User Data Management with MongoDB

Solutions Architect, MongoDB
Heather Kirksey
#MongoDB
User Data Management
with MongoDB

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 UserActivity
– Social Media

MongoDB is a(n) ___________
database
• Document
• Open source
• High performance
• Horizontally scalable
• Full featured

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

Data Modeling Exercise
• Insurance Company Data
• Account information
– Name, address, etc
– Account status
– Notes

Data Modeling Example
User Opened
Date: 05/26/2013
Status: Successful
Account Modified
Date: 06/04/2013
Status: Added spouse
User Call Log
Date: 01/22/2014
Type: Complaint

Insurance Company Site
User Opened
Date: 05/26/2013
Status: Successful
Account Modified
Date: 06/04/2013
User Call Log
Date: 01/22/2014
Type: Complaint
2 types
of data
2 types
of data

Rule of Thumb
• Categories of data map well to MongoDB
Collections
Policies Activities

Policies
policy = {
name: “John Smith”
employer: “10gen”,
address: “555 Fictional
Ave”,
e-mail:
“me@john.smith.com”,
spouse: “Yes” ,
dependents: “No”,
dates: [
{start: 5/26/2013
10:12:00},
end: 5/26/2023
10:12:00}],

Activities
activity = {
user-id: “JohnSmith421”
type: “account-opening”
,
status: “Success”,
dates: 5/26/2013
10:12:00,
related-doc:
“/customer/JohnSmith421/open
.pdf”
}
User Opened Account
Date: 05/26/2013
Status: Success
Account Modified
Date: 06/04/2013
Action: Added Spouse
User Call Log
Date: 01/22/2014
Type: Complaint

User Check-Ins
• Activity Streams
– Posts, tweets, check-ins
– Recording user actions

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

Inserting a Place
var p = { name: “MongoDB HQ”,
address:“229 W 43rd St”,
city: “NewYork”,
zip: “10036”,
tags: [“mongoDB”,“business”],
latlong: [40.0, 72.0],
tips: [{user:“John Smith”, time: “3/15/2013”,tip:“Make sure to stop by
for office hours!”}]}
> db.posts.save(p)

Updating Tips
db.places.update({name: “MongoDB HQ”},
{$push :{tips:
{user: “John”, time:3/10/2014,
tip: “stop by for office hours on
Wednesdays from 4-6”}}})

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”})

User Check Ins
Record User Check-Ins
Check-Ins
Users
Stats
Users
Stats

Users
user1 = {
e-mail: “me@john.smith.com”,
check-ins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}
checkins [] = ObjectId reference to Check-Ins
Collection

Check-Ins
user1 = {
place: “MongoDB 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)

Simple Stats
db.checkins.find({place: “MongoDB HQ”)
db.checkins.find({place: “MongoDB HQ”})
.sort({ts:-1}).limit(10)
db.checkins.find({place: “MongoDB 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:”MongoDB HQ”, value: 17}, … , …]
…or try using the aggregation framework!

Adding More User Data
User Opened
Date: 05/26/2013
Status: Successful
Account Modified
Date: 06/04/2013
User Call Log
Date: 01/22/2014
Type: Complaint

Tracking Clicks
Policies Activities

Each Click Creates a New Doc
Policies Activities Clicks

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

Extending the Schema
user1 = {
e-mail: “me@John.Smith.com”,
check-ins:
[4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]
}

Extending the Schema
user1 = {
e-mail: “me@John.Smith.com”,
check-ins:
[4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d
8c736ab]
}
friends:
[7b47j62bk1d3c5621c1icv90, 1h11p62bf1d8
c716za]

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 is easy
– Create new data structures
– Extend existing

MongoDB World
New York City, June 23-25
#MongoDBWorld
See what’s next in MongoDB including
• MongoDB 2.6
• Sharding
• Replication
• Aggregation
http://world.mongodb.com
Save $200 with discount code THANKYOU

Solutions Architect, MongoDB
Heather Kirksey
#MongoDB
Thank You

User Data Management with MongoDB

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