Building a Scalable Inbox System with MongoDB and Java
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Building a Scalable Inbox System with MongoDB and Java

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Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted......

Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted by time, popularity, or other parameter. A scalable inbox is a difficult problem to solve: for millions of users, varied data from many sources must be sorted and presented within milliseconds. Different strategies can be used: scatter-gather, fan-out writes, and so on. This session presents an actual application developed by 10gen in Java, using MongoDB. This application is open source and is intended to show the reference implementation of several strategies to tackle this common challenge. The presentation also introduces many MongoDB concepts.

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  • 1. Technical Account Manager Lead, MongoDB Inc @antoinegirbal Antoine Girbal JavaOne 2013 Building a scalable inbox system with MongoDB and Java
  • 2. Single Table En Agenda • Problem Overview • Schema and queries • Java Development • Design Options – Fan out on Read – Fan out on Write – Bucketed Fan out on Write – Cached Inbox • Discussion
  • 3. Problem Overview
  • 4. Let‟s get Social
  • 5. Sending Messages ?
  • 6. Reading my Inbox ?
  • 7. Schema and Queries
  • 8. Basic CRUD • Save your first document: > db.test.insert({firstName: "Antoine", lastName: "Girbal" } ) • Find the document: > db.test.find({firstName: "Antoine" } ) { _id: ObjectId("524495105889411fab0cdfa3"),firstName: "Antoine", lastName: "Girbal" } • Update the document: > db.test.update({_id: ObjectId("524495105889411fab0cdfa3")}, { x: 1, y: 2 } ) • Remove the document: > db.test.remove({_id: ObjectId("524495105889411fab0cdfa3")}) • No schema definition or other declaration, it's easy!
  • 9. The User Document { "_id": ObjectId("519c12d53004030e5a6316d2"), "address": { "streetAddress": "2600 Rafe Lane", "city": "Jackson", "state": "MS", "zip": 39201, "country": "US" }, "birthday": "IDODate("1980-12-26T00:00:00.000Z"), "company": "Parade of Shoes", "domain": "SanFranciscoAgency.com", "email": "AnthonyJDacosta@pookmail.com", "firstName": "Anthony", "gender": "male", "lastName": "Dacosta", "location": [ -90.183518, 32.368619 ], … }
  • 10. The User Collection The collection statistics: > db.users.stats() { "ns": "edges.users", "count": 1000000, // number of documents "size": 637864480, // size of all documents "avgObjSize": 637.86448, "storageSize": 845197312, "numExtents": 16, "nindexes": 2, "lastExtentSize": 227786752, "paddingFactor": 1.0000000000260925, // padding after documents "systemFlags": 1, "userFlags": 0, "totalIndexSize": 66070256, "indexSizes": { "_id_": 29212848, "uid_1": 36857408 }, "ok": 1 }
  • 11. Queries on Users Finding a user by email address… > db.users.find({ "email": "AnthonyJDacosta@pookmail.com" }).pretty() { "_id": ObjectId("519c12d53004030e5a6316d2"), … By default will use a slow table scan… > db.users.find({ "email": "AnthonyJDacosta@pookmail.com" } ).explain() { "cursor": "BasicCursor", "nscannedObjects": 1000000, // 1m objects scanned "nscanned": 1000000, … Use an index for fast performance… > db.users.ensureIndex({ "email": 1 } ) // does not do anything if index is there > db.users.find({ "email": "AnthonyJDacosta@pookmail.com" }).explain() { "cursor": "BtreeCursor email_1", // Btree, sweet! "nscannedObjects": 1, // document is found almost right away "nscanned": 1, …
  • 12. Users Relationships • Here the follower / followee relationships are of "many-to-many" type. It can be either stored as: 1. a list of followers in user 2. a list of followees in user 3. a relationship collection: "followees" 4. two relationship collections: "followees" and "followers". • Ideal solutions: – a few million users and a 1000 followee limit: Solution #2 – no boundaries and relative scaling: Solution #3 – no boundaries and max scaling: Solution #4
