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Common Use Cases for MongoDB

Common Use Cases for MongoDB

Presented at LA DeveloperWeek 2013.


Brandon Black

October 20, 2013


  1. Software Engineer, MongoDB Brandon Black @brandonmblack Common Use Cases for

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  3. None
  4. None
  5. What is MongoDB?

  6. MongoDB is a ___________ database •  Document •  Open source

    •  High performance •  Horizontally scalable •  Full featured
  7. Document Database •  Not for .PDF & .DOC files • 

    A document is essentially an associative array •  Document = JSON object •  Document = PHP Array •  Document = Python Dict •  Document = Ruby Hash
  8. Open Source •  MongoDB is an open source project • 

    Available on GitHub •  Licensed under the AGPL •  Started & sponsored by MongoDB, Inc. (10gen) •  Commercial licenses available •  Contributions welcome
  9. High Performance •  Written in C++ •  Extensive use of

    memory-mapped files i.e. read-through write-through memory caching. •  Runs nearly everywhere •  Data serialized as BSON (fast parsing) •  Full support for primary & secondary indexes •  Document model = less work
  10. Shard N Shard 3 Shard 2 Shard 1 Horizontally Scalable

  11. Database Landscape Depth of Functionality Scalability & Performance Memcached MongoDB

  12. Full Featured •  Ad Hoc queries •  Real time aggregation

    •  Rich query capabilities •  Strongly consistent •  Geospatial features •  Native support for most programming languages •  Flexible schema
  13. mongodb.org/downloads

  14. NoSQL and MongoDB

  15. NoSQL Features Flexible Data Models •  Lists, embedded objects • 

    Sparse data •  Semi-structured data •  Agile development High Data Throughput • Reads • Writes Big Data •  Aggregate Data Size •  Number of Objects Low Latency •  For reads and writes •  Millisecond Latency Cloud Computing •  Runs everywhere •  No special hardware Commodity Hardware •  Ethernet •  Local data storage •  JSON Based •  Dynamic Schemas •  Replica Sets to scale reads •  Sharding to scale writes •  1000s of shards in a single DB •  Data partitioning •  Designed for “typical” OS and local file system •  Scale-out to overcome hardware limitations •  In-memory cache •  Scale-out working set
  16. Use Cases

  17. High Volume Data Feeds •  More machines, more sensors, more

    data •  Variably structured Machine Generated Data •  High frequency trading •  Daily closing price Securities Data •  Multiple data sources •  Each changes their format consistently •  Student Scores, Telecom logs Social Media
  18. High Volume Data Feeds Data Sources Asynchronous Writes Flexible document

    model can adapt to changes in data format Write to memory with periodic disk flush Data Sources Data Sources Data Sources Scale writes over multiple shards
  19. Operational Intelligence •  Large volume of users •  Very strict

    latency requirements Ad Targeting •  Expose data to millions of customers •  Reports on large volumes of data •  Reports that update in real time Real-Time Dashboards •  Join the conversation Social Media Monitoring
  20. Operational Intelligence Dashboards API Low latency reads Parallelize queries across

    replicas and shards In database aggregation Flexible schema adapts to changing input data Can use same cluster to collect, store and report on data
  21. {  cookie_id:  “1234512413243”,      advertiser:{        

         apple:  {                  actions:  [                        {  impression:  ‘ad1’,  time:  123  },                        {  impression:  ‘ad2’,  time:  232  },                        {  click:  ‘ad2’,  time:  235  },                        {  add_to_cart:  ‘laptop’,                            sku:  ‘asdf23f’,                              time:  254  },                        {  purchase:  ‘laptop’,  time:  354  }                    ]  …   Behavioral Profiles 1 2 3 See Ad See Ad 4 Click Convert Rich profiles collecting multiple complex actions Scale out to support high throughput of activities tracked Dynamic schemas make it easy to
  22. Metadata •  Diverse product portfolio •  Complex querying and filtering

    Product Catalogue •  Data mining Data analysis •  Retina Scans •  Fingerprints Biometric
  23. Metadata {  ISBN:  “00e8da9b”,      type:  “Book”,    

     country:  “Egypt”,      title:  “Ancient  Egypt”   }   {  type:  “Artefact”,      medium:  “Ceramic”,      country:  “Egypt”,      year:  “3000  BC”   }   Flexible data model for similar but different objects Indexing and rich query API for easy searching and sorting db.archives.        find({  “country”:  “Egypt”  });  
  24. Content Management •  Comments and user generated content •  Personalization

    of content, layout News Site •  Generate layout on the fly •  No need to cache static pages Multi-device rendering •  Store large objects •  Simpler modeling of metadata Sharing
  25. Content Management {  camera:  “Nikon  d4”,      location:  [

     -­‐122.418333,  37.775  ]     }   {  camera:  “Canon  5d  mkII”,      people:  [  “Jim”,  “Carol”  ],        taken_on:  ISODate("2012-­‐03-­‐07T18:32:35.002Z")   }   {  origin:  “facebook.com/photos/xwdf23fsdf”,      license:  “Creative  Commons  CC0”,        size:  {              dimensions:  [  124,  52  ],            units:  “pixels”      }   }   Flexible data model for similar but different objects Horizontal scalability for large data sets Geo spatial indexing for location-based searches GridFS for large object storage
  26. Is My Use Case a Good Fit For MongoDB?

  27. Application Why MongoDB Might be a good fit Large number

    of objects to store Sharding lets you split objects across multiple servers High write or read throughput Sharding + Replication lets you scale read and write traffic across multiple servers Low latency access Memory mapped storage engine caches documents in RAM, enabling in-memory performance. Data locality of documents can significantly improve latency over join-based approaches Variable data in objects Dynamic schema and JSON data model enable flexible data storage without sparse tables or complex joins Cloud based deployment Sharding and replication let you work around hardware limitations in clouds.
  28. What Next?

  29. Online Documentation http://docs.mongodb.org/ MongoDB Blog http://blog.mongodb.org/

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  31. Free Online Courses http://education.mongodb.com/ Events & Webinars http://www.mongodb.com/events Presentations http://www.mongodb.com/presentations

  32. Software Engineer, MongoDB Brandon Black @brandonmblack Thank You http://github.com/brandonblack/presentations http://speakerdeck.com/brandonblack

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