This document discusses different schema designs for common use cases in MongoDB. It presents four examples: (1) modeling a message inbox, (2) retaining historical data within limits, (3) storing variable attributes efficiently, and (4) looking up users by multiple identities. For each use case, it analyzes different modeling approaches and tradeoffs between query performance and write efficiency. The key message is that the optimal schema depends on an application's specific access patterns and usage.

1. Perl Engineer & Evangelist, 10gen
Mike Friedman
#MongoDBdays
Schema Design
Four Real-World Use
Cases

2. Single Table En
Agenda
• Why is schema design important
• 4 Real World Schemas
– Inbox
– History
– IndexedAttributes
– Multiple Identities
• Conclusions

3. 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?"

4. #1 - Message Inbox

5. Let’s get
Social

6. Sending Messages
?

7. Design Goals
• Efficiently send new messages to recipients
• Efficiently read inbox

8. Reading my Inbox
?

9. 3 Approaches (there are
more)
• Fan out on Read
• Fan out on Write
• Fan out on Write with Bucketing

10. // 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

11. Fan out on read – Send
Message
Shard 1 Shard 2 Shard 3
Send
Message

12. Fan out on read – Inbox Read
Shard 1 Shard 2 Shard 3
Read
Inbox

13. 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

14. // 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 = msg.to[recipient]
db.inbox.save( msg );
}
// Read my inbox
db.inbox.find( { recipient: "Joe" } ).sort( { sent: -1 } )
Fan out on write

15. Fan out on write – Send
Message
Shard 1 Shard 2 Shard 3
Send
Message

16. Fan out on write– Read Inbox
Shard 1 Shard 2 Shard 3
Read
Inbox

17. 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

18. // 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

19. // 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

20. 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

21. Fan out on write with buckets -
Send
Shard 1 Shard 2 Shard 3
Send
Message

22. Fan out on write with buckets -
Read
Shard 1 Shard 2 Shard 3
Read
Inbox

23. #2 – History

25. 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

26. 3 Approaches (there are
more)
• Bucket by Number of messages
• Fixed size Array
• Bucket by Date + TTL Collections

27. 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("…") }}}})
// Remove elements based on a date
db.inbox.update({owner: "friend1" },
{ $pull: { messages: {
sent: { $gte: ISODate("…") } } } } )
Inbox – Bucket by #
messages

28. 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 }

29. 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

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

31. // 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

32. #3 – Indexed Attributes

33. 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

34. 2 Approaches (there are
more)
• Attributes as Embedded Document
• Attributes as Objects in an Array

35. 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-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

36. Considerations
• Each attribute needs an Index
• Each time you extend, you add an index
• Lots and lots of indexes

37. 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

38. Considerations
• Only one index needed on attr
• Can support range queries, etc.
• Index can be used only once per query

39. #4 – Multiple Identities

40. Design Goal
• Ability to look up by a number of different
identities e.g.
• Username
• Email address
• FB Handle
• LinkedIn URL

41. 2 Approaches (there are
more)
• Identifiers in a single document
• Separate Identifiers from Content

42. db.users.findOne()
{ _id: "joe",
email: "joe@example.com,
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

43. Read by _id (shard key)
Shard 1 Shard 2 Shard 3
find( { _id: "joe"} )

44. Read by email (non-shard
key)
Shard 1 Shard 2 Shard 3
find ( { email: joe@example.com }
)

45. 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

46. // 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: "joe@abc.com" }, user: "1200-42" } )
db.identities.save(
{ identifier : { li: "joe.e.smith" }, user: "1200-42" } )
// Shard collection by _id
db.shardCollection( "mydb.identities", { identifier : 1 } )
// Create unique index
db.users.ensureIndex( { _id: 1} , { unique: true} )
// Shard collection by _id
db.shardCollection( "mydb.users", { _id: 1 } )
Document per Identity

47. Read requires 2 reads
Shard 1 Shard 2 Shard 3
db.identities.find({"identifier" : {
"hndl" : "joe" }})
db.users.find( { _id: "1200-42"}
)

48. Considerations
• Lookup to Identities is a routed query
• Lookup to Users is a routed query
• Unique indexes available

49. Conclusion

50. 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

51. Perl Engineer & Evangelist, 10gen
Mike Friedman
#MongoDBdays
Thank You