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MongoDB for Content
Management
Bryan Reinero
Consulting Engineer, 10gen
Agenda
• Sample Content Management System (CMS)
 Application
• Schema Design Considerations
• Viewing the Final Product
• Building Feeds and Querying Data
• Replication, Failover, and Scaling
• Further Resources
Sample CMS
Application
CMS Application Overview
• Business news service
• Hundreds of stories per day
• Millions of website visitors per month
• Comments
• Related stories
• Tags
Viewing Stories (Web Site)
                  Headline

   Date, Byline                   Tags




      Copy

                             Related Stories



   Comments
Viewing Categories/Tags (Web
            Site)

           Headline

          Date, Byline

           Lead Text


           Headline

          Date, Byline

           Lead Text
Sample Article
            Headline
                    Byline, Date, Comments

                        Copy



                        Image




                         Related Stories
Schema Design
Considerations
Sample Relational DB Structure
story             author           tag    comment
id                id               id     Id
headline          first_name       name   storyid
copy              last_name        …      name
authorid          title                   Email
slug              …                       comment_text
…                                         …

related_story     link_story_tag
id                Id
storyid           storyid
related_storyid   tagid
…                 …
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
MongoDB Schema Design
• “Schemaless”, however, schema design is
 important
• JSON documents
• Design for the use case and work backwards
• Do not use 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
Sample MongoDB Schema
{
    _id: 375,
    headline: ”Apple Reports Second Quarter Earnings",
    date: ISODate("2012-07-14T01:00:00+01:00"),
    slug: “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!”}
    ]
}
Adding Fields Based on
Story
{
    _id: 375,
    headline: ”Apple Reports Second Quarter Earnings",
    date: ISODate("2012-07-14T01:00:00+01:00"),
    slug: “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”
}
High Comment Volume
{
    _id: 375,
    headline: ”Apple Reports Second Quarter Earnings",
    date: ISODate("2012-07-14T01:00:00+01:00"),
    slug: “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”}
      …
    ]
}
Managing Related Stories
{
    _id: 375,
    headline: ”Apple Reports Second Quarter Earnings",
    date: ISODate("2012-07-14T01:00:00+01:00"),
    slug: “apple-reports-second-quarter-earnings”,
    …
    RelatedStories: [
      {
        headline: “Google Reports on Revenue”,
        date: ISODate("2012-07-15T01:00:00+01:00"),
        slug: “goog-revenue-third-quarter”
      }, {
        headline: “Yahoo Reports on Revenue”,
        date: ISODate("2012-07-15T01:00:00+01:00"),
        slug: “yhoo-revenue-third-quarter”
      }
    ]
}
Full Sample Schema
{ // Story Collection (sample document)
 _id: 375,
 headline: ”Apple Reports Second Quarter Earnings",
 date: ISODate("2012-07-14T01:00:00+01:00"),
 slug: “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 Schema (Contd.)
    image: “/images/aapl/tim-cook.jpg”,
    ticker: “AAPL”,
    RelatedStories: [
      {
        headline: “Google Reports on Revenue”,
        date: ISODate("2012-07-15T01:00:00+01:00"),
        slug: “goog-revenue-third-quarter”
      }, {
        headline: “Yahoo Reports on Revenue”,
        date: ISODate("2012-07-15T01:00:00+01:00"),
        slug: “yhoo-revenue-third-quarter”
      }
    ]
}

{ // Comment collection (sample document)
  _id: 1891, storyid: 375, name: “Frank”, comment: “Great story!”
}
Querying and Indexing
Inserting and Updating
Stories
// Inserting new stories are easy, just submit JSON document
db.cms.insert( { headline: “Apple Reports Revenue”... });

// Adding story tags

db.cms.update( { _id : 375 }, { $addToSet : { tags : "AAPL" } } )

// Adding a comment (if embedding comments in story)
db.cms.update( { _id : 375 }, { $addToSet : { comments: {
name: „Jason‟, „comment: „Great Story‟} } } )
MongoDB Indexes for CMS


// Index on story slug
db.cms.ensureIndex( { slug : 1 });



// Index on story tags
db.cms.ensureIndex( { tags: 1 });
Querying MongoDB


// All Story information
db.cms.find( { slug : “apple-reports-second-quarter-earnings” });



// All Stories for a given tag
db.cms.find( { tags: “Earnings” });
Building Custom RSS
Feeds
Query Tags and Sort by Date


// Very simple to gather specific information for a feed
db.cms.find( { tags: { $in : [“Earnings”, “AAPL”] } }).sort(
{ date : -1 });
Replication, Failover, and
Scaling
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
Replication
Reading from Secondaries (Delayed
Consistency)




