One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for RDBMSs, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense. Understandably, this begets good questions: Are foreign keys permissible, or is it better to represent one-to-many relations withing a single document? Are join tables necessary, or is there another technique for building out many-to-many relationships? What level of denormalization is appropriate? How do my data modeling decisions affect the efficiency of updates and queries? In this session, we'll answer these questions and more, provide a number of data modeling rules of thumb, and discuss the tradeoffs of various data modeling strategies.

1. #MongoChicago
Intro to Schema Design
Chad Tindel
Senior Solution Architect, 10gen

2. Agenda
• Working with documents
• Evolving a Schema
• Queries and Indexes
• Common Patterns
Single Table En

3. RDBMS MongoDB
Database ➜ Database
Table ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Terminology

4. Working with
Documents

5. Modeling Data

6. Documents
Provide flexibility and
performance

7. Normalized Data

8. Disk seeks and data locality
Seek = 5+ ms Read = really really fast
Post
Comment
Author Tags

9. De-Normalized (embedded)
Data

10. Disk seeks and data locality
Article
Author
Comment
Tag
Comment
Comment
Comment
Comment
Comment

11. Relational Schema Design
Focus on data storage

12. Document Schema Design
Focus on data use

13. Schema Design
Considerations
• How do we manipulate the data?
– Dynamic Ad-Hoc Queries
– Atomic Updates
– Map Reduce
• What are the access patterns of the application?
– Read/Write Ratio
– Types of Queries / Updates
– Data life-cycle and growth rate

14. Data Manipulation
• Conditional Query Operators
– Scalar: $ne, $mod, $exists, $type, $lt, $lte, $gt, $gte, $ne
– Vector: $in, $nin, $all, $size
• Atomic Update Operators
– Scalar: $inc, $set, $unset
– Vector: $push, $pop, $pull, $pushAll, $pullAll, $addToSet

15. Data Access
• Flexible Schemas
• Ability to embed complex data structures
• Secondary Indexes
• Compound Indexes
• Multi-Value Indexes (Indexes on Arrays)
• Aggregation Framework
– $project, $match, $limit, $skip, $sort, $group, $unwind
• No Joins

16. Getting Started

17. Library Management
Application
• Patrons
• Books
• Authors
• Publishers

18. An Example
One to One Relations

19. Modeling Patrons
patron = { patron = {
_id: "joe" _id: "joe"
name: "Joe Bookreader” name: "Joe Bookreader",
} address: {
street: "123 Fake St. ",
address = { city: "Faketon",
patron_id = "joe",
state: "MA",
street: "123 Fake St. ",
city: "Faketon", zip: 12345
state: "MA", }
zip: 12345 }
}

20. One to One Relations
• Mostly the same as the relational approach
• Generally good idea to embed “contains”
relationships
• Document model provides a holistic
representation of objects

21. An Example
One To Many Relations

22. Modeling Patrons
patron = {
_id: "joe"
name: "Joe Bookreader",
join_date: ISODate("2011-10-15"),
addresses: [
{street: "1 Vernon St.", city: "Newton", state: "MA", …},
{street: "52 Main St.", city: "Boston", state: "MA", …},
]
}

23. Publishers and Books
• Publishers put out many books
• Books have one publisher

24. Book
MongoDB: The Definitive Guide,
By Kristina Chodorow and Mike Dirolf
Published: 9/24/2010
Pages: 216
Language: English
Publisher: O’Reilly Media, CA

25. Modeling Books – Embedded
Publisher
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
publisher: {
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
}

26. Modeling Books & Publisher
Relationship
publisher = {
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}

27. Book _id as a Foreign Key
(BAD)
publisher = {
name: "O’Reilly Media",
founded: "1980",
location: "CA"
books: [ "123456789", ... ]
}
book = {
_id: "123456789",
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}

28. Publisher _id as a Foreign
Key
publisher = {
_id: "oreilly",
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
publisher_id: "oreilly"
}

29. Where do you put the foreign
Key?
• Array of books inside of publisher
– Makes sense when many means a handful of items
– Useful when items have bound on potential growth
• Reference to single publisher on books
– Useful when items have unbounded growth (unlimited # of
books)
• SQL doesn’t give you a choice, no arrays

30. Another Example
One to Many Relations

31. Books and Patrons
• Book can be checked out by one Patron at a
time
• Patrons can check out many books (but not
1000s)

32. Modeling Checkouts
patron = {
_id: "joe"
name: "Joe Bookreader",
join_date: ISODate("2011-10-15"),
address: { ... }
}
book = {
_id: "123456789"
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
...
}

33. Modeling Checkouts
patron = {
_id: "joe"
name: "Joe Bookreader",
join_date: ISODate("2011-10-15"),
address: { ... },
checked_out: [
{ _id: "123456789", checked_out: "2012-10-15" },
{ _id: "987654321", checked_out: "2012-09-12" },
...
]
}

34. De-normalization
Provides data locality
De-normalize for speed

35. Modeling Checkouts -- de-
normalized
patron = {
_id: "joe"
name: "Joe Bookreader",
join_date: ISODate("2011-10-15"),
address: { ... },
checked_out: [
{ _id: "123456789",
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
checked_out: ISODate("2012-10-15")
},
{ _id: "987654321"
title: "MongoDB: The Scaling Adventure", ...
}, ...
]
}

36. Referencing vs. Embedding
• Embedding is a bit like pre-joined data
• Document level ops are easy for server to
handle
• Embed when the “many” objects always appear
with (viewed in the context of) their parents.
• Reference when you need more flexibility

37. An Example
Single Table Inheritance

38. Single Table Inheritance
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
published_date: ISODate("2010-09-24"),
kind: ”loanable"
locations: [ ... ]
pages: 216,
language: "English",
publisher: {
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
}

39. An Example
Many to Many Relations

40. Books and Authors
book = {
title: "MongoDB: The Definitive Guide",
authors = [
{ _id: "kchodorow", name: ”Kristina Chodorow” },
{ _id: "mdirolf", name: ”Mike Dirolf” }
]
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}
author = {
_id: "kchodorow",
name: "Kristina Chodorow",
hometown: "New York"
}
db.authors.find ( { _id : ‘kchodorow’ } )
db.books.find( { authors.id : ‘kchodorow’ } )

41. An Example
Trees

42. Modeling Trees
• Parent Links
- Each node is stored as a document
- Contains the id of the parent
• Child Links
- Each node contains the id’s of the children
- Can support graphs (multiple parents / child)

43. Parent Links
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
category: "MongoDB"
}
category = { _id: MongoDB, parent: Databases }
category = { _id: Databases, parent: Programming }
category = { _id: Programming }

44. Child Links
book = {
_id: 123456789,
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}
category = { _id: MongoDB, children: [ 123456789, … ] }
category = { _id: Databases, children: [ MongoDB, Postgres }
category = { _id: Programming, children: [ Databases, Languages ] }

45. Array of Ancestors
book = {
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
parent: ”MongoDB”,
categories: [ Programming, Databases, MongoDB ]
}
book = {
title: "MySQL: The Definitive Guide",
authors: [ ”Michael Kofler" ],
published_date: ISODate("2010-09-24"),
pages: 320,
language: "English",
parent: ”MySQL",
categories: [ Programming, Databases, MySQL ]
}
db.books.find( { categories : MongoDB } )

46. An Example
Queues

47. Book Document
book = {
_id: 123456789,
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
available: 3
}
db.books.findAndModify({
query: { _id: 123456789, available: { "$gt": 0 } },
update: { $inc: { available: -1 } }
})

48. #MongoChicago
Thank You
Chad Tindel
Solution Architect, 10gen