Feedly

Build Status

Note

The Feedly open source project is in no way related to feedly.com. 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 where it powers the flat feed, aggregated feed and the notification system. (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

Authors

  • Thierry Schellenbach
  • Tommaso Barbugli
  • Guyon Morée

Resources

Tutorials

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(
        pin.user_id,
        PinVerb,
        pin.id,
        pin.influencer_id,
        time=make_naive(pin.created_at, pytz.utc),
        extra_context=dict(item_id=pin.item_id)
    )
    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)
feed.add(activity)

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(
        normal=PinFeed,
    )
    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

feedly.add_pin(pin)

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

@login_required
def feed(request):
    '''
    Items pinned by the people you follow
    '''
    context = RequestContext(request)
    feed = feedly.get_feeds(request.user.id)['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.

Features

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

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 Redis based, database fallback, very similar to Fashiolista’s old approach.

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

Facebook history

Django project with good naming conventions

Activity stream specification

Quora post on best practises

Quora scaling a social network feed

Redis ruby example

FriendFeed approach

Thoonk setup

Yahoo Research Paper

Twitter’s approach

Cassandra at Instagram