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feedly 0.9.40

Feedly allows you to build complex feed and caching structures using Redis.

Latest Version: 0.11.3


[![Build Status](](

[![Coverage Status](](


The Feedly open source project is in no way related to To avoid confusion we are considering renaming the 1.0 release of the project.

## What can you build? ##

Feedly allows you to build newsfeed and notification systems using Cassandra and/or Redis.
Examples of what you can build are the Facebook newsfeed, your Twitter stream or your Pinterest following page.
We've built Feedly for [Fashiolista] [fashiolista] where it powers the [flat feed] [fashiolista_flat], [aggregated feed] [fashiolista_aggregated] and the [notification system] [fashiolista_notification].
(Feeds are also commonly called: Activity Streams, activity feeds, news streams.)


To quickly make you acquainted with Feedly, we've created a Pinterest like example application, you can find it [here] [example_app_link]


* Thierry Schellenbach
* Tommaso Barbugli
* Guyon Morée

## Using Feedly ##

This quick example will show you how to publish a Pin to all your followers. So lets create
an activity for the item you just pinned.

def create_activity(pin):
from feedly.activity import Activity
activity = Activity(
time=make_naive(pin.created_at, pytz.utc),
return activity

Next up we want to start publishing this activity on several feeds.
First of we want to insert it into your personal feed, and secondly into the feeds of all your followers.
Lets start first by defining these feeds.

# setting up the feeds

class PinFeed(RedisFeed):
key_format = 'feed:normal:%(user_id)s'

class UserPinFeed(PinFeed):
key_format = 'feed:user:%(user_id)s'

Writing to these feeds is very simple. For instance to write to the feed of user 13 one would do


feed = UserPinFeed(13)

But we don't want to publish to just one users feed. We want to publish to the feeds of all users which follow you.
This action is called a fanout and is abstracted away in the Feedly manager class.
We need to subclass the Feedly class and tell it how we can figure out which user follow us.


class PinFeedly(Feedly):
feed_classes = dict(
user_feed_class = UserPinFeed

def add_pin(self, pin):
activity = pin.create_activity()
# add user activity adds it to the user feed, and starts the fanout
self.add_user_activity(pin.user_id, activity)

def get_user_follower_ids(self, user_id):
ids = Follow.objects.filter(target=user_id).values_list('user_id', flat=True)
return {FanoutPriority.HIGH:ids}

feedly = PinFeedly()

Now that the feedly class is setup broadcasting a pin becomes as easy as


Calling this method wil insert the pin into your personal feed and into all the feeds of users which follow you.
It does so by spawning many small tasks via Celery. In Django (or any other framework) you can now show the users feed.

# django example

def feed(request):
Items pinned by the people you follow
context = RequestContext(request)
feed = feedly.get_feeds(['normal']
activities = list(feed[:25])
context['activities'] = activities
response = render_to_response('core/feed.html', context)
return response


This example only briefly covered how Feedly works.
The full explanation can be found on read the docs.


[Installing Feedly] [docs_install]
[Settings] [docs_settings]
[Feedly (Feed manager class) implementation] [docs_feedly]
[Feed class implementation] [docs_feed]
[Choosing the right storage backend] [docs_storage_backend]
[Building notification systems] [docs_notification_systems]


[Pinterest style feed example app] [mellowmorning_example]

## Feedly Design ##

*The first approach*

A first feed solution usually looks something like this:

SELECT * FROM tweets
JOIN follow ON (follow.target_id = tweet.user_id)
WHERE follow.user_id = 13

This works in the beginning, and with a well tuned database will keep on working nicely for quite some time.
However at some point the load becomes too much and this approach falls apart. Unfortunately it's very hard
to split up the tweets in a meaningfull way. You could split it up by date or user, but every query will still hit
many of your shards. Eventually this system collapses, read more about this in [Facebook's presentation] [facebook].

*Push or Push/Pull*
In general there are two similar solutions to this problem.

In the push approach you publish your activity (ie a tweet on twitter) to all of your followers. So basically you create a small list
per user to which you insert the activities created by the people they follow. This involves a huge number of writes, but reads are really fast they can easily be sharded.

For the push/pull approach you implement the push based systems for a subset of your users. At Fashiolista for instance we used to
have a push based approach for active users. For inactive users we only kept a small feed and eventually used a fallback to the database
when we ran out of results.


Feedly uses celery and Redis/Cassandra to build a system with heavy writes and extremely light reads.
It features:

- Asynchronous tasks (All the heavy lifting happens in the background, your users don't wait for it)
- Reusable components (You will need to make tradeoffs based on your use cases, Feedly doesnt get in your way)
- Full Cassandra and Redis support
- The Cassandra storage uses the new CQL3 and Python-Driver packages, which give you access to the latest Cassandra features.
- Built for the extremely performant Cassandra 2.0


Feedly allows you to easily use Cassndra/Redis and Celery (an awesome task broker) to build infinitely scalable feeds.
The high level functionality is located in 4 classes.

- Activities
- Feeds
- Feed managers (Feedly)
- Aggregators

*Activities* are the blocks of content which are stored in a feed. It follows the nomenclatura from the [activity stream spec] [astream]
Every activity therefor stores at least:

- Time (the time of the activity)
- Verb (the action, ie loved, liked, followed)
- Actor (the user id doing the action)
- Object (the object the action is related to)
- Extra context (Used for whatever else you need to store at the activity level)

Optionally you can also add a target (which is best explained in the activity docs)

*Feeds* are sorted containers of activities. You can easily add and remove activities from them.

*Feedly* classes (feed managers) handle the logic used in addressing the feed objects.
They handle the complex bits of fanning out to all your followers when you create a new object (such as a tweet).

In addition there are several utility classes which you will encounter

- Serializers (classes handling serialization of Activity objects)
- Aggregators (utility classes for creating smart/computed feeds based on algorithms)
- Timeline Storage (cassandra or redis specific storage functions for sorted storage)
- Activity Storage (cassandra or redis specific storage for hash/dict based storage)

## Background Articles ##

A lot has been written about the best approaches to building feed based systems.
Here's a collection on some of the talks:

[Twitter 2013] [twitter_2013]
Redis based, database fallback, very similar to Fashiolista's old approach.


[Etsy feed scaling] [etsy]
(Gearman, separate scoring and aggregation steps, rollups - aggregation part two)


[Facebook history] [facebook]


[Django project, with good naming conventions.] [djproject]
(actor, verb, object, target)

[Quora post on best practises] [quora]


[Quora scaling a social network feed] [quora2]


[Redis ruby example] [redisruby]


[FriendFeed approach] [friendfeed]


[Thoonk setup] [thoonk]


[Yahoo Research Paper] [yahoo]


[Twitter’s approach] [twitter]


[Cassandra at Instagram] [instagram]


## Developing Feedly ##

**Vagrant and Pinterest example**

Clone the github repo and run the following commands to setup your development environment using vagrant.
Booting a vagrant machine will take a bit of time, be sure to grab a cup of coffee while waiting for vagrant up to complete.

From the root of the feedly project run:
>>> vagrant up
>>> vagrant provision
>>> vagrant ssh
>>> git clone pinterest_example
>>> cd pinterest_example
>>> python runserver 0:8000

Visit []( to see the example app up and running.
The most interesting bit of example code are located in:

core/ and core/

The included Pinterest example app has its own test suite. You can run this by executing
>>> python pinterest_example/ test core
File Type Py Version Uploaded on Size (md5) Source 2014-02-28 137KB