Schema Design by Example ~ MongoSF 2012

Schema Design by Example
Kevin Hanson
Solutions Architect
@hungarianhc
kevin@10gen.com

1

Agenda

• MongoDB Data Model

• Blog Posts & Comments

• Geospatial Check-Ins

• Food For Thought

MongoDB Data Model:
Rich Documents
{
title: ‘Who Needs Rows?’,
reasons: [
{ name: ‘scalability’,
desc: ‘no more joins!’ },
{ name: ‘human readable’,
desc: ‘ah this is nice...’ }
],
model: {
relational: false,
awesome: true
}
}

Embedded Documents
{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "roger",
date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)",
text : "Spirited Away",
tags : [ "Tezuka", "Manga" ],
comments : [

{

author : "Fred",

date : "Sat Jul 24 2010 20:51:03 GMT-0700 (PDT)",

text : "Best Movie Ever"

}
]}

Parallels

RDBMS MongoDB
Table Collection
Row Document
Column Field
Index Index
Join Embedding &
Linking
Schema Object

Relational

Category
• Name
• Url

Article
User • Name
Tag
• Name • Slug • Name
• Email Address • Publish date • Url
• Text

Comment
• Comment
• Date
• Author

MongoDB

Article
• Name
• Slug
• Publish date
User • Text
• Name • Author
• Email Address
Comment[]
• Comment
• Date
• Author

Tag[]
• Value

Category[]
• Value

Schema Design by Example ~ MongoSF 2012

How Should the Documents Look?

What Are We Going to Do with the
Data?

To embed or to link...
That is the question!

1) Fully Embedded
{
blog-title: ‘Commuting to Work’,
blog-text: [
‘This section is about airplanes’,
‘this section is about trains’
],
comments: [
{ author: ‘Kevin Hanson’,
comment: ‘dude, what about driving?’ },
{ author: ‘John Smith’,
comment: ‘this blog is aWful!!11!!!!’ }
],
}

1) Fully Embedded
Pros
• Can query the comments or the blog for results
• Cleanly encapsulated

1) Fully Embedded
Pros
• Can query the comments or the blog for results
• Cleanly encapsulated

Cons
• What if we get too many comments? (16MB
MongoDB doc size)
• What if we want our results to be comments, not
blog posts?

2) Separating Blog & Comments
{ {
_id: _id:
ObjectId("4c4ba5c0672c685 ObjectId("4c4ba5c0672c685e5e
e5e8aabf3") 8aabf4")
comment-ref: blog-ref:
ObjectId("4c4ba5c0672c685 ObjectId("4c4ba5c0672c685e5e
e5e8aabf4") 8aabf3")
blog-title: ‘Commuting to comments: [
Work’, { author: ‘Kevin Hanson’,
blog-text: [ comment: ‘dude, what about
‘This section is about driving?’ },
airplanes’, { author: ‘John Smith’,
‘this section is about comment: ‘this blog is
trains’ aWful!!11!!!!’ }
] ],
} }

Pros
• Blog Post Size Stays Constant
• Can Search Sets of Comments

Pros
• Blog Post Size Stays Constant
• Can Search Sets of Comments

Cons
• Too Many Comments? (same problem)
• Managing Document Links

3) Each Comment Gets Own Doc
{
blog-title: ‘Commuting to Work’,
blog-text: [
‘This section is about airplanes’,
‘this section is about trains’]
}

{
commenter: ‘Kevin Hanson’,
comment: ‘dude, what about driving?’
}

{
commenter: ‘John Smith’,
comment: ‘this blog is aWful!!11!!!!’
}

Pros
• Can Query Individual Comments
• Never Need to Worry About Doc Size

Pros
• Can Query Individual Comments
• Never Need to Worry About Doc Size

Cons
• Many Documents
• Standard Use Cases Become Complicated

Managing Arrays
Pushing to an Array Inﬁnitely...
• Document Will Grow Larger than Allocated
Space
• Document May Increase Max Doc Size of
16MB

Can this be avoided??
• Yes!
• A Hybrid of Linking and Embedding

We Need 3 Things

Places Check-Ins Users

Places

Q: Current location
A: Places near
location

User Generated
Content

Places

Inserting a Place

var p = { name: “10gen HQ”,
address: “578 Broadway, 7th
Floor”,
city: “New York”,
zip: “10012”}

> db.places.save(p)

Tags, Geo Coordinates, and Tips

{ name: “10gen HQ”,
address: “578 Broadway, 7th Floor”,
city: “New York”,
zip: “10012”,
tags: [“MongoDB”, “business”],
latlong: [“40.0”, “72.0”],
tips: [{user: “kevin”, time:
“3/15/2012”,tip: “Make sure to stop by
for office hours!”}],}

Updating Tips

db.places.update({name:"10gen HQ"},

{$push :{tips:

{user:"nosh", time:
3/15/2012,

tip:"stop by for
office hours on

Wednesdays from 4-6"}}}}

Querying Places
• Creating Indexes
★db.places.ensureIndex({tags:1})
★db.places.ensureIndex({name:1})
★db.places.ensureIndex({latlong:”2d”}
)

• Finding Places
★db.places.ﬁnd({latlong:{$near:
[40,70]}})

• Regular Expressions
★db.places.ﬁnd({name: /
^typeaheadstring/)

User Check-Ins

Record User Check-Ins Users

Stats Stats

Check-Ins Users

Users
user1 = {

name: “Kevin Hanson”

e-mail: “kevin@10gen.com”,

check-ins:
[4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}

checkins [] = ObjectId reference to Check-
Ins Collection

Check-Ins
checkin = {

place: “10gen HQ”,

ts: 9/20/2010 10:12:00,

userId: <object id of user>
}

Every Check-In is Two Operations
• Insert a Check-In Object (check-ins
collection)
• Update ($push) user object with check-in

Simple Stats
db.checkins.find({place: “10gen HQ”)

db.checkins.find({place: “10gen HQ”})

.sort({ts:-1}).limit(10)

db.checkins.find({place: “10gen HQ”,

ts: {$gt: midnight}}).count()

Stats w/ MapReduce
mapFunc = function() {emit(this.place, 1);}

reduceFunc = function(key, values) {return
Array.sum(values);}

res =
db.checkins.mapReduce(mapFunc,reduceFunc,

{query: {timestamp: {$gt:nowminus3hrs}}})

res = [{_id:”10gen HQ”, value: 17}, ….., ….]
... or try using the new aggregation framework!
Chris Westin @ 1:50PM

Data How the App Wants It
Think About How the Application Wants
the Data, Not How it is most “Normalized”

Example: Our Business Cards

More info at http://www.mongodb.org/

Kevin Hanson
Solutions Architect, 10gen
twitter: @hungarianhc
kevin@10gen.com

Facebook | Twitter |
http://bit.ly/ @mongodb http://linkd.in/joinmongo
LinkedIn
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Schema Design by Example ~ MongoSF 2012

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