Schema Design

#MongoDBDays

Schema Design
Craig Wilson
Software Engineer, 10gen
@craiggwilson

Agenda
• Working with Documents
• Schema Design by Example
• Common Patterns

Single Table En

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

Terminology

Embedding

Example Schema (MongoDB)

Embedding

Linking

Example Schema (MongoDB)

Documents
Provide flexibility and
performance

Traditional Schema Design
Focuses on data storage

Document Schema Design
Focuses on data use

Document Schema Design
• Read Heavy?
• Write Heavy?
– Document Growth

• Analytics
– Map Reduce
– External System

Tools for Working with Data
• Dynamic Schemas
• Embedded data structures
• Ad-hoc queries
– Simple Queries
– Aggregation Framework

• Secondary indexes
• Multi-Key indexes

Tools for Manipulating Data
• On the way out
– Scalar: $ne, $mod, $exists, $type, $lt, $lte, $gt, $gte, $ne
– Vector: $in, $nin, $all, $size

• On the way in
– Scalar: $inc, $set, $unset
– Vector: $push, $pop, $pull, $pushAll, $pullAll, $addToSet

Library Management Application
• Patrons
• Books
• Authors
• Publishers

Use Case #1
As a Librarian, when I
swipe a patron’s card, I
need to verify their
address.

Modeling Patrons

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

One to One Relations
• “Belongs to” relationships are often embedded
• Document model provides a holistic
representation of objects with embedded entities
• Optimized for read performance

Use Case #2
As a Librarian, I want to
store multiple addresses
so I have a better chance
of getting my book back.

Modeling Patrons

patron = { patron = {
_id: "joe", _id: "joe",
name: "Joe Bookreader", name: "Joe Bookreader",
address: { join_date: ISODate("2011-10-15"),
addresses: [
{street: "1 Vernon St.", city: "Newton", …},
city: "Faketon", {street: "52 Main St.", city: "Boston", …},
state: "MA", ]
zip: 12345 }
}
}

Use Case #3
see to the publisher of a
book.

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

Book Data

MongoDB: The Definitive Guide,
By Kristina Chodorow and Mike Dirolf
Published: 9/24/2010
Pages: 216
Language: English

Publisher: O’Reilly Media, CA

Book Model with Embedded
Publisher

book = {
_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"
}
}

Book Model with Embedded
Publisher
• Optimized for read performance of Books
• Other queries are difficult
– All publishers

Use Case #4
see all the publishers in
the system.

Book Model with a Publisher
Link
publisher = {
_id: “oreilly”,
founded: "1980",
location: "CA"
}

book = {
_id: “123”,
publisher_id: “oreilly”,
pages: 216,
language: "English"
}

Use Case #5
see all the books a
publisher has published.

Publisher Model with Book
Links
publisher = {
_id: “oreilly”,
founded: "1980",
location: "CA“,
books: [“123”,…]
}

book = {
_id: “123”,
authors: [ "Kristina Chodorow", "Mike Dirolf" ]
pages: 216,
language: "English"
}

Use Case #6
find the author(s) of
book “Foo”.

Books and Authors
book = {
_id: “123”,
pages: 216,
language: "English"
}

author = {
_id: “kchodorow”,
name: "Kristina Chodorow",
hometown: "New York"
}

author = {
_id: “mdirolf”,
name: “Mike Dirolf",
hometown: “Albany"
}

Relation stored on book end
book = {
authors = ["kchodorow", "mdirolf“],
pages: 216,
language: "English"
}
author = {
_id: "kchodorow",
}
author = {
_id: “mdirolf”,
}

Relation stored on book end
book = {
authors = [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
pages: 216,
language: "English"
}
author = {
_id: "kchodorow",
}
author = {
_id: “mdirolf”,
}

Use Case #7
find other books written
by the same author.

Relation stored on author end

book = {
_id: “123”,
pages: 216,
language: "English"
}

author = {
_id: "kchodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]
}

Relation stored on both sides

book = {
_id: “123”,
authors = [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
pages: 216,
language: "English"
}

author = {
_id: "kchodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]

}

Linking vs. Embedding
• Embedding
– Great for read performance
• One seek to load entire object
• One roundtrip to database
– Writes can be slow
– Maintaining data integrity

• Linking
– More flexibility
– Data integrity is maintained
– Work is done during reads

Parent Links

book = {
pages: 216,
category: "MongoDB"
}

category = { _id: ”MongoDB”, parent: “Databases” }
category = { _id: ”Databases”, parent: “Programming” }

Array of Ancestors
book = {
pages: 216,
parent: "MongoDB",
categories: ["MongoDB", "Databases", "Programming" ]
}

book = {
title: "MySQL: The Definitive Guide",
authors: [ ”Michael Kofler" ],
pages: 216,
parent: "MySQL",
categories : ["MySQL", "Databases", "Programming" ]
}

Ancestors as path
book = {
pages: 216,
category: "MongoDB/Databases/Programming"
}

book = {
title: "MySQL: The Definitive Guide",
authors: [ ”Michael Kofler" ],
pages: 216,
parent: "MySQL",
category: "MySQL/Databases/Programming"
}

Single Table Inheritance

id type area radius length width
1 circle 3.14 1
2 square 4 2
3 rect 10 5 2

• Sparse data
• Is missing value not required or an error?

Single collection (table)
inheritance - MongoDB
> db.shapes.find()

{ _id : 1, type: "circle", area : 3.14, radius : 1 }
{ _id : 2, type: "square", area : 4, length : 2 }
{ _id : 3, type: "rect", area : 10, length : 5, width : 2 }

Summary
• Schema design is different in MongoDB
• Basic data design principals stay the same
• Focus on how application accesses/manipulates
data
• Rapidly evolve schema to meet your
requirements

#MongoDBDays

Thank You
Craig Wilson
Software Engineer, 10gen
@craiggwilson

Schema Design

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