Technical Account Manager Lead, MongoDB Inc

@antoinegirbal

Antoine Girbal

JavaOne 2013

Building a scalable inbox system with MongoDB and Java

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

Problem Overview

Let’s getSocial

Sending Messages

?

Reading my Inbox

?

Schema and Queries

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!

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 ],

…}

The User CollectionThe 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

}

Queries on UsersFinding 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, …

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 user2. a list of followees in user3. 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

Relationship DataLet's look at a sample document:

> use edgesswitched 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 }

Relationship QueriesTo 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})

Message DocumentThe 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 }

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": "Royague", "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…

Java Development

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

Driver Features

• CRUD

• Support for replica sets

• Connection pooling

• Distributed reads to slave servers

• BSON serializer/deserializer (lazy option)

• JSON serializer/deserializer

• GridFS

Message Storepublic 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 messagepublic 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(); }

Message Store// save a messagepublic 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 timepublic 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 timepublic List<Message> sentBy(List<String> user_ids) { return (List) this.ds.find(DBMessage.class) .field("sender").in(user_ids).order("-sentAt").limit(50).asList(); }

Graph StoreBelow 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 followedpublic 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; }

Graph Store// find followers of a userpublic 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));}

Design Options

4 Approaches (there are more)• Fan out on Read

• Fan out on Write

• Bucketed Fan out on Write

• Inbox Caches

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

Fan out on ReadPut 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" } ……

Fan out on read – Send Message

Shard 1 Shard 2 Shard 3

Send Message

Fan out on read – Inbox Read

Shard 1 Shard 2 Shard 3

Read Inbox

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

}

Fan out on read - sort

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

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 followerfor( follower in followersOf( msg.from) ) {

msg.recipient = recipientdb.inbox.save(msg);

}

// Read my inbox, super easydb.inbox.find({ recipient: ”Joe” }).sort({ sent: -1 })

Fan out on write – Send Message

Shard 1 Shard 2 Shard 3

Send Message

Fan out on write– Read Inbox

Shard 1 Shard 2 Shard 3

Read Inbox

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

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 documentfor( 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 inboxdb.buckets.find({ owner: ”Joe” }).sort({ sequence: -1 }).limit(2)

Bucketed fan out on write - Send

Shard 1 Shard 2 Shard 3

Send Message

Bucketed fan out on write - Read

Shard 1 Shard 2 Shard 3

Read Inbox

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

Cached Inbox// Shard on “owner"db.shardCollection(”myapp.caches”, { ”owner”: 1 } )

// Send a message, add it to the existing caches of followersfor( follower in followersOf( msg.from) ) {

db.caches.update({ owner: recipient }, { $push: { messages: {$each: [ msg ], $sort: { ‘sent’: 1 },$slice: -50 } } } );

// Read my inboxIf( msgs = db.caches.find({ owner: ”Joe” }) ) {

// cache document existsreturn msgs;

} else {// fall back to "fan out on read" and cache itdb.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 });

}

Cached Inbox – Send

Shard 1 Shard 2 Shard 3

Send Message

Cached Inbox- Read

Shard 1 Shard 2 Shard 3

Read Inbox

1

2

Cache Hit

Cache Miss

Discussion

TradeoffsFan out on Read

Fan out on Write

Bucketed Fan out on

Write

Inbox Cache

Send Message Performance

Best Single shardSingle write

GoodShard per recipientMultiple writes

WorstShard per recipientAppends (grows)

MixedDepends on how many users are in cache

Read Inbox Performance

WorstBroadcast all shardsRandom reads

GoodSingle shardRandom reads

Best Single shardSingle read

MixedRecent messages fastOlder messages are slow

Data Size Best Message stored once

WorstCopy per recipient

WorstCopy per recipient

GoodSame as FoR + size of cache

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

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

Technical Account Manager Lead, MongoDB Inc

Antoine Girbal

JavaOne 2013

Thank You