  • 13. Relationship Data Let's look at a sample document: > use edges switched to db edges > db.followees.findOne() { "_id": ObjectId(), "user": "17052001”, "followee": "31554261” } And the statistics: > db.followees.stats() { "ns": "edges.followees", "count": 1000000, "size": 64000048, "avgObjSize": 64.000048, "storageSize": 86310912, "numExtents": 10, "nindexes": 2, "lastExtentSize": 27869184, "paddingFactor": 1, "systemFlags": 1, "userFlags": 0, "totalIndexSize": 85561840, "indexSizes": { "_id_": 32458720, "user_1_followee_1": 53103120 }, "ok": 1 }
  • 14. Relationship Queries To find all the users that a user follows: > db.followees.ensureIndex({ user: 1, followee: 1 }) // why not just index on user? We shall see > db.followees.find({user: "11622712"}) { "_id" : ObjectId("51641c02e4b0ef6827a34569"), "user" : "11622712", "followee" : "30432718" } … > db.followees.find({user: "11622712"}).explain() { "cursor" : "BtreeCursor user_1_followee_1", "n" : 66, "indexOnly" : false, "millis" : 0, // this is fast Even faster if using a “covered” index: > db.followees.find({user: "11622712"}, {followee: 1, _id: 0}).explain() { "cursor" : "BtreeCursor user_1_followee_1", "n" : 66, "nscannedObjects" : 0, "nscanned" : 66, "indexOnly" : true, // this means covered To find all the followers of a user, we just need the opposite index:: > db.followees.ensureIndex({followee: 1, user: 1}) > db.followees.find({followee: "30313973"}, {user: 1, _id: 0})
  • 15. Message Document The message document: > db.messages.findOne() { "_id": "ObjectId("519d4858e4b079162fe7eb12"), "uid": "48268973", // the author id "username": "Abiall", // why store the username? "text": "Lorem ipsum dolor sit amet, consectetur ...", "created": ISODate(2013-05-22T22:36:08.663Z"), "location": [ -95.470188, 37.366044 ], "tags": [ "gadgets" ] } Collection statistics: > db.messages.stats() { "ns": "msg.messages", "count": 21440518, "size": 14184598000, "avgObjSize": 661.5790719235422, "storageSize": 15749418944, "numExtents": 27, "nindexes": 2, "lastExtentSize": 2146426864, "paddingFactor": 1, "systemFlags": 1, "userFlags": 0, "totalIndexSize": 1454289648, "indexSizes": { "_id_": 695646784, "uid_1_created_1": 758642864 }, "ok": 1 }
  • 16. Implementing the Outbox The query is on "uid" and needs to be sorted by descending "created" time: > db.messages.ensureIndex({ "uid": 1, "created": 1 } ) // use a compound index > db.messages.find({ "uid": "31837072" } ).sort({ "created": -1 } ).limit(100) { "_id": ObjectId("519d626ae4b07916312e15b1") }, "uid": "31837072", "username": "Roya "text": "Lorem ipsum dolor sit amet, consectetur adipisicing elit , sed do eiusmod tempor …", "created": ISODate("2013-05-23T00:27:22.369Z"), "location": [ "-118.296138", "33.772832" ], "tags": [ "Art" ] } … > db.messages.find({ "uid": "31837072" }).sort({ "created": -1 }).limit(100).explain() { "cursor": "BtreeCursor uid_1_created_1 reverse", "n": 18, "nscannedObjects": 18, "nscanned": 18, "scanAndOrder": false, "millis": 0 …
  • 17. Java Development
  • 18. Java support • Java driver is open source, available on github and Maven. • mongo.jar is the driver, bson.jar is a subset with BSON library only. • Java driver is probably the most used MongoDB driver • It receives active development by MongoDB Inc and the community
  • 19. Driver Features • CRUD • Support for replica sets • Connection pooling • Distributed reads to slave servers • BSON serializer/deserializer (lazy option) • JSON serializer/deserializer • GridFS
  • 20. Message Store public class MessageStoreDAO implements MessageStore { private Morphia morphia; private Datastore ds; public MessageStoreDAO( MongoClient mongo ) { this.morphia = new Morphia(); this.morphia.map(DBMessage.class); this.ds = morphia.createDatastore(mongo, "messages"); this.ds.getCollection(DBMessage.class). ensureIndex(new BasicDBObject("sender",1).append("sentAt",1) ); } // get a message public Message get(String user_id, String msg_id) { return (Message) this.ds.find(DBMessage.class) .filter("sender", user_id) .filter("_id", new ObjectId(msg_id)) .get(); }
