1 24 - user data management
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The document discusses modeling and querying user data in MongoDB. It provides examples of storing user account information, activity streams like check-ins, and extending the data model to track additional information like social connections and user clicks. The key points are that user data maps well to MongoDB's flexible document model, the data design should optimize for common queries, and the schema can easily be extended over time to support new use cases.
![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”
}](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-12-320.jpg)
![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)](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-16-320.jpg)
![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:{$near:[40,70]}})
• Regular Expressions
★ db.places.find({name: /^typeaheadstring/)
• Using Tags
★ db.places.find({tags: “business”})](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-18-320.jpg)
![Users
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}
checkins [] = ObjectId reference to Check-Ins
Collection](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-20-320.jpg)
![Stats w/ MapReduce
mapFunc = function() {emit(this.place, 1);}
reduceFunc = function(key, values) {return
Array.sum(values);}
res = db.checkins.mapReduce(mapFunc,reduceFunc,
{query: {timestamp: {$gt:nowminus3hrs}}})
res = [{_id:”10gen HQ”, value: 17}, ….., ….]
... or try using the new aggregation framework!](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-23-320.jpg)
![Extending the Schema
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-29-320.jpg)
![Extending the Schema
user1 = {
name: “Kevin Hanson”
e-mail: “kevin@10gen.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab],
friends: [7b47j62bk1d3c5621c1icv90,
1h11p62bf1d8c716za]
}](https://image.slidesharecdn.com/1-24-userdatamanagement-130124140335-phpapp01/85/1-24-user-data-management-30-320.jpg)
1 24 - user data management
- 1. User Data Management with Please stand by... We’ll begin shortly. 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
- 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 Facebook | Twitter | LinkedIn http://bit.ly/mongo> @mongodb http://linkd.in/joinmongo