This document provides an introduction to schema design in MongoDB. It begins by explaining why schema design is important, even though MongoDB is often described as "schema-less". The goals of the session are to explain the document model, differences from relational databases, how schema design impacts performance, and thinking about schema design in a new way. Various schema design patterns are discussed, including embedding vs referencing for different types of data relationships. Examples of schema designs for a guitar collector application and healthcare application are provided. Tools like Atlas, Stitch, and Compass that are useful for MongoDB are also mentioned.

1. #MDBlocal

2. #MDBlocal
Jump Start:
Introduction to Schema Design

3. 20 MARCH, 2018
#MDBlocal
Andrew Ryder
Consulting Engineer, MongoDB
ryder@mongodb.com

4. #MDBlocal
Why a Schema Design Jumpstart?
● Schema Design is important in all databases
● Many performance issues we see in MongoDB installations are due to
poor schema design decisions
● MongoDB schema design is different from relational DBs
● MongoDB supports some interesting new patterns
But wait! Isn’t MongoDB “schema-less”?

5. #MDBlocal
Goals for this session
At the end of this session, you should be able to:
● Explain the document model
● Explain the difference between relational DBs and document-based DBs
● Understand how schema design impacts performance
● Think about schema design in a new way

6. #MDBlocal
High-Level Schema Design
Start with the Consumer

7. #MDBlocal
Relational Schemas

8. #MDBlocal
MongoDB Schemas

9. #MDBlocal
How is Data Stored in MongoDB?

10. #MDBlocal
MongoDB Documents are Rich Data Structures
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: 447557505611,
city: ‘London’,
location: { type: Point,
coordinates: [-0.223,51.52]},
Profession: [‘banking’, ‘finance’, ‘trader’],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array of sub-
documents (JSON objects)
Typed field names
Fields
Array

11. #MDBlocal
Document Schemas are Flexible
{
sku: ‘PAINTZXC123’,
product_name: ‘Metallic
Paint’,
product_type: ‘paint’,
colours: [‘Red’, ‘Green’],
size_litres: [5, 10]
}
{
sku: ‘TSHRTASD43546’,
product_name: ‘T-shirt’,
product_type: ‘Mongodb
swag’,
colours: [‘Heather Gray’ …
],
size: [‘S’, ‘M’, ‘L’, ‘XL’],
material: ‘100% cotton’,
wash: ‘cold’,
dry: ‘tumble dry low’
}
● Different Documents in the same ProductsCatalog collection in MongoDB
● Polymorphic Schema - think base class & derived classes

12. #MDBlocal
Rich Indexes
db.collectors.createIndex({
"location":"2dsphere",
"cars.year": 1
});

13. #MDBlocal
Expressive Queries

14. #MDBlocal
Progress on Goals for this session
At the end of this session, you should be able to:
● Explain the document model
● Explain the difference between relational DBs and document-based DBs
● Understand how schema design impacts performance
● Think about schema design in a new way

15. #MDBlocal
2 Big “Rules”:
● Consider how the data will be used
● Data that works together lives together

16. #MDBlocal
Example > Guitar Collectors
ERD
Relational DB MongoDB
Table → Collection
Column → Field
Row → Document
Terminology

17. #MDBlocal
Example > Guitar Collectors
Relational
Consider your consumers:
how will this data be used?

18. #MDBlocal
Example > Guitar Collectors
MongoDB - Embedding

19. #MDBlocal
Example > Guitar Collectors
Typical queries from a consumer of this data might be:
● “What guitars does Aimee Doe own?”
● “Show me all the people who own a Gibson
Les Paul of any age”
● “How many people within 100 miles of Seattle
own guitars made before 1970?”
Consider how the data will be used
X

20. #MDBlocal
Example > Guitar Collectors
Consider how the data will be used
Typical queries from a consumer of this data might be:
● “What guitars does Aimee Doe own?”
● “Show me all the people who own a Gibson
Les Paul of any age”
● “How many people within 100 miles of Seattle
own guitars made before 1970?”

