This document discusses the importance of schema design in MongoDB, highlighting its differences from relational databases and how proper schema design can prevent performance issues. It emphasizes the use of JSON documents for data storage, the significance of embedding documents, and presents various considerations for effective data modeling, particularly for time series data. The conclusion stresses the flexibility of MongoDB's schema design and suggests methods like pre-aggregation and bucketing to enhance query performance and manage large datasets.

1. #MDBW17
{Schema Design}
JUMP START

2. #MDBW17
WHY THIS TALK?
1) Schema Design in MongoDB is important
2) Schema Design in MongoDB differs from Relational
3) Almost all performance issues, due to Schema Design

3. #MDBW17
WHAT I WANT YOU TO GET OUT OF THIS
TALK
At the end of this presentation, you should be able to:
1) Understand the Document Model
2) Leverage the Document Model to Design your Use Case
3) Optimize your Schema for humongous workloads

4. HIGH LEVEL
SCHEMA DESIGN

5. #MDBW17
RELATIONAL SCHEMA DESIGN
https://iedei.files.wordpress.com/2012/04/mercedes-f1-car-disassembled.jpeg

6. #MDBW17
MONGODB SCHEMA DESIGN
http://www.northlodge.org/f1/2002/bar/bar-2002-01-honda-02.jpg

7. How MongoDB
Stores Data

8. #MDBW17
MONGODB USES JSON DOCUMENTS
JSON is a widely used standard
• JSON is fast, lean, flexible
• JSON is supported by almost all languages and tools
• JSON is used for modern communication & data exchange
MongoDB stores BSON (Binary JSON)

9. #MDBW17
DOCUMENTS ARE RICH DATA STRUCTURES
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: 447557505611,
city: ‘London’,
location: { type: Point,
coordinates: [45.123,47.232]},
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
Typed field values
Fields can contain
arrays
Fields

10. Schema Design

11. #MDBW17
IT IS ALL ABOUT THE DATA
Questions to keep in mind
• Data access patterns?
• Number of Reads vs Updates?
• Expected size of a Document?
Why Schema Design is important
• Almost all performance issues are related to Schema Design

12. #MDBW17
COLLECTIONS & DOCUMENTS
People Collection
{
id: 1,
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’
}
{
model: ‘Bently’,
year: 1973,
color: ‘silver’,
owner: 1
}
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold‘,
owner: 1
}
{
id: 2,
first_name: ‘Ortega’,
surname: ‘Alvaro’,
city: ‘Valencia’
}
Cars Collection

13. #MDBW17
EMBEDDING DOCUMENTS
{
id: 1,
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’
}
{
model: ‘Bently’,
year: 1973,
color: ‘silver’,
owner: 1
}
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold‘,
owner: 1
}
People Collection Car Collection

14. #MDBW17
EMBEDDING DOCUMENTS
{
id: 1,
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’
}
{
model: ‘Bently’,
year: 1973,
color: ‘silver’,
owner: 1
}
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold‘,
owner: 1
}
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
cars: [
{
model: ‘Bently’,
year: 1973,
color: ‘silver’
},
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold’
}
]
}
People Collection People CollectionCar Collection

15. #MDBW17
EMBEDDING DOCUMENTS
{
id: 1,
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’
}
{
model: ‘Bently’,
year: 1973,
color: ‘silver’,
owner: 1
}
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold‘,
owner: 1
}
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
cars: [
{
model: ‘Bently’,
year: 1973,
color: ‘silver’
},
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold’
}
]
}
People Collection People CollectionCar Collection

16. #MDBW17
WHICH CARS BELONG TO PAUL?
HOW FAST CAN YOU ANSWER THIS?
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
cars: [
{
model: ‘Bently’,
year: 1973,
color: ‘silver’
},
{
model: ‘Rolls Royce’,
year: 1965,
color: ‘gold’
}
]
}

17. WHY DEVELOPERS
LOVE MONGODB

18. #MDBW17
DOCUMENTS == OBJECTS

19. #MDBW17
DEVELOPMENT – BEFORE MONGODB
{ CODE } DB SCHEMAXML CONFIG
APPLICATION RELATIONAL DATABASE
OBJECT RELATIONAL
MAPPING

20. #MDBW17
DEVELOPMENT – BEFORE MONGODB
{ CODE } DB SCHEMAXML CONFIG
APPLICATION RELATIONAL DATABASE
OBJECT RELATIONAL
MAPPING
Time? Focus?

