This document summarizes a presentation on schema design in MongoDB. Some key points covered include: - MongoDB uses collections instead of tables and documents instead of rows. - Schema design focuses on how an application will access and use data, rather than on data storage. - Embedded documents are useful for one-to-one relationships but can impact write performance. - Linked data using references is more flexible and ensures data integrity. - Examples demonstrate different approaches to modeling one-to-one, one-to-many relationships and categories.

1. #MongoDBDays
Schema Design
Craig Wilson
Software Engineer, MongoDB
@craiggwilson

2. All application development is
Schema Design

3. Success comes from a
Proper Data Structure

4. Terminology
RDBMS
MongoDB
Database
➜ Database
Table
➜ Collection
Row
➜ Document
Index
➜ Index
Join
➜ Embedding & Linking

5. Working with Documents

6. What is a Document?
{
_id:
“123”,
title:
"MongoDB:
The
Definitive
Guide",
authors:
[
{
_id:
"kchodorow",
name:
"Kristina
Chodorow“
},
{
_id:
"mdirold",
name:
“Mike
Dirolf“
}
],
published_date:
ISODate("2010-­‐09-­‐24"),
pages:
216,
language:
"English",
publisher:
{
name:
"O’Reilly
Media",
founded:
"1980",
location:
"CA"
}
}

7. Traditional Schema Design
Focus on Data Storage

8. Document Schema Design
Focus on Data Usage

9. Traditional Schema Design
What answers do I have?

10. Document Schema Design
What questions do I have?

11. Schema Design By Example

12. Library Management Application
• Patrons/Users
• Books
• Authors
• Publishers

13. Question:
What is a Patron’s Address?

14. A Patron and their Address
>
patron
=
db.patrons.find({
_id
:
“joe”
})
{
_id:
"joe“,
name:
"Joe
Bookreader”
}
>
address
=
db.addresses.find({
_id
:
“joe”
})
{
_id:
"joe“,
street:
"123
Fake
St.
",
city:
"Faketon",
state:
"MA",
zip:
12345
}

15. A Patron and their Address
>
patron
=
db.patrons.find({
_id
:
“joe”
})
{
_id:
"joe",
name:
"Joe
Bookreader",
address:
{
street:
"123
Fake
St.
",
city:
"Faketon",
state:
"MA",
zip:
12345
}
}

16. One-to-One Relationships
• “Belongs to” relationships are often embedded.
• Holistic representation of entities with their
embedded attributes and relationships.
• Optimized for read performance

17. Question:
What are a Patron’s
Addresses?

18. A Patron and their Addresses
> patron = db.patrons.find({ _id : “bob” })
{
_id: “bob",
name: “Bob Knowitall",
addresses: [
{street: "1 Vernon St.", city: "Newton", …},
{street: "52 Main St.", city: "Boston", …},
]
}

19. A Patron and their Addresses
> patron = db.patrons.find({ _id : “bob” })
{
_id: “bob",
name: “Bob Knowitall",
addresses: [
{street: "1 Vernon St.", city: "Newton", …},
{street: "52 Main St.", city: "Boston", …},
]
}
> patron = db.patrons.find({ _id : “joe” })
{
_id: "joe",
name: "Joe Bookreader",
address: { street: "123 Fake St. ", city: "Faketon", …}
}

20. Migration Possibilities
• Migrate all documents when the schema changes.
• Migrate On-Demand
– As we pull up a patron’s document, we make the change.
– Any patrons that never come into the library never get
updated.
• Leave it alone
– As long as the application knows about both types…

21. Question:
Who is the publisher of this
book?

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

23. Book with embedded Publisher
> book = db.books.find({ _id : “123” })
{
_id: “123”,
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"
}
}

24. Book with embedded Publisher
• Optimized for read performance of Books
• Other queries become difficult

25. Question:
Who are all the publishers
in the system?

26. All Publishers
> publishers = db.publishers.find()
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
{
_id: “penguin”,
name: “Penguin”,
founded: “1983”,
location: “CA”
}

27. Book with linked Publisher
> book = db.books.find({ _id: “123” })
{
_id: “123”,
publisher_id: “oreilly”,
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}
> db.publishers.find({ _id : book.publisher_id })
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}

28. Question:
What are all the books a
publisher has published?

29. Publisher with linked Books
> publisher = db.publishers.find({ _id : “oreilly” })
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA“,
books: [“123”,…]
}
> books = db.books.find({ _id: { $in : publisher.books } })

30. Question:
Who are the authors of a
given book?

31. Books with linked Authors
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English“,
authors: [“kchodorow”, “mdirolf”]
}
> authors = db.authors.find({ _id : { $in : book.authors } })
{ _id: "kchodorow", name: "Kristina Chodorow”, hometown: … }
{ _id: “mdirolf", name: “Mike Dirolf“, hometown: … }

32. Books with linked Authors
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English“,
authors = [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
}

33. Question:
What are all the books an
author has written?

34. Authors with linked Books
> authors = db.authors.find({ _id : “kchodorow” })
{
_id: "kchodorow",
name: "Kristina Chodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]
}

35. Links on both Authors and Books
> authors = db.authors.find({ _id : “kchodorow” })
{
_id: "kchodorow",
name: "Kristina Chodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]
}
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
authors = [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
}

36. Linking vs. Embedding
• Embedding
– Great for read performance
– Writes can be slow
– Data integrity needs to be managed
• Linking
– Flexible
– Data integrity is built-in
– Work is done during reads

37. Question:
What are all the books
about databases?

38. Categories as Documents
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
category: “MongoDB”
}
> categories = db.categories.find({ _id: “MongoDB” })
{
_id: “MongoDB”,
parent: “Databases”
}

39. Categories as an Array
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
categories: [“MongoDB”, “Databases”, “Programming”]
}
> db.books.find({ categories: “Databases” })

40. Categories as a Path
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
category: “Programming/Databases/MongoDB”
}
> db.books.find({ category: ^Programming/Databases/* })

41. Conclusion
• Schema design is different in MongoDB
• Basic data design principals stay the same
• Focus on how an application accesses/manipulates
data
• Evolve the schema to meet requirements as they
change

42. #MongoDBDays
Schema Design
Craig Wilson
Software Engineer, 10gen
@craiggwilson