MongoDB is great for content management and delivery across a multitude of apps such as e-commerce websites, online publications, web content management systems (CMS), document management, archives and others. MongoDB's flexible schema and data model make it easy to catalog multiple content types with diverse meta data. -Schema design for content management -Using GridFS for storing binary files -How you can leverage MongoDB's auto-sharding to partition your content across multiple servers

1. Senior Solutions Architect, MongoDB
Mark Helmstetter
twitter.com/mongodb
MongoDB for Content
Management

2. Agenda
• MongoDB Features and Overview
• Sample Content Management System (CMS)
Application
• Schema Design Considerations
• Building Feeds and Querying Data
• Replication, Failover, and Scaling
• Case Studies
• Further Resources

3. MongoDB Features
• JSON Document Model with
Dynamic Schemas
• Auto-Sharding for Horizontal
Scalability
• Text Search
• Aggregation Framework and
MapReduce
• Full, Flexible Index Support
and Rich Queries
• Built-In Replication for High
Availability
• Advanced Security
• Large Media Storage with
GridFS

4. Sample CMS Application

5. CMS Application Overview
• Business news service
• Hundreds of stories per day
• Millions of website visitors per month
• Comments
• Related stories
• Tags
• Company profiles

6. Viewing Stories (Web Site)
Headline
Date, Byline
Copy
Comments
Tags
Related Stories

7. Viewing Categories/Tags (Web Site)
Headline
Date, Byline
Lead Text
Headline
Date, Byline
Lead Text

8. Sample Article
Headline
Byline, Date, Comments
Copy
Related Stories
Image

9. Schema Design
Considerations

10. Sample Relational DB Structure
story
id
headline
copy
authorid
slug
…
author
id
first_name
last_name
title
…
tag
id
name
…
Comment
id
storyid
name
Email
comment_text
…
related_story
id
storyid
related_storyid
…
link_story_tag
id
storyid
tagid
…

11. Sample Relational DB Structure
• Number of queries per page load?
• Caching layers add complexity
• Tables may grow to millions of rows
• Joins will become slower over time as db increases
in size
• Schema changes
• Scaling database to handle more reads

12. MongoDB Schema Design
• “Dynamic Schema”, however, schema design is
important
• JSON documents
• Design for the use case and work backwards
• Avoid a relational model in MongoDB
• No joins or transactions, most related information
should be contained in the same document
• Atomic updates on documents, equivalent of
transaction

13. {
_id: 375,
headline: ”Apple Reports Second Quarter Earnings",
date: ISODate("2013-07-14T01:00:00+01:00"),
url: “apple-reports-second-quarter-earnings”,
byline: {
author: “Jason Zucchetto”,
title: “Lead Business Editor”
},
copy: “Apple reported second quarter revenue today…”,
tags: [
”AAPL",
”Earnings”
],
comments: [
{ name: “Frank”, comment: “Great story!”}
]
}
Sample MongoDB Schema

14. {
_id: 375,
headline: ”Apple Reports Second Quarter Earnings",
date: ISODate("2013-07-14T01:00:00+01:00"),
url: “apple-reports-second-quarter-earnings”,
byline: {
author: “Jason Zucchetto”,
title: “Lead Business Editor”
},
copy: “Apple reported second quarter revenue today…”,
tags: [
”AAPL",
”Earnings”
],
image: “/images/aapl/tim-cook.jpg”,
ticker: “AAPL”
}
Adding Fields Based on
Story

15. {
_id: 375,
headline: ”Apple Reports Second Quarter Earnings",
date: ISODate("2013-07-14T01:00:00+01:00"),
url: “apple-reports-second-quarter-earnings”,
…
copy: “Apple reported second quarter revenue today…”,
tags: [
”AAPL",
”Earnings”
],
last25comments: [
{ name: “Frank”, comment: “Great story!”},
{ name: “John”, comment: “This is interesting”}
…
]
}
High Comment Volume

16. {
_id: 375,
headline: ”Apple Reports Second Quarter Earnings",
date: ISODate("2013-07-14T01:00:00+01:00"),
url: “apple-reports-second-quarter-earnings”,
…
relatedstories: [
{
headline: “Google Reports on Revenue”,
date: ISODate("2013-07-15T01:00:00+01:00"),
slug: “goog-revenue-third-quarter”
}, {
headline: “Yahoo Reports on Revenue”,
date: ISODate("2013-07-15T01:00:00+01:00"),
slug: “yhoo-revenue-third-quarter”
}
]
}
Managing Related Stories

17. { // Story Collection (sample document)
_id: 375,
headline: ”Apple Reports Second Quarter Earnings",
date: ISODate("2013-07-14T01:00:00+01:00"),
url: “apple-reports-second-quarter-earnings”,
byline: {
author: “Jason Zucchetto”,
title: “Lead Business Editor”
},
copy: “Apple reported second quarter revenue today…”,
tags: [
”AAPL",
”Earnings”
],
last25comments: [
{ name: “Frank”, comment: “Great story!”},
{ name: “John”, comment: “This is interesting”}
]
Full Sample Story Schema

