feedly 0.3.22

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

Feedly
------

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

Warning: Super alpha at the moment, not all dependencies included, come back in a few weeks :)
Or use it as a source of inspiration, more than a ready made project.

**What is a feed?**

A feed is a stream of content which is created by people or subjects you follow.
Prime examples are the Facebook newsfeed, your Twitter stream or your Pinterest following page.

Feeds are commonly also called: Activity Streams, activity feeds, news streams.

**Why is it hard?**

It's very hard to split up data for social sites. You can't easily store all Facebook users in Brasil on one server and the ones in The Netherlands on another. One of the recommended approaches to this problem is to publish your activity (ie a tweet on twitter) to all of your followers. These streams of content are hard to maintain and keep up to date, but they are really fast for the user and can easily be sharded.

**Feedly**

Feedly allows you to easily use Redis and Celery (an awesome task broker) to build infinitely scalable feeds.
The core functionality is located in 3 core classes.

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

Structures are basic building blocks wrapping python functionality around Redis datastructures. There are convenient objects for hashes, lists and sorted sets.

Activities is the content which is stored in a feed. It follows the nomenclatura from the [activity stream spec] [astream]
[astream]: http://activitystrea.ms/specs/atom/1.0/#activity.summary
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. They extend upon the data structures and add custom serialization logic and behavior.

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)
- Marker (FeedEndMarker, marker class allowing you to correctly cache an empty feed)

**Example**

```python
#Feedly level, on the background this spawns hundreds of tasks to update the feeds of your followers
love_feedly.add_love(love)
love_feedly.remove_love(love)
#Follow a user, adds their content to your feed
love_feedly.follow_user(follow)
love_feedly.unfollow_user(follow)

#Feed level, show the activities stored in the feed
feed = LoveFeed(user_id)
loves = feed[:20]
```

**Admin Interface**

You can find a basic admin interface at /feedly/admin/
Note that it's currently still tied into Fashiolista's use cases.
So this is one which will definitely require forking.

**Features**

Feedly uses celery and redis to build a system which is heavy in terms of writes, but
very light for reads.

- 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)
- It supports distributed redis calls (Threaded calls to multiple redis servers)


**Tradeoffs**

*Store Serialized activities or ids in the feed*
Every feed contains a list of activities. But do you store the data for this activity per feed, or do you only store the id and cache the activity data.
If you store the activity plus data your feed's memory usage will increase.
If you store the id you will need to make more calls to redis upon reads.
In general you will want to store the id to reduce memory usage. Only for notification style feeds which require aggregation (John and 3 other people started following you) you might consider including
the data neccesary to determine the unique keys for aggregation.

*Fallback to the database?*
In general I recommend starting with the database as a fallback. This allows you to get used to running the feed system in production and rebuilt when you eventually lose data.
If your site is already quite large and you want to support multiple content types (Facebook allows pictures, messages etc. Twitter only supports messages.) it will become
impossible to rebuild from the database at some point. If that's the case you need to be sure you have the skills to properly setup persistence storage on your redis slaves.



**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:

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

[etsy]: http://www.slideshare.net/danmckinley/etsy-activity-feeds-architecture/

[Facebook history] [facebook]

[facebook]: http://www.infoq.com/presentations/Facebook-Software-Stack

[Django project, with good naming conventions.] [djproject]
[djproject]: http://justquick.github.com/django-activity-stream/
http://activitystrea.ms/specs/atom/1.0/
(actor, verb, object, target)

[Quora post on best practises] [quora]

[quora]: http://www.quora.com/What-are-best-practices-for-building-something-like-a-News-Feed?q=news+feeds

[Quora scaling a social network feed] [quora2]

[quora2]: http://www.quora.com/What-are-the-scaling-issues-to-keep-in-mind-while-developing-a-social-network-feed

[Redis ruby example] [redisruby]

[redisruby]: http://blog.waxman.me/how-to-build-a-fast-news-feed-in-redis

[FriendFeed approach] [friendfeed]

[friendfeed]: http://backchannel.org/blog/friendfeed-schemaless-mysql

[Thoonk setup] [thoonk]

[thoonk]: http://blog.thoonk.com/

[Yahoo Research Paper] [yahoo]

[yahoo]: http://research.yahoo.com/files/sigmod278-silberstein.pdf

[Twitter’s approach] [twitter]

[twitter]: http://www.slideshare.net/nkallen/q-con-3770885


**Scalable Notification Systems**

Fortunately building a scalable notification system is almost entirely identical to an activity feed. There is a feed, it is sometimes aggregated (grouped) and it contains activity.
It has a different purpose for the user:

* show activity on your account vs
* activity by your followers

From a tech standpoint though, the implementations are almost identical. The main objects are:

* AggregatedActivity (Stores many activities)
* Activity (Actor, Verb, Object, Target)
* Verb

Activities are best explained with a small example:

Tommaso added your find to his list "back in black"
Activity(actor=Tommaso, verb=Add, object=find, target=list)
Vannesa loved your find
Activity(actor=Vannesa, verb=Love, object=find)
Tommaso loved your find
Activity(actor=Tommaso, verb=Love, object=find)

For notification you will often collapse the last two into:

Tommaso and Vanessa loved your find
AggregatedActivity(group=loved_find_today, first_seen, last_seen, activities, seen_at, read_at)

The storage and access logic is handled using three classes

* NotificationFeedly (Integration between your app and the data structure)
* NotificationFeed (Handles serialization and redis communication to store your aggregated activities)
* Aggregator (Determines when to aggregated several activities into an aggregated activity)

Tutorial

Step 1 - Subclass NotificationFeed

```python
class MyNotificationFeed(NotificationFeed):
def get_aggregator(self):
aggregator_class = RecentVerbAggregator
aggregator = aggregator_class()
return aggregator
```

Step 2 - Subclass the aggregator

```python
class RecentVerbAggregator(BaseAggregator):
'''
Aggregates based on the same verb and same time period
'''
def get_group(self, activity):
'''
Returns a group based on the day and verb
'''
verb = activity.verb.id
date = activity.time.date()
group = '%s-%s' % (verb, date)
return group
```

Step 3 - Test adding data

```python
feed = MyNotificationFeed(user_id)
activity = Activity(
user_id, LoveVerb, object_id, influencer_id, time=created_at,
extra_context=dict(entity_id=self.entity_id)
)
feed.add(activity)
print feed[:5]
```

Step 4 - Subclass NotificationFeedly
```python
# See feedly/notification_feedly for a full example
class MyNotificationFeedly(Feedly):
'''
Abstract the access to the notification feed
'''
def add_love(self, love):
feed = MyNotificationFeed(user_id)
activity = Activity(
love.user_id, LoveVerb, love.id, love.influencer_id,
time=love.created_at, extra_context=dict(entity_id=self.entity_id)
)
feed.add(activity)
```