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In this session, we'll examine schema design insights and trade-offs using real world examples. We'll look at three example applications: building an email inbox, selecting a shard key for a large scale web application, and using MongoDB to store user profiles. From these examples you should leave the session with an idea of the advantages and disadvantages of various approaches to modeling your data in MongoDB. Attendees should be well versed in basic schema design and familiar with concepts in the morning's basic schema design talk. No beginner topics will be covered in this session.
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Technical Director, 10gen
@jonnyeight [email protected] alvinonmongodb.com
Alvin Richards
#MongoDBdays
Schema Design 4 Real World Use Cases
One size fits all?
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 • RBMS – "What answers do I have?" • MongoDB – "What question will I have?"
#1 - Message Inbox
Let’s get Social
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 reads db.inbox.ensureIndex( { to: 1, sent: 1 } ) msg = { from: "Joe", to: [ "Bob", "Jane" ],
sent: new Date(), message: "Hi!",
} // Send a message db.inbox.save( msg ) // Read my inbox db.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
• 1 document per message sent
• Multiple recipients in an array key
• Reading an inbox is 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 message for ( recipient in msg.to ) {
msg.recipient = recipient db.inbox.save( msg );
} // Read my inbox db.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
• 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 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
// 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 message for( 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 inbox db.inbox.find( { owner: "Joe" } ).sort ( { sequence: -1 } ).limit( 2 )
Fan out on write – with buckets
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
#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 range db.inbox.find ( { owner: "friend1", messages: { $elemMatch: { sent: { $gte: ISODate("2013-04-04…") }}}}) // Remove elements based on a date db.inbox.update( { owner: "friend1" }, { $pull: { messages: { sent: { $gte: ISODate("2013-04-04…") } } } } )
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 $push db.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 year db.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
• Attributes as Objects in an Array
db.files.insert( { _id: "local.0", attr: { type: "text", size: 64, created: ISODate("2013-03-01T09:59:42.689Z" } } )
db.files.insert( { _id:"local.1", attr: { type: "text", size: 128} } )
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-document db.files.ensureIndex( { "attr.type": 1 } ) db.files.find( { "attr.type": "text"} )
// Can perform range queries db.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("2013-03-01T09:59:42.689Z" } ] } )
db.files.insert( { _id: "local.1", attr: [ { type: "text" }, { size: 128 } ] } )
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 queries db.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 index db.files.find( { $or: [ { attr: { $gte: { created: ISODate("2013-02-01T…") } } }, { attr: { $gt: { size:128 }, $lte: { size: 16384 } } } ] } )
Queries
#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: "[email protected], fb: "joe.smith", // facebook li: "joe.e.smith", // linkedin other: {…} }
// Shard collection by _id db.shardCollection("mongodbdays.users", { _id: 1 } )
// Create indexes on each key db.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"} )
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 index db.identities.ensureIndex( { identifier : 1} , { unique: true} ) // Create a document for each users document db.identities.save( { identifier : { hndl: "joe" }, user: "1200-42" } ) db.identities.save( { identifier : { email: "[email protected]" }, user: "1200-42" } ) db.identities.save( { identifier : { li: "joe.e.smith" }, user: "1200-42" } ) // Shard collection by _id db.shardCollection( "mongodbdays.identities", { identifier : 1 } )
// Create unique index db.users.ensureIndex( { _id: 1} , { unique: true} )
// Create a docuemnt that holds all the other user attributes db.users.save( { _id: "1200-42", ... } )
// Shard collection by _id db.shardCollection( "mongodbdays.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"} )
Solution
• 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
Technical Director, 10gen
@jonnyeight [email protected] alvinonmongodb.com
Alvin Richards
#MongoDBdays
Thank You