Technical Director, 10gen

@jonnyeight alvin@10gen.com alvinonmongodb.com

Alvin Richards

#MongoDBdays

Schema Design3 Real World Use Cases

I'm planning a Trip to LA…

Single Table En

Agenda

• Why is schema design important

• 3 Real World Schemas– Inbox– Indexed Attributes– Multiple Identities

• Conclusions

Why is Schema Design important?

• Largest factor for a performant system

• Schema design with MongoDB is different

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

• Must consider use case with schema

#1 - Message Inbox

Let’s getSocial

Sending Messages

?

Reading my Inbox

?

Design Goals

• Efficiently send new messages to recipients

• Efficiently read inbox

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

• Fan out on Write

• Fan out on Write with Bucketing

Fan out on read – Send Message

Shard 1 Shard 2 Shard 3

Send Message

db.inbox.save( { to: [ "Bob", "Jane" ], … } )

Fan out on read – Inbox Read

Shard 1 Shard 2 Shard 3

Read Inbox

db.inbox.find( { to: "Bob" } )

// 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: "Bob" } ).sort( { sent: -1 } )

Fan out on read

Considerations

1 document per message sent Multiple recipients in an array key Reading inbox finds 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

Fan out on write – Send Message

Shard 1 Shard 2 Shard 3

Send Message

db.inbox.save( { to: "Bob", …} )

Fan out on write– Read Inbox

Shard 1 Shard 2 Shard 3

Read Inbox

db.inbox.find( { to: "Bob" } )

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

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

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

}

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

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

}

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

Fan out on write

Considerations

✖1 document per recipient per messageReading inbox is finding all of the

messages with me as the recipientCan shard on recipient, so inbox reads hit

one shard

✖But still lots of random IO on the shard

Fan out on write with buckets• 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

• A few documents to read the whole inbox

Bucketed fan out on write - Send

Shard 1 Shard 2 Shard 3

Send Message

db.inbox.update( { to: "Bob"}, { $push: { msg: … } })

Bucketed fan out on write - Read

Shard 1 Shard 2 Shard 3

Read Inbox

db.inbox.find( { to: "Bob" } )

// 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!",

}// 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( { to: msg.to[recipient], sequence: sequence },

{ $push: { "messages": msg } },

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

Fan out on write – with buckets

Considerations

Fewer documents per recipientReading inbox is just finding a few bucketsCan shard on recipient, so inbox reads hit

one shard

✖But still some random IO on the shard

But…

• What if I do not / cannot retain all history?

– Space limited: Hours, Days, Weeks, $$$– Legislative limited: HIPPA, SOX, DPA

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

seen that

• Fixed size Array

• Bucket by Date + TTL Collections

// Query with a date rangedb.inbox.find ( { owner: "Joe", messages: { $elemMatch: { sent: { $gte: ISODate("2013-04-04…") }}}})

// Remove elements based on a datedb.inbox.update( { owner: "Joe" }, { $pull: { messages: { sent: { $gte: ISODate("2013-04-04…") } } } } )

Inbox – Bucket by # messages

Considerations

Limited to a known range of messages

✖Shrinking documents• space can be reclaimed withdb.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

Push this object onto the array

Sort the resulting array

by "sent"

Limit the array to 50 elements

Considerations

Limited to a known # of messages

✖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

Considerations

Limited to a known range of messages Automatic purge of expired data

No need to have a CRON task, etc. to do this

✖ Per Collection basis

#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

• Attributes as Objects in an Array

// Flexible set of attributes

db.files.insert( { _id:"mongod", attr: { type: "binary", size: 256, created: ISODate("2013-04-01T18:13:42.689Z") } } )

// 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

Considerations

Attributes can be queried via an IndexEquality & Range queries supported

✖Each attribute needs an Index

✖Each time you extend, you add an index

✖Single index is used (unless you have $or)

// Flexible set of attributes, each attribute is an object

db.files.insert( { _id: "mongod", attr: [ { type: "binary" }, { size: 256 }, { created: ISODate("2013-04-01T18:13:42.689Z") } ] } )

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

Attributes as Objects in Array

// Range queriesdb.files.find( { attr: { $gt: { size:64 }, $lte: { size: 16384 } } } )

db.files.find( { attr: { $gte: { created: ISODate("2013-02-01T00:00:01.689Z") } } } )

// Multiple condition – Only the first predicate on the query can use the Index// ensure that this is the most selective. // Index Intersection will allow multiple indexes, see SERVER-3071

db.files.find( { $and: [ { attr: { $gte: { created: ISODate("2013-02-01T…") } } }, { attr: { $gt: { size:128 }, $lte: { size: 16384 } } } ] } )

// Each $or can use an indexdb.files.find( { $or: [ { attr: { $gte: { created: ISODate("2013-02-01T…") } } }, { attr: { $gt: { size:128 }, $lte: { size: 16384 } } } ] } )

Queries

Considerations

Attributes can be queried via a Single index

New attributes do not need extra Indexes Equality & Range queries supported

✖ $and can only use a Single Index

#3 – 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)• Multiple 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 a document that holds all the other user attributesdb.users.save( { _id: "1200-42", ... } )

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

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

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

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

Document per Identity

Read requires 2 queries

Shard 1 Shard 2 Shard 3

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

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

Considerations

Multiple queries, but always routed 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

• Avoid Random IO

• Avoid Scatter / Gather query pattern

Technical Director, 10gen

@jonnyeight alvin@10gen.com alvinonmongodb.com

Alvin Richards

#MongoDBdays

Thank You