1. Schema Design
by Alex Litvinok

2. Schema Design
Basic unit of data – Document..

3. Schema Design
What is document?
• BSON Document
• Embedding
• Links across documents

4. Schema Design
Example
01. event = {
02. _id: ObjectId(‘47cc67093475061e3d95369d’),
03. name: ‘MeetUP #2’,
04. date: ISODate(‘2012-04-05 19:00:00'),
05. where: {
06. city: ‘Minsk’,
07. adress: ‘Nezavisimosti, 186’ }
08. }

5. Schema Design
RDBMS? @#$.? NoSQL!
Relation DB Document DB
Database Database
Table Collection
Row(s) Document
Index Index
Join Embedding and Links
Partition Shard
Partition Key Shard Key

6. Schema Design
Why?
• Make queries easy and fast
• Facilitate sharding and automaticity

7. Schema Design
Strategy
• Start with a normalized model
• Embed docs for simplicity and optimization

8. Schema Design
Normalized? Denormalized?

9. Schema Design
Normalized schema
01. Order = {
02. _id : orderId, Order
03. user : userInfo, • _id
04. items : [
• user
05. productId1, • items *
06. productId2,
07. productId3
08. ] Product
09. } • _id
10. Product = { • name
11. _id: productId, • price
12. name : name, • desc
13. price : price,
14. desc : description * Link to collection of product
15. }

10. Schema Design
Normalized schema
• Normalized documents are a perfectably
acceptable way to use MongoDB.
• Normalized documents provide maximum
flexibility.

11. Schema Design
Links across documents
DBRef
{ $ref : <collname>, $id : <idvalue>[, $db : <dbname>] }
Or simple storage of _id..

12. Schema Design
Denormalized schema
01. Order = {
02. _id : orderId, Order
03. user : userInfo, • _id
04. items : [ {
• user
05. _id: productId1, • items
06. name : name1,
07. price : price1 • _id
• name
08. }, {
• price
09. _id: productId2,
10. name : name2, • _id
11. price : price3 • name
12. }] • price
13. }

13. Schema Design
Denormalized schema
• Embedded documents are good for fast queries.
• The embedded documents always available with
the parent documents.
• Embedded and nested documents are good for
storing complex hierarchies.

14. Schema Design
Embedding documents
01.
{
02.
title : "Contributors",
03.
data: [
04.
{ name: “Grover" },
05.
{ name: “James", surname: “Madison" },
06.
{ surname: “Grant" }
07.
]
08.
}
09.

15. Schema Design
..fast queries

16. Schema Design
Indexes
Basics
> db.collection.ensureIndex({ name:1 });
Indexing on Embedded Fields
> db.collection.ensureIndex({ location.city:1 })
Compound Keys
> db.collection.ensureIndex({ name:1, age:-1 })

17. Schema Design
Also indexes..
The _id Index
• Automatically created except capped collection
• Index is special and cannot be deleted
• Enforces uniqueness for its keys
Indexing Array Elements
• Indexes for each element of the array
Compound Keys
• Direction of the index ( 1 for ascending or -1 for descending )

18. Schema Design
Again indexes...
Create options
sparse, unique, dropDups, background, v…
Geospatial Indexing
> db.places.ensureIndex( { loc : "2d" } )
> db.places.ensureIndex( { loc : "2d" } , { min : -500 , max : 500 } )
> db.places.ensureIndex( { loc : "2d" } , { bits : 26 } )

19. Schema Design
Analysis and Optimization
Profiler | Explain

20. Schema Design
Database Profiler
Profiling Level
• 0 - Off
• 1 - log slow operations (by default, >100ms is considered slow)
• 2 - log all operations
> db.setProfilingLevel(2);

21. Schema Design
Database Profiler
Viewing the Data – collection system.profile
> db.system.profile.find()
{ "ts" : "Thu Jan 29 2009 15:19:32 GMT-0500 (EST)" , "info" : "query
test.$cmd ntoreturn:1 reslen:66 nscanned:0 <br>query: { profile: 2 }
nreturned:1 bytes:50" , "millis" : 0}

22. Schema Design
Explain
> db.collection.find( … ).explain()
{
cursor : "BasicCursor",
indexBounds : [ ],
nscanned : 57594,
nscannedObjects : 57594,
nYields : 2 ,
n:3,
millis : 108,
indexOnly : false,
isMultiKey : false,
nChunkSkips : 0
}

23. Schema Design
From theory to Actions..

24. Schema Design
Seating plan
{
_id: ObjectId,
event_id: ObjectId
seats: {
A1:1,
A2:1,
A3:0,
…
H30:0
}
}

25. Schema Design
Seating plan
{
_id: {
event_id: ObjectId,
seat: ‘C9’
},
updated: new Date(),
state: ‘AVALIBLE’
}

26. Schema Design
Feed reader
• Users
• Feed
• Entries

27. Schema Design
Feed reader
Storage users
{
_id: ObjectId,
name: ‘username’,
feeds: [
ObjectId,
ObjectId,
…
]
}

28. Schema Design
Feed reader
Storage feeds
{
_id: ObjectId,
url: ‘http://bbc.com/news/feed’,
name: ‘BBC News’,
latest: Date(‘2012-01-10T12:30:13Z’),
enties:[{
latest: Date(‘2012-01-10T12:30:13Z’),
title: ‘Bomb kills Somali sport officials’,
description: ‘…’, …
}]
}

29. Schema Design
Some tips
1. Duplicate data for speed, reference data for integrity
2. Try to fetch data in a single query
3. Design documents to be self-sufficient
4. Override _id when you have your own simple, unique id
5. Don’t always use an index

30. Schema Design
Conclusion
• Embedded docs are good for fast queries
• Embedded and nested docs are good for storing hierarchies
• Normalized docs are a most acceptable

31. Schema Design
? ? ? ?