Building a complete social networking platform presents many challenges at scale. Socialite is a reference architecture and open source Java implementation of a scalable social feed service built on DropWizard and MongoDB. We'll provide an architectural overview of the platform, explaining how you can store an infinite timeline of data while optimizing indexing and sharding configuration for access to the most recent window of data. We'll also dive into the details of storing a social user graph in MongoDB.

1. #MongoDBdays #askAsya @asya999
Building a Social Platform
with MongoDB
Asya Kamsky
MongoDB Inc

2. Solutions Engineering
• Identify Popular Use Cases
– Directly from MongoDB Users
– Addressing "limitations"
• Go beyond documentation and blogs
• Create open source project
• Run it!

3. Social Status Feed

4. Status Feed

5. Status Feed

6. Socialite
https://github.com/10gen-labs/socialite
• Open Source
• Reference Implementation
– Various Fanout Feed Models
– User Graph Implementation
– Content storage
• Configurable models and options
• REST API in Dropwizard (Yammer)
– https://dropwizard.github.io/dropwizard/
• Built-in benchmarking

7. Architecture
Content Proxy
Graph Service Proxy

8. Pluggable Services
• Major components each have an interface
– see com.mongodb.socialite.services
• Configuration selects implementation to use
• ServiceManager organizes :
– Default implementations
– Lifecycle
– Binding configuration
– Wiring dependencies
– see com.mongodb.socialite.ServiceManager

9. Simple Interface
https://github.com/10gen-labs/socialite
GET /users/{user_id} Get a User by their ID
DELETE /users/{user_id} Remove a user by their ID
POST /users/{user_id}/posts Send a message from this user
GET /users/{user_id}/followers Get a list of followers of a user
GET /users/{user_id}/followers_count Get the number of followers of a user
GET /users/{user_id}/following Get the list of users this user is following
GET /users/{user_id}/following count Get the number of users this user follows
GET /users/{user_id}/posts Get the messages sent by a user
GET /users/{user_id}/timeline Get the timeline for this user
PUT /users/{user_id} Create a new user
PUT /users/{user_id}/following/{target} Follow a user
DELETE /users/{user_id}/following/{target} Unfollow a user

10. Technical Decisions
User
timeline
cache
Schema
Indexing Horizontal Scaling

11. Operational Testing
Real life validation of our choices.
Most important criteria?
User facing latency
Linear scaling of resources

12. Scaling Goals
• Realistic real-life-scale workload
– compared to Twitter, etc.
• Understanding of HW required
– containing costs
• Confirm architecture scales linearly
– without loss of responsiveness

13. Architecture
Content Proxy
Graph Service Proxy

14. Operational Testing
• All hosts in AWS
• Each service used its own DB, cluster or shards
• All benchmarks through `mongos` (sharded config)
• Used MMS monitoring for measuring throughput
• Used internal benchmarks for measuring latency
• Based volume tested on real life social metrics

15. Scaling for Infinite Content

16. Architecture
Content Proxy
Graph Service Proxy

17. Socialite Content Service
• System of record for all user content
• Initially very simple (no search)
• Mainly designed to support feed
– Lookup/indexed by _id and userid
– Time based anchors/pagination

18. Social Data Ages Fast
• Half life of most content is 1 day !
• Popular content usually < 1 month
• Access to old data is rare

19. Content Service
• Index by userId, _id
• Shard by userId (or userId, _id)
• Supports “user data” as pass-through
{
"_id" : ObjectId("52aaaa14a0ee0d44323e623a"),
"_a" : "user1",
"_m" : "this is a post”,
"_d" : {
"geohash" : "6gkzwgjzn820"
}
}

20. Benchmarks

21. Architecture
Content Proxy
Graph Service Proxy

22. Graph Data - Social
John Kate
follows
Bob
Pete
Recommendation ?

23. Graph Data - Promotional
John Kate
follows
Bob
Pete
Mention
Acme
Soda
Recommendation ?

24. Graph Data - Everywhere
• Retail
• Complex product catalogues
• Product recommendation engines
• Manufacturing and Logistics
• Tracing failures to faulty component batches
• Determining fallout from supply interruption
• Healthcare
• Patient/Physician interactions

25. Design Considerations

26. The Tale of Two Biebers
VS

27. Follower Churn
• Tempting to focus on scaling content
• Follow requests rival message send rates
• Twitter enforces per day follow limits