  • 21. Message Store // save a message public Message save(String user_id, String message, Date date) { Message msg = new DBMessage( user_id, message, date ); ds.save( msg ); return msg; } // find message by author sorted by descending time public List<Message> sentBy(String user_id) { return (List) this.ds.find(DBMessage.class) .filter("sender",user_id).order("-sentAt").limit(50).asList(); } // find message by several authors sorted by descending time public List<Message> sentBy(List<String> user_ids) { return (List) this.ds.find(DBMessage.class) .field("sender").in(user_ids).order("-sentAt").limit(50).asList(); }
  • 22. Graph Store Below uses Solution #4: both a follower and followee list public class GraphStoreDAO implements GraphStore { private DBCollection friends; private DBCollection followers; public GraphStoreDAO(MongoClient mongo) { this.followers = mongo.getDB("edges").getCollection("followers"); this.friends = mongo.getDB("edges").getCollection("friends"); followers.ensureIndex( new BasicDBObject("u",1).append("o",1), new BasicDBObject("unique", true)); friends.ensureIndex( new BasicDBObject("u",1).append("o",1), new BasicDBObject("unique",true)); } // find users that are followed public List<String> friendsOf(String user_id) { List<String> theFriends = new ArrayList<String>(); DBCursor cursor = friends.find( new BasicDBObject("u",user_id), new BasicDBObject("_id",0).append("o",1)); while(cursor.hasNext()) theFriends.add( (String) cursor.next().get("o")); return theFriends; }
  • 23. Graph Store // find followers of a user public List<String> followersOf(String user_id) { List<String> theFollowers = new ArrayList<String>(); DBCursor cursor = followers.find( new BasicDBObject("u",user_id), new BasicDBObject("_id",0).append("o",1)); while(cursor.hasNext()) theFollowers.add( (String) cursor.next().get("o")); return theFollowers; } public void follow(String user_id, String toFollow) { friends.save( new BasicDBObject("u",user_id).append("o",toFollow)); followers.save( new BasicDBObject("u",toFollow).append("o",user_id)); } public void unfollow(String user_id, String toUnFollow) { friends.remove(new BasicDBObject("u", user_id).append("o", toUnFollow)); followers.remove(new BasicDBObject("u", toUnFollow).append("o", user_id)); }
  • 24. Design Options
  • 25. 4 Approaches (there are more) • Fan out on Read • Fan out on Write • Bucketed Fan out on Write • Inbox Caches
  • 26. Fan out on read • Generally, not the right approach • 1 document per message sent • Reading an inbox is finding all messages sent by the list of people users follow • Requires scatter-gather on sharded cluster • Then a lot of random IO on a shard to find everything
  • 27. Fan out on Read Put the followees ids in a list: > var fees = [] > db.followees.find({user: "11622712"}) .forEach( function(doc) { fees.push( doc.followee ) } ) Use $in and sort() and limit() to gather the inbox: > db.messages.find({ uid: { $in: fees } }).sort({ created: -1 }).limit(100) { "_id": ObjectId("519d627ce4b07916312f0a09"), "uid": "34660390", "username": "Dingdowas" { "_id": ObjectId("519d627ce4b07916312f0a10"), "uid": "34661390", "username": "John" } … { "_id": ObjectId("519d627ce4b07916312f0a11"), "uid": "34662390", "username": "Brenda" } … …
  • 28. Fan out on read – Send Message Shard 1 Shard 2 Shard 3 Send Message
  • 29. Fan out on read – Inbox Read Shard 1 Shard 2 Shard 3 Read Inbox
  • 30. Fan out on read > db.messages.find({ uid: { $in: fees } } ).sort({ created: -1 } ).limit(100).explain() { "cursor": "BtreeCursor uid_1_created_1 multi", "isMultiKey": false, "n": 100, "nscannedObjects": 1319, "nscanned": 1384, "nscannedObjectsAllPlans": 1425, "nscannedAllPlans": 1490, "scanAndOrder": true, // it is sorting in RAM?? "indexOnly": false, "nYields": 0, "nChunkSkips": 0, "millis": 31 // takes about 30ms }
  • 31. Fan out on read - sort
  • 32. Fan out on write • Tends to scale better than fan out on read • 1 document per recipient • Reading my inbox is just finding all of the messages with me as the recipient • Can shard on recipient, so inbox reads hit one shard • But still lots of random IO on the shard
