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Building a Social Network with MongoDBBrian Zambrano
MongoSVDecember 3, 2010
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Friday, December 3, 2010
Eventbrite Brand Tenets
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Friday, December 3, 2010
Eventbrite Brand Tenets
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Social Recommendations
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Eventbriteʼs Social Graph
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Eventbriteʼs Social Graph
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Friday, December 3, 2010
Neighbors
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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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Friday, December 3, 2010
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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Friday, December 3, 2010
Providing Recommendations
1. User visits http://eventbrite.com/mytickets/2. Fetch neighbors3. Fetch neighborsʼ events4. Score each possible event5. Return recommendations
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MongoDB setup
• One non-sharded replica set• Two DBs on Large EC2 instances• One arbiter
• Three collections• Users• Events• Orders
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User Data in MongoDB
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{ "_id": 4558992,}
Unique User Id
Friday, December 3, 2010
User Data in MongoDB
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{ "_id": 4558992, "events" : { "all_ids": [ 116706, 179487, 16389, 827496 ], "curr_ids": [ 827496 ], },}
Past and currentattendance
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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)
Friday, December 3, 2010
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
Friday, December 3, 2010
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], },}
Friday, December 3, 2010
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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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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Recommended Events Query
Two + n queries1. 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 countdb.orders.find({_eid : event_id})
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Friday, December 3, 2010
Recommended Events Query
Two + n queries1. 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 countdb.orders.find({_eid : event_id})
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Optimization opportunity: Embed orders in Event records
Friday, December 3, 2010
Updating Neighbors
Two queries, one update1. 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 neighbors3. Update nn_ids
db.users.update({_id : uid}, {$set : {nn_ids: nn}})
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Facebook Friendʼs Events
Two queries1. Get FB friends
db.users.find({fb._id : {$in : fb.friends}})
2. Get events FB friends are attendingdb.events.find({_id : {$in : fb_friends_events}})
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The Future
• Incorporate other social networks• Iterate scoring algorithm• Count recommendation impressions
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Friday, December 3, 2010
Weʼre hiring!
http://www.eventbrite.com/jobs/
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Thanks!
Brian Zambrano <[email protected]>
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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