28. Edge Metadata
• Models – friends/followers
• Requirements typically start simple
• Add Groups, Favorites, Relationships

29. Storing Graphs in MongoDB

30. Option One – Embedding Edges

31. Embedded Edge Arrays
• Storing connections with user (popular choice)
Most compact form
Efficient for reads
• However….
– User documents grow
– Upper limit on degree (document size)
– Difficult to annotate (and index) edge
{
"_id" : "djw",
"fullname" : "Darren Wood",
"country" : "Australia",
"followers" : [ "jsr", "ian"],
"following" : [ "jsr", "pete"]
}

32. Embedded Edge Arrays
• Creating Rich Graph Information
– Can become cumbersome
{
"_id" : "djw",
"fullname" : "Darren Wood",
"country" : "Australia",
"friends" : [
{"uid" : "jsr", "grp" : "school"},
{"uid" : "ian", "grp" : "work"} ]
}
{
"_id" : "djw",
"fullname" : "Darren Wood",
"country" : "Australia",
"friends" : [ "jsr", "ian"],
"group" : [ ”school", ”work"]
}

33. Option Two – Edge Collection

34. Edge Collections
• Document per edge
> db.followers.findOne()
{
"_id" : ObjectId(…),
"from" : "djw",
"to" : "jsr"
}
• Very flexible for adding edge data
> db.friends.findOne()
{
"_id" : ObjectId(…),
"from" : "djw",
"to" : "jsr",
"grp" : "work",
"ts" : Date("2013-07-10")
}

35. Operational comparison
• Updates of embedded arrays
– grow non-linearly with number of indexed array elements
• Updating edge collection => inserts
– grows close to linearly with existing number of edges/user

36. Edge Insert Rate

37. Edge Collection
Indexing Strategies

38. Finding Followers
Consider our single follower collection :
> db.followers.find({from : "djw"}, {_id:0, to:1})
{
"to" : "jsr"
}
Using index :
{
"v" : 1,
"key" : { "from" : 1, "to" : 1 },
"unique" : true,
"ns" : "socialite.followers",
"name" : "from_1_to_1"
}
Covered index
when searching on
"from" for all
followers
Specify only if
multiple edges
cannot exist

39. Finding Following
What about who a user is following?
Can use a reverse covered index :
{
"v" : 1,
"key" : { "from" : 1, "to" : 1 },
"unique" : true,
"ns" : "socialite.followers",
"name" : "from_1_to_1"
}
{
"v" : 1,
"key" : { "to" : 1, "from" : 1 },
"unique" : true,
"ns" : "socialite.followers",
"name" : "to_1_from_1"
}
Notice the flipped
field order here

40. Finding Following
Wait ! There is an issue with the reverse index…..
SHARDING !
{
"v" : 1,
"key" : { "from" : 1, "to" : 1 },
"unique" : true,
"ns" : "socialite.followers",
"name" : "from_1_to_1"
}
{
"v" : 1,
"key" : { "to" : 1, "from" : 1 },
"unique" : true,
"ns" : "socialite.followers",
"name" : "to_1_from_1"
}
If we shard this collection
by "from", looking up
followers for a specific
user is "targeted" to a
shard
To find who the user is
following however, it must
scatter-gather the query to
all shards

41. Dual Edge Collections

42. Dual Edge Collections
When "following" queries are common
– Not always the case
– Consider overhead carefully
Can use dual collections storing
– One for each direction
– Edges are duplicated reversed
– Can be sharded independently

43. Edge Query Rate Comparison
Number of shards
vs
Number of queries
Followers collection
with forward and
reverse indexes
Two collections,
followers, following
one index each
1 10,000 10,000
3 90,000 30,000
6 360,000 60,000
12 1,440,000 120,000

44. Architecture
Content Proxy
Graph Service Proxy

45. Feed Service
• Two main functions :
– Aggregating “followed” content for a user
– Forwarding user’s content to “followers”
• Common implementation models :
– Fanout on read
• Query content of all followed users on fly
– Fanout on write
• Add to “cache” of each user’s timeline for every post
• Various storage models for the timeline

