Perl Engineer & Evangelist, 10gen

Mike Friedman

#MongoDBdays

Schema DesignFour Real-World Use Cases

Single Table En

Agenda

• Why is schema design important

• 4 Real World Schemas– Inbox– History– Indexed Attributes– Multiple Identities

• Conclusions

Why is Schema Design important?

• Largest factor for a performant system

• Schema design with MongoDB is different

• RDBMS – "What answers do I have?"• MongoDB – "What question will I have?"

#1 - Message Inbox

Let’s getSocial

Sending Messages

?

Design Goals

• Efficiently send new messages to recipients

• Efficiently read inbox

Reading my Inbox

?

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

• Fan out on Write

• Fan out on Write with Bucketing

// Shard on "from"db.shardCollection( "mongodbdays.inbox", { from: 1 } )

// Make sure we have an index to handle inbox readsdb.inbox.ensureIndex( { to: 1, sent: 1 } )

msg = {from: "Joe",to: [ "Bob", "Jane" ],

sent: new Date(), message: "Hi!",

}

// Send a messagedb.inbox.save( msg )

// Read my inboxdb.inbox.find( { to: "Joe" } ).sort( { sent: -1 } )

Fan out on read

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

Considerations

• One document per message sent

• Reading an inbox means finding all messages with my own name in the recipient field

• Requires scatter-gather on sharded cluster

• Then a lot of random IO on a shard to find everything

// Shard on “recipient” and “sent” db.shardCollection( "mongodbdays.inbox", { ”recipient”: 1, ”sent”: 1 } )

msg = {from: "Joe",to: [ "Bob", "Jane" ],

sent: new Date(), message: "Hi!",

}

// Send a messagefor ( recipient in msg.to ) {

msg.recipient = msg.to[recipient]db.inbox.save( msg );

}

// Read my inboxdb.inbox.find( { recipient: "Joe" } ).sort( { sent: -1 } )

Fan out on write

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

Considerations

• One 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

// Shard on “owner / sequence”db.shardCollection( "mongodbdays.inbox", { owner: 1, sequence: 1 } )db.shardCollection( "mongodbdays.users", { user_name: 1 } )

msg = {from: "Joe",to: [ "Bob", "Jane" ],

sent: new Date(), message: "Hi!",

}

Fan out on write with buckets

// Send a messagefor( recipient in msg.to) { count = db.users.findAndModify({

query: { user_name: msg.to[recipient] }, update: { "$inc": { "msg_count": 1 } }, upsert: true, new: true }).msg_count;

sequence = Math.floor(count / 50);

db.inbox.update({ owner: msg.to[recipient], sequence: sequence }, { $push: { "messages": msg } },{ upsert: true } );

}

// Read my inboxdb.inbox.find( { owner: "Joe" } ).sort ( { sequence: -1 } ).limit( 2 )

Fan out on write with buckets

Fan out on write with buckets• Each “inbox” document is an array of

messages

• Append a message onto “inbox” of recipient

• Bucket inboxes so there’s not too many messages per document

• Can shard on recipient, so inbox reads hit one shard

• 1 or 2 documents to read the whole inbox

Fan out on write with buckets - Send

Shard 1 Shard 2 Shard 3

Send Message

Fan out on write with buckets - Read

Shard 1 Shard 2 Shard 3

Read Inbox

#2 – History

Design Goals

• Need to retain a limited amount of history e.g.– Hours, Days, Weeks– May be legislative requirement (e.g. HIPPA, SOX,

DPA)

• Need to query efficiently by – match– ranges

3 Approaches (there are more)• Bucket by Number of messages

• Fixed size Array

• Bucket by Date + TTL Collections

db.inbox.find() { owner: "Joe", sequence: 25, messages: [ { from: "Joe", to: [ "Bob", "Jane" ], sent: ISODate("2013-03-01T09:59:42.689Z"), message: "Hi!" }, …] }

// Query with a date rangedb.inbox.find ({owner: "friend1", messages: { $elemMatch: {sent:{$gte: ISODate("…") }}}})

// Remove elements based on a datedb.inbox.update({owner: "friend1" }, { $pull: { messages: { sent: { $gte: ISODate("…") } } } } )

Inbox – Bucket by # messages

Considerations

• Shrinking documents, space can be reclaimed with– db.runCommand ( { compact: '<collection>' } )

• Removing the document after the last element in the array as been removed– { "_id" : …, "messages" : [ ], "owner" : "friend1", "sequence" : 0 }

msg = { from: "Your Boss", to: [ "Bob" ],

sent: new Date(), message: "CALL ME NOW!"

