Modern Web Applications with MongoDB

1. Building Modn Web Acaons

2. @spf13Steve Francia AKA Chief Evangelist @ responsible for Developer Experience: drivers, integrations, web & technical writing

3. ๏ History of web development ๏ Picking the right data model ๏ Modern DB interaction ๏ Modern Web Scale Agda

4. What is a modn Web A?

6. If you don't ow whe you've come from, you don't ow whe you are. - James Burke

7. te 90’s ๏Web is born ๏Web development mostly done in perl or C ๏Everyone is a webmaster ๏Relational databases

8. r ’s ๏ Web growth redefines scale ๏ Javascript avoided ๏ Dynamic languages come of age ๏ LAMP ๏ Everyone is a PHP programmer ๏ Relational databases

9. Mid ’s ๏ Social re- redefines scale ๏ Multimedia rules ๏ Heavy caching (memcache) required LAM(m)P ๏ Frameworks (Ruby on Rails) with heavy database abstractions en vogue ๏ Everyone is a OO programmer ๏ Relational databases*

10. is is whe  all falls apa

11. Condons ๏ Web users exponentially increasing ๏ Excessive layering causes applications to be slower ๏ Social (dynamic data) limits use of caching crutch ๏ Cost per byte decreasing rapidly ๏ Data growing in size & complexity

12. Symptoms ๏ Over abstraction ๏ Agile development unsustainable ๏ Needlessly complex architectures ๏ Memcache

13. 2010 trds (Areßing r Ißues) ๏Horizontal scale ๏Variety of Choices (LAMP no more) ๏Specializing

14. Raonal designed for one ing, used for hing

17. What do we lk for in a database? ๏ Right structure to match my data ๏ Performance & Scale ๏ Features that enable me as a developer

18. K Queson: WHAT IS A RECORD?

19. K Value ๏ One-dimensional storage ๏ Single value is a blob ๏ Query on key only ๏ Some support secondary indexes ๏ No schema ๏ Value cannot be updated, only replaced Key Blob Cassandra, Redis, MemcacheD, Riak, DynamoDB

20. Raonal๏ Query on any field ๏ In-place updates ๏ Two-dimensional storage ๏ Each field contains a single value ๏ Very structured schema (table) ๏ Normalization process requires many tables, joins, indexes, and poor data locality Primary Key Oracle, MSSQL, MySQL, PostgreSQL, DB2

21. Documt๏ N-dimensional storage ๏ Each field can contain 0, 1, many, or embedded values ๏ Query on any field & level ๏ Flexible schema ๏ Inline updates ๏ Embedding related data has optimal data locality, requires fewer indexes, has better performance _id MongoDB, CouchDB, RethinkDB

22. Raonal design

23. Documt design

24. Example Blog Post doc { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "steve", date : "Sat Apr 24 2013 19:47:11", text : "About MongoDB...", tags : [ "tech", "databases" ], comments : [ { author : "Fred", date : "Sat Apr 25 2013 20:51:03 GMT-0700", text : "Best Post Ever!" } ] }

27. MongoDB spks your ngauage ๏ Drivers in 14+ languages ๏ Interface is natural and idiomatic for each language ๏ Document natively maps to map/hash/object array/dict/struct

28. place1 = { name : "10gen HQ", address : "229 W 43rd St. 5th Floor", city : "New York", zip : "10036", tags : [ "business", "awesome" ] } Start with an object (or array, hash, dict, etc)

29. Inserting the record Initial Data Load > db.places.insert(place1) > db.places.insert(place1)

30. Querying > db.places.findOne({ zip: "10036", tags: "awesome" }) > db.places.find({tags: [ "rad", "awesome" ]}) { name : "10gen HQ", address : "229 W 43rd St. 5th Floor", city : "New York", zip : "10036", tags : [ "business", "awesome" ] }

31. Updating > db.places.update( {name : "10gen HQ"}, { $push : { comments : { author : "steve", date : 6/26/2013, text : "Office hours are great!" } } } )

32. Nested documents // Index nested documents > db.places.ensureIndex({ "comments.author":1 }) // optional > db.places.find({'comments.author':'Fred'}) { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), name : "10gen HQ", address : "229 W 43rd St. 5th Floor", city : "New York", zip : "10036", comments : [ { author : "Fred", date : "Sat Apr 25 2013 20:51:03", text : "Best Place Ever!" } ] }

33. Multiple values // Index on tags (multi-key index) > db.places.ensureIndex({ tags: 1}) // optional > db.places.find( { tags: 'tech' } ) { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), name : "10gen HQ", address : "229 W 43rd St. 5th Floor", city : "New York", zip : "10036", tags : [ "business", "awesome", "tech" ], }

34. Paginating Places in JS per_page = 10; page_num = 3; places = db.places .find({ "city" : "new york" }) .sort({ "ts" : -1 }) .skip((page_num - 1) * per_page) .limit(per_page);

35. Paginating Places in Ruby @per_page = 10 @page_num = 3 @places = @db.places .find({ :city => "new york" }) .sort({ :ts => -1 }) .skip(( @page_num - 1 ) * @per_page) .limit(@per_page)

36. ch ftures๏ Rich query language ๏ GeoSpatial ๏ Text search ๏ Flexible schema ๏ Aggregation & MapReduce ๏ GridFS (distributed & replicated file storage) ๏ Integration with Hadoop, Storm, Solr & more

37. Database ndscape Scalability&Performance Depth of Functionality MongoDB Key Value RDBMS

38. NoSQL popu

43. Scabi Needs ๏ Data is highly available ๏ Data is consistent ๏ Performant (caching unnecessary)

44. Difft Aroaches ๏ MultiMaster ๏ Peer to peer ๏ Has Conflicts ๏ Ring based approach combines high availability and distribution ๏ Complex application logic ๏ Single Master ๏ Consistent ๏ Slaves have delayed writes ๏ High availability ๏ No scalable solution ๏ Single Master ๏ Consistent ๏ Secondaries have delayed writes ๏ High availability ๏ Range based distribution

45. MongoDB : bui to scale ๏ Intelligent replication ๏ Automatic partitioning of data (user configurable) ๏ Horizontal Scale ๏ Targeted Queries ๏ Parallel Processing

46. Igt Repcaon Node 1 Secondary Node 2 Secondary Node 3 Primary Replication Heartbeat Replication

47. Scable Archecture Node 1 Secondary Config Server Node 1 Secondary Config Server Node 1 Secondary Config Server Shard Shard Shard Mongos App Server Mongos App Server Mongos App Server

48. High Avaibi in Shards Shard Primary Secondary Secondary Shard orMongod x

49. Targed Requests Shard Shard Shard Mongos 1 2 3 4

50. Pall proceßing Shard Shard Shard Mongos 1 2 2 2 4 44 3 3 3 6 5

52. e g database to t your data

53. e g database for YOUR dopmt

54. e g Database for scale & Pfoance

55. Gng staed

56. sy deploymt ๏ Heroku ๏ Rackspace ๏ Amazon ๏ Engine Yard ๏ App Fog ๏ ServerGrove ๏ Azure ๏ Nodejitsu

57. Indust acaon ๏ Media & Entertainment ๏ Retail ๏ Social ๏ Finance ๏ Gaming ๏ Insurance ๏ Healthcare ๏ Government ๏ Archiving ๏ Telecom ๏ Education

58. E IF YOU KED ! Questions? http://spf13.com http://github.com/spf13 @spf13 #DevCon5

Modern Web Applications with MongoDB

Steven Francia

Modern Web Applications with MongoDB