46. Fanout On Read

47. Fanout On Read
Pros
Simple implementation
No extra storage for timelines
Cons
– Timeline reads (typically) hit all shards
– Often involves reading more data than required
– May require additional indexing on Content

48. Fanout On Write

49. Fanout On Write
Pros
Timeline can be single document read
Dormant users easily excluded
Working set minimized
Cons
– Fanout for large follower lists can be expensive
– Additional storage for materialized timelines

50. Fanout On Write
• Three different approaches
– Time buckets
– Size buckets
– Cache
• Each has different pros & cons

51. Timeline Buckets - Time
Upsert to time range buckets for each user
> db.timed_buckets.find().pretty()
{
"_id" : {"_u" : "jsr", "_t" : 516935},
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"},
{"_id" : ObjectId("...dd2"), "_a" : "ian", "_m" : "message from ian"}
]
}
{
"_id" : {"_u" : "ian", "_t" : 516935},
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"}
]
}
{
"_id" : {"_u" : "jsr", "_t" : 516934 },
"_c" : [
{"_id" : ObjectId("...da7"), "_a" : "ian", "_m" : "earlier from ian"}
]
}

52. Timeline Buckets - Size
More complex, but more consistently sized
> db.sized_buckets.find().pretty()
{
"_id" : ObjectId("...122"),
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"},
{"_id" : ObjectId("...dd2"), "_a" : "ian", "_m" : "message from ian"},
{"_id" : ObjectId("...da7"), "_a" : "ian", "_m" : "earlier from ian"}
],
"_s" : 3,
"_u" : "jsr"
}
{
"_id" : ObjectId("...011"),
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"}
],
"_s" : 1,
"_u" : "ian"
}

53. Timeline - Cache
Store a limited cache, fall back to "fanout on read"
– Create single cache doc on demand with upsert
– Limit size of cache with $slice
– Timeout docs with TTL for inactive users
> db.timeline_cache.find().pretty()
{
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"},
{"_id" : ObjectId("...dd2"), "_a" : "ian", "_m" : "message from ian"},
{"_id" : ObjectId("...da7"), "_a" : "ian", "_m" : "earlier from ian"}
],
"_u" : "jsr"
}
{
"_c" : [
{"_id" : ObjectId("...dc1"), "_a" : "djw", "_m" : "message from daz"}
],
"_u" : "ian"
}

54. Embedding vs Linking Content
Embedded content for direct access
– Great when it is small, predictable in size
Link to content, store only metadata
– Read only desired content on demand
– Further stabilizes cache document sizes
> db.timeline_cache.findOne({”_id" : "jsr"})
{
"_c" : [
{"_id" : ObjectId("...dc1”)},
{"_id" : ObjectId("...dd2”)},
{"_id" : ObjectId("...da7”)}
],
”_id" : "jsr"
}

55. Socialite Feed Service
• Implemented four models as plugins
– FanoutOnRead
– FanoutOnWrite – Buckets (size)
– FanoutOnWrite – Buckets (time)
– FanoutOnWrite - Cache
• Switchable by config
• Store content by reference or value
• Benchmark-able back to back

56. Benchmark by feed type

57. Benchmarking the Feed
• Biggest challenge: scaling the feed
• High cost of "fanout on write"
• Popular user posts => # operations:
– Content collection insert: 1
– Timeline Cache: on average, 130+ cache document
updates
• SCATTER GATHER (slowest shard determines
latency)

58. Benchmarking the Feed
• Timeline is different from content!
– "It's a Cache"
IT CAN BE REBUILT!

59. Benchmarking the Feed
IT CAN BE REBUILT!

60. Benchmarking the Feed
• Results
– over two weeks
– ran load with one million users
– ran load with ten million users
– used avg send rate 1K/s; 2K/s; reads 10K-20k/s
– 22 AWS c3.2xlarge servers (7.5GB RAM)
– 18 across six shards (3 content, 3 user graph)
– 4 mongos and app machines
– 2 c2x4xlarge servers (30GB RAM)
– timeline feed cache (six shards)

61. Summary

62. Socialite
https://github.com/10gen-labs/socialite
• Real Working Implementation
– Implements All Components
– Configurable models and options
• Built-in benchmarking
• Questions?
– I will be at "Ask The Experts" this afternoon!
https://github.com/10gen-labs/socialite

63. Thank You!
https://github.com/10gen-labs/socialite