Reading from Secondaries (Delayed
Consistency)
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)
Sharding with MongoDB
Additional Resources
• Use Case Tutorials:
 http://docs.mongodb.org/manual/use-cases/
• What others are doing:
 http://www.10gen.com/use-case/content-
 management
• This presentation & video recording:
 https://www.10gen.com/presentations/webinar
https://twitter.com/mongodb




Thank You
Bryan Reinero
Consulting Engineer, 10gen

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Webinar: MongoDB for Content Management

  • 2. Agenda • Sample Content Management System (CMS) Application • Schema Design Considerations • Viewing the Final Product • Building Feeds and Querying Data • Replication, Failover, and Scaling • Further Resources
  • 4. CMS Application Overview • Business news service • Hundreds of stories per day • Millions of website visitors per month • Comments • Related stories • Tags
  • 5. Viewing Stories (Web Site) Headline Date, Byline Tags Copy Related Stories Comments
  • 6. Viewing Categories/Tags (Web Site) Headline Date, Byline Lead Text Headline Date, Byline Lead Text
  • 7. Sample Article Headline Byline, Date, Comments Copy Image Related Stories
  • 9. Sample Relational DB Structure story author tag comment id id id Id headline first_name name storyid copy last_name … name authorid title Email slug … comment_text … … related_story link_story_tag id Id storyid storyid related_storyid tagid … …
  • 10. 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
  • 11. MongoDB Schema Design • “Schemaless”, however, schema design is important • JSON documents • Design for the use case and work backwards • Do not use 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
  • 12. Sample MongoDB Schema { _id: 375, headline: ”Apple Reports Second Quarter Earnings", date: ISODate("2012-07-14T01:00:00+01:00"), slug: “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!”} ] }
  • 13. Adding Fields Based on Story { _id: 375, headline: ”Apple Reports Second Quarter Earnings", date: ISODate("2012-07-14T01:00:00+01:00"), slug: “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” }
  • 14. High Comment Volume { _id: 375, headline: ”Apple Reports Second Quarter Earnings", date: ISODate("2012-07-14T01:00:00+01:00"), slug: “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”} … ] }
  • 15. Managing Related Stories { _id: 375, headline: ”Apple Reports Second Quarter Earnings", date: ISODate("2012-07-14T01:00:00+01:00"), slug: “apple-reports-second-quarter-earnings”, … RelatedStories: [ { headline: “Google Reports on Revenue”, date: ISODate("2012-07-15T01:00:00+01:00"), slug: “goog-revenue-third-quarter” }, { headline: “Yahoo Reports on Revenue”, date: ISODate("2012-07-15T01:00:00+01:00"), slug: “yhoo-revenue-third-quarter” } ] }
  • 16. Full Sample Schema { // Story Collection (sample document) _id: 375, headline: ”Apple Reports Second Quarter Earnings", date: ISODate("2012-07-14T01:00:00+01:00"), slug: “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”} ]
  • 17. Full Sample Schema (Contd.) image: “/images/aapl/tim-cook.jpg”, ticker: “AAPL”, RelatedStories: [ { headline: “Google Reports on Revenue”, date: ISODate("2012-07-15T01:00:00+01:00"), slug: “goog-revenue-third-quarter” }, { headline: “Yahoo Reports on Revenue”, date: ISODate("2012-07-15T01:00:00+01:00"), slug: “yhoo-revenue-third-quarter” } ] } { // Comment collection (sample document) _id: 1891, storyid: 375, name: “Frank”, comment: “Great story!” }
  • 19. Inserting and Updating Stories // Inserting new stories are easy, just submit JSON document db.cms.insert( { headline: “Apple Reports Revenue”... }); // Adding story tags db.cms.update( { _id : 375 }, { $addToSet : { tags : "AAPL" } } ) // Adding a comment (if embedding comments in story) db.cms.update( { _id : 375 }, { $addToSet : { comments: { name: „Jason‟, „comment: „Great Story‟} } } )
  • 20. MongoDB Indexes for CMS // Index on story slug db.cms.ensureIndex( { slug : 1 }); // Index on story tags db.cms.ensureIndex( { tags: 1 });
  • 21. Querying MongoDB // All Story information db.cms.find( { slug : “apple-reports-second-quarter-earnings” }); // All Stories for a given tag db.cms.find( { tags: “Earnings” });
  • 23. Query Tags and Sort by Date // Very simple to gather specific information for a feed db.cms.find( { tags: { $in : [“Earnings”, “AAPL”] } }).sort( { date : -1 });
  • 25. 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
  • 27. Reading from Secondaries (Delayed Consistency) Reading from Secondaries (Delayed Consistency)
  • 28. 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)
  • 30. Additional Resources • Use Case Tutorials: http://docs.mongodb.org/manual/use-cases/ • What others are doing: http://www.10gen.com/use-case/content- management • This presentation & video recording: https://www.10gen.com/presentations/webinar