Storing data about users is central to most applications. Whether you run a social network, an identity management system, or an online game, your users are the core of your business. In this webinar, we will discuss MongoDB’s capability to accommodate your evolving user data model and sustain the transaction load associated with accessing and updating user data.

1. User Data Management
with
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Audio: 1-877-668-4493
Kevin Hanson
Solutions Architect, 10gen Access code: 666 500 606
kevin@10gen.com Q&A to follow the webinar
twitter: @hungarianhc
Recording Available 24 Hours After Event
Other issues? E-mail webinars@10gen.com

2. Agenda
// High Level Overview
> MongoDB
> User Data
// Modeling & Querying User Data
> Insurance Company User Data
> User Check-Ins
// Extending the Data Model for Future Use-Cases
> Tracking User Activity
> Social Media

3. MongoDB
• Scalable, High-Performance, Open Source, Document-
Oriented Database
– JSON (well... BSON) Storage Model
– Indexes and Full Query Language
– Easy for Developers to Pick Up
– Easy for Administrators to Scale
• More Features at http://www.mongodb.org/
• Overview Video: http://www.10gen.com/what-is-mongodb

4. User Data
• Account Information
– Name, Address, etc
– Account Status
– Notes
• Activity Streams
– Posts, Tweets, Likes, Check-Ins
– Recording User Actions
• Social Networks
– Friends, Connections
– Groups, Tags

5. Insurance Company Data
Account Information
– Name, Address, etc
– Account Status
eling
– Notes Mod
A Data rcise
Exe

6. Insurance Company

7. Insurance Company

8. Insurance Company

9. Insurance Company
User Opened Account
Date: 05/26/2012
Status: Success
Account Modified
Date: 06/22/2012
Action: Added Spouse
User Call Log
Date: 07/24/2012
Type: Complaint

10. 2 Types of Data
User Opened Account
Date: 05/26/2012
Status: Success
Account Modified
Date: 06/22/2012
Action: Added Spouse
User Call Log
Date: 07/24/2012
Type: Complaint

11. Rule of Thumb: Categories of Data
Map Well to MongoDB Collections
Policies Activities

12. Policies
policy = {
name: “Kevin Hanson”
employer: “10gen”,
address: “555 University”,
e-mail: “kevin@10gen.com”,
twitter: “@hungarianhc”,
spouse: “Yes”,
dependents: “No”,
dates: [
{start: 5/26/2012 10:12:00},
end: 5/26/2013 10:12:00}],
others: “No”
}

13. Activities
User Opened Account
Date: 05/26/2012 activity = {
Status: Success
user-id: “kevinhanson421”
type: “account-opening”,
Account Modified
Date: 06/22/2012
status: “Success”,
Action: Added Spouse
dates: 5/26/2012 10:12:00,
related-doc: “/customer/
User Call Log kevinhanson421/open.pdf”
Date: 07/24/2012
}
Type: Complaint

14. User Check-Ins
Activity Streams
– Posts, Tweets, Check-Ins
– Recording User Actions

15. Places
Q: Current location
A: Places near
location
User Generated
Content
Places

16. Inserting a Place
var p = { 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!”}]}
> db.posts.save(p)

17. Updating Tips
db.places.update({name:"10gen HQ"},
{$push :{tips:
{user:"Kevin", time:3/15/2012,
tip:"stop by for office hours on
Wednesdays from 4-6"}}}}

18. Querying Our 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/)
• Using Tags
★ db.places.find({tags: “business”})

19. User Check-Ins
Record User Check-Ins Users
Stats Stats
Check-Ins Users

20. Users
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}
checkins [] = ObjectId reference to Check-Ins
Collection

21. Check-Ins
user1 = {
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 ID
(users collection)

22. 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()

23. 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!

24. Adding More User Data
User Opened Account
Date: 05/26/2012
Status: Success
Account Modified
Date: 06/22/2012
Action: Added Spouse
User Call Log
Date: 07/24/2012
Type: Complaint

25. Adding More User Data
Click! Click!
User Opened Account
Date: 05/26/2012
Click!
Status: Success
Account Modified
Click! Date: 06/22/2012 Click!
Action: Added Spouse
User Call Log
Date: 07/24/2012
Type: Complaint

26. Tracking Clicks
Policies Activities

27. Each Click Creates a New Doc
Policies Activities Clicks

28. Clicks
click = {
user: “kevinhanson”,
ts: 9/20/2010 10:12:00,
link: “http://some-link-here.com/wherever”
}
Now we can audit user activity...
db.clicks.find({user:”kevinhanson”}).sort({ts:-1})
Show me all of Kevin’s clicks sorted by timestamp.

29. Extending the Schema
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}

30. Extending the Schema
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab],
friends: [7b47j62bk1d3c5621c1icv90,
1h11p62bf1d8c716za]
}

31. Takeaways
// User Data Fits Well in MongoDB
> Flexible Data Model
> Stay Agile; Make Changes
> Many Customers in Production
// Application Patterns Drive Data Design
> Optimize Data Model For Queries
> Primary Use Cases Drive Design
// Adding Features Can Be Easy
> Create New Data Structures
> Extend Existing

32. More info at http://www.mongodb.org/
conferences,
appearances,
and
meetups
http://www.10gen.com/events
Questions?
kevin@10gen.com
@hungarianhc
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