
Periodic Tasks Retries

Distributed Task Queue Celery 2.2 released!
By @asksol on 2011-02-01.
Celery is an asynchronous task queue/job queue based on distributed
message passing. It is focused on real-time operation, but supports A great number of new features,
scheduling as well. including Jython, eventlet and gevent
support. Everything is detailed in the
The execution units, called tasks, are executed concurrently on a single
Changelog, which you should have read
or more worker servers using multiprocessing, Eventlet, or gevent.
before upgrading.
Tasks can execute asynchronously (in the background) or
synchronously (wait until ready). Users of Django must also upgrade to
django-celery 2.2.
Celery is used in production systems to process millions of tasks a day.
This release would not have been
Celery is written in Python, but the protocol can be implemented in
possible without the help of
any language. It can also operate with other languages using
contributors and users, so thank you,
webhooks.
and congratulations!
The recommended message broker is RabbitMQ, but limited support
for Redis, Beanstalk, MongoDB, CouchDB, and databases (using
Celery 2.1.1 bugfix
SQLAlchemy or the Django ORM) is also available.
release
By @asksol on 2010-10-14.

Celery is easy to integrate with Django, Pylons and Flask, using the
All users are urged to upgrade. For a list
django-celery, celery-pylons and Flask-Celery add-on packages.
of changes see the Changelog.
Example Users of Django must also upgrade to
This is a simple task adding two numbers: django-celery 2.1.1.

Tasks?

Anything that takes more than about 200ms

Updating a search index

Resizing images

Hitting external APIs

Generating reports

Trivial example
Fetch the content of a web page
from celery.task import task

@task
def fetch_url(url):
return urllib.urlopen(url).read()

>>> result = fetch_url.delay(‘http://cnn.com/’)
>>> html = result.wait()

Python and MongoDB EuroPython 2011
Italy / Florence

tutorial
19th–26th June 2011

TELL YOUR FRIENDS!
Tweet about this
A session at EuroPython 2011 session

Andreas
Jung WHEN
CEO, ZOPYX Ltd Time 14:30–18:30 CET
Date 20th June 2011
MongoDB is the new star of the so-called NoSQL databases. Using
Python with MongoDB is the next logical step after having used SESSION HASH TAG
Python for years with relational databases. #sftzh

This talk will give an introduction into MongoDB and demonstrate SHORT URL
how MongoDB can be be used from Python. lanyrd.com/sftzh

More information can be found under: OFFICIAL SESSION
PAGE
http://www.zopyx.com/resources/python-mongodb-tutorial-at... ep2011.europython.eu/conf

View the schedule

More sessions at EuroPython 2011 on Python
Topics
MongoDB
Add coverage to this session Python

http://www.slideshare.net/ajung/python-mo Edit topics
A URL to coverage such as videos, slides, podcasts, handouts, sketchnotes, photos
etc. SCHEDULE
INCOMPLETE?
Add Add another session

Add coverage
http://www.slideshare.net/ajung/python-mongo-
dbtrainingeurop...

Link title Python and
MongoDB tutorial

Python mongo db-training-europython-2011 EuroPython 2011
Italy / Florence
19th–26th June 2011
Type of coverage
Link Audio Liveblog
Write-up Sketch notes Photos
Slides Transcript Notes
Video Handout

Coverage preview
From SlideShare:

The task itself...

Tries using http://embed.ly/ to find a preview

Fetches the HTTP headers and first 2048 bytes

If HTML, attempts to extract the <title>

If other, gets the file type and size from headers

Behind the scenes...
ar = enhance_link.delay(url)
poll_url = '/working/%s/' % signed.dumps({
'task_id': ar.task_id,
'on_done_url': on_done_url,
})
if 'ajax' in request.POST:
return render_json(request, {
'ok': True,
'poll_url': poll_url,
})
else:
return HttpResponseRedirect(poll_url)

And when it’s done...

from celery.backends import default_backend

...
task_id = request.REQUEST.get('id', '')
result = default_backend.get_result(task_id)

Conﬁguration
# Carrot / Celery: queue uses Redis
CARROT_BACKEND = "ghettoq.taproot.Redis"
BROKER_HOST = " 10.11.11.11" # redis server
BROKER_PORT = 6379
BROKER_VHOST = "6"

# Task results stored in memcached, so they can
# expire automatically
CELERY_RESULT_BACKEND = "cache"
CELERY_CACHE_BACKEND =
"memcached://10.11.11.12:11211;..."

Advanced Celery

celerybeat for scheduling periodic tasks (a
smarter version of cron)

celeryev / celerymon for monitoring your
worker cluster

celerycam for snapshotting cluster state

The Activity
Stream pattern
How do you implement Twitter?