}

// 2.4 Introduces $each, $sort and $slice for $pushdb.messages.update(

{ _id: 1 }, { $push: { messages: { $each: [ msg ],

$sort: { sent: 1 },

$slice: -50 }

} })

Maintain the latest – Fixed Size Array

Considerations

• Need to compute the size of the array based on retention period

// messages: one doc per user per day

db.inbox.findOne(){

_id: 1, to: "Joe", sequence: ISODate("2013-02-04T00:00:00.392Z"), messages: [ ] }

// Auto expires data after 31536000 seconds = 1 yeardb.messages.ensureIndex( { sequence: 1 }, { expireAfterSeconds: 31536000 } )

TTL Collections

#3 – Indexed Attributes

Design Goal

• Application needs to stored a variable number of attributes e.g.– User defined Form– Meta Data tags

• Queries needed– Equality– Range based

• Need to be efficient, regardless of the number of attributes

2 Approaches (there are more)• Attributes as Embedded Document

• Attributes as Objects in an Array

db.files.insert( { _id: "local.0", attr: { type: "text", size: 64, created: ISODate("..." } } )

db.files.insert( { _id: "local.1", attr: { type: "text", size: 128} } )

db.files.insert( { _id: "mongod", attr: { type: "binary", size: 256, created: ISODate("...") } } )

// Need to create an index for each item in the sub-documentdb.files.ensureIndex( { "attr.type": 1 } )db.files.find( { "attr.type": "text"} )

// Can perform range queriesdb.files.ensureIndex( { "attr.size": 1 } )db.files.find( { "attr.size": { $gt: 64, $lte: 16384 } } )

Attributes as a Sub-Document

Considerations

• Each attribute needs an Index

• Each time you extend, you add an index

• Lots and lots of indexes

db.files.insert( {_id: "local.0", attr: [ { type: "text" },

{ size: 64 },

{ created: ISODate("...") } ] } )

db.files.insert( { _id: "local.1", attr: [ { type: "text" },

{ size: 128 } ] } )

db.files.insert( { _id: "mongod", attr: [ { type: "binary" },

{ size: 256 }, { created: ISODate("...") } ] } )

db.files.ensureIndex( { attr: 1 } )

Attributes as Objects in Array

Considerations

• Only one index needed on attr

• Can support range queries, etc.

• Index can be used only once per query

#4 – Multiple Identities

Design Goal

• Ability to look up by a number of different identities e.g.

• Username• Email address• FB Handle• LinkedIn URL

2 Approaches (there are more)• Identifiers in a single document

• Separate Identifiers from Content

db.users.findOne(){ _id: "joe", email: "joe@example.com, fb: "joe.smith", // facebook li: "joe.e.smith", // linkedin other: {…}}

// Shard collection by _iddb.shardCollection("mongodbdays.users", { _id: 1 } )

// Create indexes on each keydb.users.ensureIndex( { email: 1} )db.users.ensureIndex( { fb: 1 } )db.users.ensureIndex( { li: 1 } )

Single Document by User

Read by _id (shard key)

Shard 1 Shard 2 Shard 3

find( { _id: "joe"} )

Read by email (non-shard key)

Shard 1 Shard 2 Shard 3

find ( { email: joe@example.com } )

Considerations

• Lookup by shard key is routed to 1 shard

• Lookup by other identifier is scatter gathered across all shards

• Secondary keys cannot have a unique index

// Create unique indexdb.identities.ensureIndex( { identifier : 1} , { unique: true} )

// Create a document for each users documentdb.identities.save( { identifier : { hndl: "joe" }, user: "1200-42" } )db.identities.save( { identifier : { email: "joe@abc.com" }, user: "1200-42" } )db.identities.save( { identifier : { li: "joe.e.smith" }, user: "1200-42" } )

// Shard collection by _iddb.shardCollection( "mydb.identities", { identifier : 1 } )

// Create unique indexdb.users.ensureIndex( { _id: 1} , { unique: true} )

// Shard collection by _iddb.shardCollection( "mydb.users", { _id: 1 } )

Document per Identity

Read requires 2 reads

Shard 1 Shard 2 Shard 3

db.identities.find({"identifier" : { "hndl" : "joe" }})

db.users.find( { _id: "1200-42"} )

Considerations

• Lookup to Identities is a routed query

• Lookup to Users is a routed query

• Unique indexes available

Conclusion

Summary

• Multiple ways to model a domain problem

• Understand the key uses cases of your app

• Balance between ease of query vs. ease of write

• Random IO should be avoided

Perl Engineer & Evangelist, 10gen

Mike Friedman

#MongoDBdays

Thank You