18. image: “/images/aapl/tim-cook.jpg”,
ticker: “AAPL”,
relatedstories: [
{
headline: “Google Reports on Revenue”,
date: ISODate("2013-07-15T01:00:00+01:00"),
slug: “goog-revenue-third-quarter”
}, {
headline: “Yahoo Reports on Revenue”,
date: ISODate("2013-07-15T01:00:00+01:00"),
slug: “yhoo-revenue-third-quarter”
}
]
}
Full Sample Story Schema

19. story
{
headline
date
url
…
relatedstories : []
last25comments : []
…
companyid
}
CMS Collections
comment
{
story_id
name
comment
}
company
{
name
url
location
ticker
last25stories : []
}

20. Querying and Indexing

21. // Display a story, related stories, and first page of comments
db.story.find( { url: “apple-reports-second-quarter-earnings” });
// Display a story, related stories, and second page of comments
db.story.find( { url: “apple-reports-second-quarter-earnings” });
db.comment.find( { story_id : 1234 }).limit(25).skip(25).sort({ date
: -1 });
// All Stories for a given tag
db.story.find( { tags: “Earnings” });
Querying MongoDB

22. // Display data for a company, latest stories
db.company.find( { url: “apple-inc” });
// Display data for a company, all stories
db.company.find( { url: “apple-inc” });
db.story.find( { company_id : 1234 });
Querying MongoDB

23. // Inserting new stories are easy, just submit JSON document
db.story.insert( { headline: “Apple Reports Revenue”... });
// Adding story tags
db.story.update( { _id : 375 }, { $addToSet : { tags : "AAPL" } }
)
// Adding a comment (if embedding comments in story)
db.story.update( { _id : 375 }, { $push: { comments: { name:
‘Jason’, ‘comment: ‘Great Story’} } } )
Inserting and Updating
Stories

24. // Index on story slug
db.story.ensureIndex( { url: 1 });
// Index on story tags
db.story.ensureIndex( { tags: 1 });
MongoDB Indexes for CMS

25. Building Custom RSS Feeds

26. // Very simple to gather specific information for a feed
db.story.find( { tags: { $in : [“Earnings”, “AAPL”] } }).sort(
{ date : -1 });
Query Tags and Sort by Date

27. Replication, Failover, and
Scaling

28. Replication
• Extremely easy to set up
• Replica node can trail primary node and maintain a
copy of the primary database
• Useful for disaster recovery, failover, backups, and
specific workloads such as analytics
• When Primary goes down, a Secondary will
automatically become the new Primary

29. Replication

30. Reading from Secondaries (Delayed
Consistency)
Reading from Secondaries (Delayed
Consistency)

31. Scaling Horizontally
• Important to keep working data set in RAM
• When working data set exceeds RAM, easy to add
additional machines and segment data across
machines (sharding)

32. Sharding with MongoDB

33. Case Studies

34. Runs unified data store serving hundreds of
diverse web properties on MongoDB
Case Study
Problem Why MongoDB Results
• Hundreds of diverse
web properties built on
Java-based CMS
• Rich documents forced
into ill-suited model
• Adding new data types,
tables to RDBMS killed
read performance
• Flexible schema
• Rich querying and support
for secondary index
support
• Easy to manage
replication and scaling
• Developers can focus on
end-user features instead
of back-end storage
• Simplified day-to-day
operations
• Simple to add new brands,
content types, etc. to
platform

35. Serves targeted content to users using MongoDB-
powered identity system
Case Study
Problem Why MongoDB Results
• 20M+ unique visitors
per month
• Rigid relational schema
unable to evolve with
changing data types
and new features
• Slow development
cycles
• Easy-to-manage
dynamic data model
enables limitless
growth, interactive
content
• Support for ad hoc
queries
• Highly extensible
• Rapid rollout of new
features
• Customized, social
conversations
throughout site
• Tracks user data to
increase engagement,
revenue

36. Powers content-serving web platform on
MongoDB to deliver dynamic data to users
Case Study
Problem Why MongoDB Results
• Static web content
• Siloed data stores,
disparate technologies
• Unable to aggregate
and integrate data for
dynamic content
• Support for agile
development
• Easy to use and
maintain
• Low subscription and
HW costs
• Ability to serve dynamic
content
• Decreased TCO
• Replaced multiple
technologies with single
MongoDB database

37. Resource Location
MongoDB Downloads mongodb.com/download
Free Online Training education. mongodb.com
Webinars and Events mongodb.com/events
White Papers mongodb.com/white-papers
Case Studies mongodb.com/customers
Presentations mongodb.com/presentations
Documentation docs.mongodb.org
Additional Info info@mongodb.com
For More Information
Resource Location

38. Questions?

39. 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

40. Senior Solutions Architect, MongoDB
Mark Helmstetter
#MongoDB
Thank You