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MongoDB Schema Design: Four Real-World Examples
 

MongoDB Schema Design: Four Real-World Examples

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In this advanced schema design presentation, we examine four real-world scenarios and examine several possible solutions to each problem.

In this advanced schema design presentation, we examine four real-world scenarios and examine several possible solutions to each problem.

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    MongoDB Schema Design: Four Real-World Examples MongoDB Schema Design: Four Real-World Examples Presentation Transcript

    • Perl Engineer & Evangelist, 10genMike Friedman#MongoDBdaysSchema DesignFour Real-World UseCases
    • Single Table EnAgenda• Why is schema design important• 4 Real World Schemas– Inbox– History– IndexedAttributes– Multiple Identities• Conclusions
    • Why is Schema Designimportant?• 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 aremore)• 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 – SendMessageShard 1 Shard 2 Shard 3SendMessage
    • Fan out on read – Inbox ReadShard 1 Shard 2 Shard 3ReadInbox
    • Considerations• One document per message sent• Reading an inbox means finding all messageswith my own name in the recipient field• Requires scatter-gather on sharded cluster• Then a lot of random IO on a shard to findeverything
    • // 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 – SendMessageShard 1 Shard 2 Shard 3SendMessage
    • Fan out on write– Read InboxShard 1 Shard 2 Shard 3ReadInbox
    • Considerations• One document per recipient• Reading my inbox is just finding all of themessages with me as the recipient• Can shard on recipient, so inbox reads hit oneshard• 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 },{ $push: { "messages": msg } },{ upsert: true } );}// Read my inboxdb.inbox.find( { owner: "Joe" } ).sort ( { sequence: -1 } ).limit( 2 )Fan out on write with buckets
    • Fan out on write with buckets• Each “inbox” document is an array of messages• Append a message onto “inbox” of recipient• Bucket inboxes so there’s not too manymessages per document• Can shard on recipient, so inbox reads hit oneshard• 1 or 2 documents to read the whole inbox
    • Fan out on write with buckets -SendShard 1 Shard 2 Shard 3SendMessage
    • Fan out on write with buckets -ReadShard 1 Shard 2 Shard 3ReadInbox
    • #2 – History
    • Design Goals• Need to retain a limited amount of history e.g.– Hours, Days, Weeks– May be legislative requirement (e.g. HIPPA, SOX, DPA)• Need to query efficiently by– match– ranges
    • 3 Approaches (there aremore)• Bucket by Number of messages• Fixed size Array• Bucket by Date + TTL Collections
    • db.inbox.find(){ owner: "Joe", sequence: 25,messages: [{ from: "Joe",to: [ "Bob", "Jane" ],sent: ISODate("2013-03-01T09:59:42.689Z"),message: "Hi!"},…] }// Query with a date rangedb.inbox.find ({owner: "friend1",messages: {$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 reclaimedwith– db.runCommand ( { compact: <collection> } )• Removing the document after the last element inthe 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 – FixedSize Array
    • Considerations• Need to compute the size of the array based onretention period
    • // messages: one doc per user per daydb.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 ofattributes e.g.– User defined Form– Meta Data tags• Queries needed– Equality– Range based• Need to be efficient, regardless of the number ofattributes
    • 2 Approaches (there aremore)• 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 differentidentities e.g.• Username• Email address• FB Handle• LinkedIn URL
    • 2 Approaches (there aremore)• Identifiers in a single document• Separate Identifiers from Content
    • db.users.findOne(){ _id: "joe",email: "joe@example.com,fb: "joe.smith", // facebookli: "joe.e.smith", // linkedinother: {…}}// 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 3find( { _id: "joe"} )
    • Read by email (non-shardkey)Shard 1 Shard 2 Shard 3find ( { email: joe@example.com })
    • Considerations• Lookup by shard key is routed to 1 shard• Lookup by other identifier is scatter gatheredacross 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 readsShard 1 Shard 2 Shard 3db.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, 10genMike Friedman#MongoDBdaysThank You