This document discusses how Eventbrite built a social network using MongoDB to power social recommendations on their site. It describes how user, event, and order data is stored in MongoDB collections to enable querying neighbors and recommended events. Challenges around the dynamic and flexible nature of the social graph and performance needs are also outlined. The system provides recommendations by fetching a user's neighbors, their events, and scoring each possible event.

1. Building a Social Network with MongoDB
Brian Zambrano
MongoSV
December 3, 2010
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2. Eventbrite Brand Tenets
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3. Eventbrite Brand Tenets
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4. Social Recommendations
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5. Eventbriteʼs Social Graph
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6. Eventbriteʼs Social Graph
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7. Neighbors
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8. Challenges
• Dynamic
• Neighbors change often
• Neighborsʼ events change often
• Flexibility
• Want to incorporate other social graphs
• Product may evolve quickly
• Performance
• We need really fast reads
• Frequent writes
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9. Why MongoDB?
• Performance
• Flexible schema design
• Easy to work with
• We felt comfortable MongoDB would mature as
our needs became more demanding
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10. Providing Recommendations
1. User visits http://eventbrite.com/mytickets/
2. Fetch neighbors
3. Fetch neighborsʼ events
4. Score each possible event
5. Return recommendations
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11. MongoDB setup
• One non-sharded replica set
• Two DBs on Large EC2 instances
• One arbiter
• Three collections
• Users
• Events
• Orders
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12. User Data in MongoDB
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{ "_id": 4558992,
}
Unique User Id
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13. User Data in MongoDB
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{ "_id": 4558992,
"events" : {
"all_ids": [ 116706, 179487, 16389, 827496 ],
"curr_ids": [ 827496 ],
},
}
Past and current
attendance
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14. User Data in MongoDB
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{ "_id": 4558992,
"events" : {
"all_ids": [ 116706, 179487, 16389, 827496 ],
"curr_ids": [ 827496 ],
},
"nns" : [
[ 2816442, 0.2 ],
[ 1615962, 0.047619047619047616 ],
],
} Nearest neighbors
(user_id, score)
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15. User Data in MongoDB
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{ "_id": 4558992,
"events" : {
"all_ids": [ 116706, 179487, 16389, 827496 ],
"curr_ids": [ 827496 ],
},
"nns" : [
[ 2816442, 0.2 ],
[ 1615962, 0.047619047619047616 ],
],
"fb" : {
"_id" : 4808871,
"name" : "Brian Zambrano",
"location" : "San Francisco, California",
"friends" : [ 568876525, 569507467, 569559792 ],
},
}
Facebook data
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16. MongoDB Indexes
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{ "_id": 4558992,
"events" : {
"all_ids": [ 116706, 179487, 16389, 827496 ],
"curr_ids": [ 827496 ],
},
"nns" : [
[ 2816442, 0.2 ],
[ 1615962, 0.047619047619047616 ],
],
"fb" : {
"_id" : 4808871,
"name" : "Brian Zambrano",
"location" : "San Francisco, California",
"friends" : [ 568876525, 569507467, 569559792],
},
}
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17. Events Collection
> db.events.findOne({_id: 799177})
{
"_id" : 799177,
"uid" : 2989008,
"title" : "MongoSV",
"venue" : {
"loc" : [
37.413042,
-122.071106
],
"state" : "CA",
"id" : 508093,
"city" : "Mountain View"
},
"logo" : "758915938.png",
"shortname" : "mongosv",
"start_date" : "Fri Dec 03 2010 01:00:00 GMT-0800 (PST)"
}
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18. Orders Collection
> db.orders.find({_eid: 799177})
{ "_id" : 17464215, "_uid" : 1111195, "_eid" : 799177 }
{ "_id" : 17575729, "_uid" : 6970539, "_eid" : 799177 }
{ "_id" : 17582343, "_uid" : 3092687, "_eid" : 799177 }
{ "_id" : 17588693, "_uid" : 2255017, "_eid" : 799177 }
{ "_id" : 17589589, "_uid" : 6976917, "_eid" : 799177 }
{ "_id" : 17601979, "_uid" : 885441, "_eid" : 799177 }
{ "_id" : 17603085, "_uid" : 2500199, "_eid" : 799177 }
{ "_id" : 17608289, "_uid" : 6984367, "_eid" : 799177 }
{ "_id" : 17681965, "_uid" : 628459, "_eid" : 799177 }
{ "_id" : 17684489, "_uid" : 7017999, "_eid" : 799177 }
{ "_id" : 17689673, "_uid" : 7020133, "_eid" : 799177 }
{ "_id" : 17728267, "_uid" : 7036607, "_eid" : 799177 }
has more
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19. Recommended Events Query
Two + n queries
1. Get neighbors
nns = db.users.find({_id : {$in : user.nn_ids}})
2. Get possible event recommendations:
db.events.find({_id : {$in : nns.events.all}})
n.For each event, get total attendee count
db.orders.find({_eid : event_id})
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20. Recommended Events Query
Two + n queries
1. Get neighbors
nns = db.users.find({_id : {$in : user.nn_ids}})
2. Get possible event recommendations:
db.events.find({_id : {$in : nns.events.all}})
n.For each event, get total attendee count
db.orders.find({_eid : event_id})
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Optimization opportunity:
Embed orders in Event records
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21. Updating Neighbors
Two queries, one update
1. Get all orders for a userʼs past events:
uids = db.orders.find({_id : {$in : user.events.all}})
2. Get all neighbors:
nns = db.users.find({_id : {$in : uids}})
➡Score neighbors
3. Update nn_ids
db.users.update({_id : uid},
{$set : {nn_ids: nn}})
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22. Facebook Friendʼs Events
Two queries
1. Get FB friends
db.users.find({fb._id : {$in : fb.friends}})
2. Get events FB friends are attending
db.events.find({_id : {$in : fb_friends_events}})
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23. The Future
• Incorporate other social networks
• Iterate scoring algorithm
• Count recommendation impressions
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24. Weʼre hiring!
http://www.eventbrite.com/jobs/
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25. Thanks!
Brian Zambrano <brianz@eventbrite.com>
Eventbriteʼs new Facebook recommendations power
social event discovery: http://bit.ly/gRVS7I
Social Commerce: A First Look at the Numbers:
http://bit.ly/gXeg9Q
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