21. #MDBlocal
Progress on Goals for this session
At the end of this session, you should be able to:
● Explain the document model
● Explain the difference between relational DBs and document-based DBs
● Understand how schema design impacts performance
● Think about schema design in a new way

22. #MDBlocal
Design Patterns
Data Relationships
● One : One
● One : Many
● Many : Many
MongoDB Patterns
● Embedding
● Reference
● Subset
● Time Series
● Tree Structure

23. #MDBlocal
Example > Healthcare
We want to build a mobile app for patients and providers.
Within our medical system:
● Patients have multiple medical procedures
● Doctors work at several hospitals & clinics, and vice versa

24. #MDBlocal
1:1 Relationships
← Embed – weak entity
Medical Procedures
Data that works together
lives together.

25. #MDBlocal
One : Many Relationships
{
_id: 2,
first: “Joe”,
last: “Patient”,
addr: { …},
procedures: [
{
id: 12345,
date: 2015-02-15,
type: “CAT scan”,
…},
{
id: 12346,
date: 2015-02-15,
type: “blood test”,
…}]
}
Patients
Embed
{
_id: 2,
first: “Joe”,
last: “Patient”,
addr: { …},
procedures: [12345, 12346]
}
{
_id: 12345,
date: 2015-02-15,
type: “CAT scan”,
…}
{
_id: 12346,
date: 2015-02-15,
type: “blood test”,
…}
Patients
Reference
Procedures
OR

26. #MDBlocal
Many : Many Relationship
Like a One-to-Many relationship, you can embed sub-documents or reference them. Which
approach you take depends on data access patterns and document sizes.
The relational way:
Hospitals
● Id
● Name
● Location
● Foo
● Bar
Hospitals_to_Physicians
Hospital_id
Physician_id
Physicians
● Id
● Name
● Address
● Phone
● etc
“Who works at this hospital?”
“At which clinics does my doctor work?”

27. #MDBlocal
Many : Many Relationship using Embedding
{
_id: 1,
name: “Oak Valley Hospital”,
city: “New York”,
beds: 131,
physicians: [
{
id: 12345,
name: “Joe Doctor”,
address: {…},
…},
{
id: 12346,
name: “Mary Well”,
address: {…},
…}]
}
{
_id: 2,
name: “Plainmont Hospital”,
city: “Omaha”,
beds: 85,
physicians: [
{
id: 63633,
name: “Harold Green”,
address: {…},
…},
{
id: 12345,
name: “Joe Doctor”,
address: {…},
…}]
}
Data Duplication
is Fine!
(usually)

28. #MDBlocal
Many : Many Relationship using References
{
_id: 1,
name: “Oak Valley Hospital”,
city: “New York”,
beds: 131,
physicians: [12345, 12346]
},
{
_id: 2,
name: “Plainmont Hospital”,
city: “Omaha”,
beds: 85,
physicians: [63633, 12346]
}
{
id: 63633,
name: “Harold Green”,
hospitals: [1,2],
…},
{
id: 12345,
name: “Joe Doctor”,
hospitals: [1],
…},
{
id: 12346,
name: “Mary Well”,
hospitals: [1,2],
…}
Hospitals Physicians
Note that references can go either direction - there is no primary key/foreign key concept in MongoDB.

29. #MDBlocal
Progress on Goals for this session
At the end of this session, you should be able to:
● Explain the document model
● Explain the difference between relational DBs and document-based DBs
● Understand how schema design impacts performance
● Think about schema design in a new way

30. #MDBlocal
Key Points
● Schema design in MongoDB is important
● Differs from relational, but some concepts are still useful
● New patterns are more difficult in relational DBs
● Many performance issues are due to poor schema design

31. #MDBlocal
Useful Tools

32. #MDBlocal
Atlas
Deploy, operate, and scale a MongoDB
database in the cloud with just a few clicks
“Database as a Service”
Cloud-hosted MongoDB instances
• You pick the cloud provider and plan,
including a free tier on AWS!
Stitch
Configure your backend application in the
cloud.
“Backend as a Service”
Cloud-hosted app engine that does a lot more
than just access your MongoDB instance:
● Integrate with 3rd party services
● Use OAuth services
● Create custom functions for event-
driven programming

33. #MDBlocal
Compass

34. #MDBlocal
THANK YOU
FOR JOINING US!