21. #MDBW17
DEVELOPMENT – WITH MONGODB
{ CODE }
Agility
APPLICATION

22. INTERNET OF
THINGS USE CASES

23. #MDBW17
OUR WEATHER COMPANY
• Weather history and forecast service
• Weather station is on the network
• Different measurements (temperature,
wind speed, humidity, etc.)
• We currently cover Vienna region with
a few hundred stations
• Many users looking for historic details
and forecasting
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg

24. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
{
sensor_id : 520124,
date : ISODate(“2017-02-25T17:00:00.000Z”),
temperature : 9,
humidity : 60
}
{
sensor_id : 520124,
date : ISODate(“2017-02-25T17:01:00.000Z”),
temperature : 10,
humidity : 60
}
{
sensor_id : 520124,
date : ISODate(“2017-02-25T17:02:00.000Z”),
temperature : 10,
humidity : 59
}
Document per measurement

25. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
Things to consider
• Relational Approach?
• What will happen if we scale?
(Region, Frequency, Stations)
• Data & Index Size?

26. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
{
sensor_id : 520124,
measurements : [
{
date : ISODate(“2017-02-25T17:13:00.000Z”),
temperature : 9,
humidity : 60
},
{
date : ISODate(“2017-02-25T17:13:05.000Z”),
temperature : 15,
humidity : 55
}, … ],
Bucketing

27. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
Things to consider
• Relational Approach?
• What will happen if we scale?
• Data & Index Size?
• Size of a Document?

28. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
{
sensor_id : 520124,
measurements : [
{
date : ISODate(“2017-02-25T17:13:00.000Z”),
temperature : 9,
humidity : 60
},
{
date : ISODate(“2017-02-25T17:13:05.000Z”),
temperature : 15,
humidity : 55
}, … ],
txCount : 100,
Bucketing by transactions

29. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
Things to consider
• Relational Approach?
• What will happen if we scale?
• Data & Index Size?
• Size of a Document?
• How will user access the data?

30. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
{
sensor_id : 520124,
start_date : ISODate(“2017-02-25T17:00:00.000Z”),
end_date : ISODate(“2017-02-25T18:00:00.000Z”),
measurements : [
{
date : ISODate(“2017-02-25T17:13:00.000Z”),
temperature : 9,
humidity : 60
},
{
date : ISODate(“2017-02-25T17:13:05.000Z”),
temperature : 15,
humidity : 55
}, … ],
txCount : 100,
Bucketing by time and transactions

31. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
Things to consider
• Relational Approach?
• What will happen if we scale?
• Data & Index Size?
• Size of a Document?
• How will user access the data?
• How will Data Scientists access the
data?

32. #MDBW17
DATA MODELLING FOR TIME SERIES
http://www.switchdoc.com/wp-content/uploads/2015/01/41-tvY-gqZL.jpg
{
sensor_id : 520124,
start_date : ISODate(“2017-02-25T17:00:00.000Z”),
end_date : ISODate(“2017-02-25T18:00:00.000Z”),
measurements : [
{
date : ISODate(“2017-02-25T17:13:00.000Z”),
temperature : 9,
humidity : 60
},
{
date : ISODate(“2017-02-25T17:13:05.000Z”),
temperature : 15,
humidity : 55
}, … ],
txCount : 100,
sum_temperature : 1600,
sum_humidity : 5700
}
Bucketing by time and transactions with pre-aggregation

33. #MDBW17
MONGODB COMPASS
1 minute writing into both simple schema and bucket of 100 documents

34. #MDBW17
CONCLUSION
• MongoDB Schema Design is different
• MongoDB Schema is flexible and support modern requirements
• Embedding Documents is key and remove need for joins
• Pre-Aggregation will improve query performance
• Buckets improve overall experience while dealing with big data
• What are your next steps?