  • 33. Fan out on Write // Shard on “recipient” and “sent” db.shardCollection(”myapp.inbox”, { ”recipient”: 1, ”sent”: 1 } ) msg = { from: "Joe”, sent: new Date(), message: ”Hi!” } // Send a message, write one message per follower for( follower in followersOf( msg.from) ) { msg.recipient = recipient db.inbox.save(msg); } // Read my inbox, super easy db.inbox.find({ recipient: ”Joe” }).sort({ sent: -1 })
  • 34. Fan out on write – Send Message Shard 1 Shard 2 Shard 3 Send Message
  • 35. Fan out on write– Read Inbox Shard 1 Shard 2 Shard 3 Read Inbox
  • 36. Bucketed Fan out on write • Each “inbox” document is an array of messages • Append a message onto “inbox” of recipient • Bucket inbox documents so there‟s not too many per document • Can shard on recipient, so inbox reads hit one shard • 1 or 2 documents to read the whole inbox
  • 37. Bucketed Fan out on Write // Shard on “owner / sequence” db.shardCollection(”myapp.buckets”, { ”owner”: 1, ”sequence”: 1 } ) db.shardCollection(”myapp.users”, { ”user_name”: 1 } ) msg = { from: "Joe”, sent: new Date(), message: ”Hi!” } // Send a message, have to find the right sequence document for( follower in followersOf( msg.from) ) { sequence = db.users.findAndModify({ query: { user_name: recipient}, update: { '$inc': { ‟msg_count': 1 }}, upsert: true, new: true }).msg_count / 50; db.buckets.update({ owner: recipient, sequence: sequence}, { $push: { „messages‟: msg } }, { upsert: true }); } // Read my inbox db.buckets.find({ owner: ”Joe” }).sort({ sequence: -1 }).limit(2)
  • 38. Bucketed fan out on write - Send Shard 1 Shard 2 Shard 3 Send Message
  • 39. Bucketed fan out on write - Read Shard 1 Shard 2 Shard 3 Read Inbox
  • 40. Cached inbox • Recent messages are fast, but older messages are slower • Store a cache of last N messages per user • Used capped array to age out older messages • Create cache lazily when user accesses inbox • Only write the message if cache exists. • Use TTL collection to time out caches for inactive users
  • 41. Cached Inbox // Shard on “owner" db.shardCollection(”myapp.caches”, { ”owner”: 1 } ) // Send a message, add it to the existing caches of followers for( follower in followersOf( msg.from) ) { db.caches.update({ owner: recipient }, { $push: { messages: { $each: [ msg ], $sort: { „sent‟: 1 }, $slice: -50 } } } ); // Read my inbox If( msgs = db.caches.find({ owner: ”Joe” }) ) { // cache document exists return msgs; } else { // fall back to "fan out on read" and cache it db.caches.save({owner:‟joe‟, messages:[]}); msgs = db.outbox.find({sender: { $in: [ followersOf( msg.from ) ] }}).sort({sent:-1}).limit(50); db.caches.update({user:‟joe‟}, {$push: msgs }); }
  • 42. Cached Inbox – Send Shard 1 Shard 2 Shard 3 Send Message
  • 43. Cached Inbox- Read Shard 1 Shard 2 Shard 3 Read Inbox 1 2 Cache Hit Cache Miss
  • 44. Discussion
  • 45. Tradeoffs Fan out on Read Fan out on Write Bucketed Fan out on Write Inbox Cache Send Message Performance Best Single shard Single write Good Shard per recipient Multiple writes Worst Shard per recipient Appends (grows) Mixed Depends on how many users are in cache Read Inbox Performance Worst Broadcast all shards Random reads Good Single shard Random reads Best Single shard Single read Mixed Recent messages fast Older messages are slow Data Size Best Message stored once Worst Copy per recipient Worst Copy per recipient Good Same as FoR + size of cache
  • 46. Things to consider • Lots of recipients • Fan out on write might become prohibitive • Consider introducing a “Group” • Make fan out asynchronous • Very large message size • Multiple copies of messages can be a burden • Consider single copy of message with a “pointer” per inbox • More writes than reads • Fan out on read might be okay
  • 47. Summary • Multiple ways to model status updates • Think about characteristics of your network – Number of users – Number of edges – Publish frequency – Access patterns • Try to minimize random IO
  • 48. Technical Account Manager Lead, MongoDB Inc Antoine Girbal JavaOne 2013 Thank You