This document provides an overview of schema design in MongoDB given different types of data relationships. It discusses modeling one-to-one, one-to-many, and many-to-many relationships using embedding, references, and hybrid approaches. Examples are given for a library management use case to model patrons, books, authors, and publishers and the tradeoffs of different schema designs depending on the growth and relationships of the data.

1. Schema Design
J. Randall Hunt
Developer and Evangelist at MongoDB

2. Who am I?
•
J. Randall Hunt
•
@jrhunt
•
github.com/ranman
•
randall@mongodb.com

3. Why are you here?
•
To learn about MongoDB
•
To engage in the MongoDB community
•
To get free stuff

7. Levels of Abstraction!

9. ORMs To Save The Day!

11. Why change something
that's been around for 40
years?

12. 10TB
Data Human Kind Has Produced Until 1991

13. 10TB
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Data Mankind Produces Every Day Since 2001

14. NOSQL/NOREL

15. Relational Schema Design
Focus on data storage

16. Document Schema Design
Focus on data use

18. Why MongoDB?
•
Focus on commodity hardware, not insane machines
•
Document Store
•
Dynamic Schema
•
Sensible Defaults
•
Modern Scaling Infrastructure

19. How People Use MongoDB
•
Analytics
•
Risk Management
•
Caching Layer
•
Recommendation Engines
•
GIS

20. Nitty Gritty

21. RDBMS
MongoDB
Database
Database
Table
Collection
Row
Document
Index
Index
Join
Embedded Document
Foreign Key
Reference

22. Documents?

23. {
}
"hello": "world"

24. {
}
!
"_id": ObjectId("51638f8332e9bc556fe86de7"),
"dstats": [
{
"+": "5",
"-": "0",
"f": "gitstreamer.py"
},
{
"+": "3",
"-": "3",
"f": "post-commit.py"
}
],
"author": "ranman",
"ts": ISODate("2013-04-08T19:48:11-0400"),
"project": "gitstreamer",
"msg": "turning this into a webapp"

26. CRUD

28. test> db.test.find()
Fetched 0 record(s) in 1ms -- Index[none]

29. test> db.test.find()
Fetched 0 record(s) in 1ms -- Index[none]
test> db.test.insert({'hello': 'world'})
Inserted 1 record(s) in 1ms
Insert WriteResult({
"ok": 1,
"n": 1
})

30. test> db.test.find()
Fetched 0 record(s) in 1ms -- Index[none]
test> db.test.insert({'hello': 'world'})
Inserted 1 record(s) in 1ms
Insert WriteResult({
"ok": 1,
"n": 1
})
test> db.test.find({'hello': 'world'})
{
"_id": ObjectId("52d61af21486ef9e06d6d41a"),
"hello": "world"
}
Fetched 1 record(s) in 0ms -- Index[none]

31. test> db.test.find()
Fetched 0 record(s) in 1ms -- Index[none]
test> db.test.insert({'hello': 'world'})
Inserted 1 record(s) in 1ms
Insert WriteResult({
"ok": 1,
"n": 1
})
test> db.test.find({'hello': 'world'})
{
"_id": ObjectId("52d61af21486ef9e06d6d41a"),
"hello": "world"
}
Fetched 1 record(s) in 0ms -- Index[none]
test> db.test.update({'hello': 'world'}, {$set: {'hello': 'welt'}})
Updated 1 existing record(s) in 0ms
Update WriteResult({
"ok": 1,
"n": 1
})

32. Lots of Operators!

36. Enough already I know what
MongoDB is! Teach me
schema design!

37. Library Management
•
Patrons
•
Books
•
Authors
•
Publishers

38. One To One Relations

39. patron = {
_id: ObjectId("52d7173817d8bbd9564613cd"),
name: 'Joe Schmoe'
}
!
address = {
patron_id: ObjectId("52d7173817d8bbd9564613cd"),
street: "100 Five Bridge Rd",
city: "Clinton",
state: "NC",
zip: 28723
}
>patron = db.patron.find({'name': 'Joe Schmoe'})[0]
>db.address.find('patron_id': patron._id)

40. patron = {
_id: ObjectId("52d7173817d8bbd9564613cd"),
name: 'Joe Schmoe',
address: {
street: "100 Five Bridge Rd",
city: "Clinton",
state: "NC",
zip: 28723
}
}
>db.patrons.findOne({'name': /Joe Schmoe/})

41. One To Many Relations

42. book = {
_id: ObjectID(...),
title: "MongoDB",
authors: ['Kristina Chodorow', 'Mike Dirolf'],
published_date: ISODate('2010-09-24'),
pages: 216,
language: 'English',
publisher: {
name: "O'Reilly Media",
founded: "1980",
location: "CA"
}
}

43. 4 ways of modeling one-to-many (there are more)
•
Embed the publisher
•
Use publisher as the "foreign key"
•
Use book as the "foreign key"
•
Hybrid

44. publisher = {
_id: "oreilly",
name: "O'Reilly Media",
founded: "1980",
location: "CA"
}
book = {
_id: ObjectID(...),
title: "MongoDB",
authors: ['Kristina Chodorow', 'Mike Dirolf'],
published_date: ISODate('2010-09-24'),
pages: 216,
language: 'English',
publisher_id: 'oreilly'
}

45. publisher = {
name: "OReilly Media",
founded: "1980",
location: "CA"
books: [ ObjectId(...), ... ]
}
book = {
_id: ObjectID(...),
title: "MongoDB",
authors: ['Kristina Chodorow', 'Mike Dirolf'],
published_date: ISODate('2010-09-24'),
pages: 216,
language: "English"
}

46. Hybrid Models
•
We store the foreign key
•
and the info relevant to the relation

47. patron = {
_id: ObjectId("52d7173817d8bbd9564613cd"),
name: "Joe Bookreader",
address: {...},
join_date: ISODate("2011-10-15"),
books: [
{_id: ObjectID(...), title: "MongoDB",
author: "Kristina C.", ...},
{_id: ObjectId(...), title: "Postgres",
author: "Randall H.", ...}
]
}

48. 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 bounds on potential growth
Reference to a single publisher on each book
•
Useful when items have unbounded growth

49. Other Things To Model
•
Trees
•
Queues
•
Many-To-Many relationships

50. Thanks!
@jrhunt