Give every user an “inbox” list of message IDs
from the people they follow

Write an ID in to EVERY follower’s inbox when a
user tweets

@timoreilly has 1,473,990 followers

redis at 100,000 writes/second = 1.5 seconds

Fabric v1.0.1 documentation » next | modules | index

Table Of Contents
Fabric
Fabric
About
Installation
Development About
Documentation
Fabric is a Python (2.5 or higher) library and command-line tool for streamlining the use of SSH for
Tutorial
Usage documentation application deployment or systems administration tasks.
FAQ It provides a basic suite of operations for executing local or remote shell commands (normally or via
API documentation
sudo ) and uploading/downloading files, as well as auxiliary functionality such as prompting the
Core API
running user for input, or aborting execution.
Contrib API
Changes from previous versions
Typical use involves creating a Python module containing one or more functions, then executing them
Getting help via the fab command-line tool. Below is a small but complete “fabfile” containing a single task:
Mailing list
Twitter from fabric.api import run
Bugs/ticket tracker
IRC def host_type():
Wiki run('uname -s')

Next topic Once a task is defined, it may be run on one or more servers, like so:

Overview and Tutorial
$ fab -H localhost,linuxbox host_type
[localhost] run: uname -s
This Page [localhost] out: Darwin
Show Source [linuxbox] run: uname -s
[linuxbox] out: Linux

Done.
Quick search Disconnecting from localhost... done.
Disconnecting from linuxbox... done.
Go
Enter search terms or a module,
In addition to use via the fab tool, Fabric’s components may be imported into other Python code,
class or function name.

Turn Python functions in to command line
arguments

High level abstraction over SSH for running
commands on remote machines

Automated deployments

Every project needs automated deployments

Repeatable, documented, easy to roll back

Ops people rave about Chef and Puppet

Powerful... but hard to set up

Fabric: take your current procedure, wrap it in
a few lines of Python

Simple examples
# fabﬁle.py
from fabric.api import run

def clear_cache():
run('echo "ﬂush_all" | nc localhost 11211')

$ fab -H memcach1,memcache2 clear_cache

Simple examples
REDIS_URL = 'http://mirrors.kernel.org/ubuntu/pool/' +
'universe/r/redis/redis-server_2.0.0~rc2-1_amd64.deb'

def bootstrap_redis():
run('cd /tmp && wget %s' % REDIS_URL)
sudo('dpkg --force-confnew -i /tmp/redis-server_2.0.0~rc2-1_amd64.deb')
put('conﬁg-ﬁles/redis.conf', '/tmp/redis.conf')
sudo('mv /tmp/redis.conf /etc/redis/redis.conf')
sudo('/etc/init.d/redis-server restart')

Simple examples
def git_export():
env.deploy_date = datetime.datetime.now().strftime(
'%Y-%m-%d-%H%M%S'
)
env.export_path = '/tmp/export/%s' % (env.deploy_date)
local('mkdir -p %(export_path)s' % env)
local(
'cd .. && git archive --preﬁx=lanyrd/ --format=tar ' +
'master | tar -x -C %(export_path)s' % env
)

$ fab git_export

Rollback with symlinks

def repoint_symlink():
with settings(warn_only = True):
run('rm %(deploy_dir)s/previous' % env)
run('mv %(deploy_dir)s/current %(deploy_dir)s/previous' % env)
run('ln -s %(deploy_dir)s/%(deploy_date)s %(deploy_dir)s/current' % env)

def rollback():
run('mv %(deploy_dir)s/current %(deploy_dir)s/_previous' % env)
run('mv %(deploy_dir)s/previous %(deploy_dir)s/current' % env)
run('mv %(deploy_dir)s/_previous %(deploy_dir)s/previous' % env)

servers.json
{
"servers": {
"appserver1": {
"instance_id": "i-a13432d",
"public_dns": "ec2-111-11-121-211.compute-1.amazonaws.com",
"private_dns": "ip-10-195-11-112.ec2.internal",
"private_ip": "10.195.11.112"
},
"appserver2": {
"instance_id": "i-a34344e",
"public_dns": "ec2-112-11-121-211.compute-1.amazonaws.com",
"private_dns": "ip-10-204-111-116.ec2.internal",
"private_ip": "10.204.111.116"
},

servers.json
....
"roles": {
"appserver": ["appserver1", "appserver2"],
"solrmaster": ["services3"],
"solrslave": ["appserver1", "appserver2", "services2"],
"solrread": ["services3", "appserver1", "appserver2", "services2"],
"redis": ["services3"],
"queuebroker": ["services3"],
"memcached": ["appserver1", "appserver2", "services3"],
}
}

In the fabﬁle.py
_js = json.load(open('servers.json'))
servers = _js['servers']
roles = _js['roles']

def server(name):
env.hosts = env.hosts or []
env.hosts.append('ubuntu@%s' % servers[name]['public_dns'])

def role(name):
for server_name in roles[name]:
server(server_name)

$ fab role:memcached clear_cache

Dynamic nginx config
def deploy_nginx_config():
def _nginx_backends(role, port):
return 'n'.join(
' server %s:%s;' % (info['private_ip'], port)
for name, info in servers.items() if name in roles[role]
)
content = open('config-files/nginx.conf').read()
content = content % {
'backends': _nginx_backends('appserver', 8000),
'solrmaster': _nginx_backends('solrmaster', 8080),
'solrslaves': _nginx_backends('solrread', 8080),
}
open('/tmp/nginx.conf', 'w').write(content)
put('/tmp/nginx.conf', '/tmp/nginx.conf')
sudo('mv /tmp/nginx.conf /etc/nginx/nginx.conf')

$ fab role:loadbalancer deploy_nginx_config

Dream setup
Web interface to push a git tag to staging

Big Red Button to push staging to production

http://lanyrd.com/sftzq

Advanced Aspects of the Django Ecosystem: Haystack, Celery & Fabric

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