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
{ "_id": 4558992, Unique User Id
}
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13. User Data in MongoDB
{ "_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
{ "_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
{ "_id": 4558992,
"events" : {
"all_ids": [ 116706, 179487, 16389, 827496 ],
"curr_ids": [ 827496 ],
},
"nns" : [
[ 2816442, 0.2 ],
[ 1615962, 0.047619047619047616 ],
],
"fb" : {
"_id" : 4808871, Facebook data
"name" : "Brian Zambrano",
"location" : "San Francisco, California",
"friends" : [ 568876525, 569507467, 569559792 ],
},
}
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16. MongoDB Indexes
{ "_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})
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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