![{
! "_id" : ObjectId("51b833cd884498553b746047"),
! "title" : "Book 1",
! "author" : "Ima Writer",
! "tags" : [
! ! "awesome",
! ! "ok",
! ! "lousy",
! ! "ok",
! ! "meh",
! ! "meh"
! ]
}
{
! "_id" : ObjectId("51b833ee884498553b746048"),
! "title" : "Book 2",
! "author" : "Heesan Author",
! "tags" : [
! ! "awesome",
! ! "ok",
! ! "lousy",
! ! "awesome",
! ! "good",
! ! "good"
! ]
}](https://image.slidesharecdn.com/dataasdocuments-130612100257-phpapp01/75/Data-as-Documents-Overview-and-intro-to-MongoDB-57-2048.jpg)
![{
! "result" : [
! ! {
! ! ! "_id" : {
! ! ! ! "tags" : "good"
! ! ! },
! ! ! "authors" : [
! ! ! ! "Heesan Author"
! ! ! ]
! ! },
! ! {
! ! ! "_id" : {
! ! ! ! "tags" : "meh"
! ! ! },
! ! ! "authors" : [
! ! ! ! "Sheesan Author",
! ! ! ! "Ima Writer"
! ! ! ]
! ! }
! ],
! "ok" : 1
}](https://image.slidesharecdn.com/dataasdocuments-130612100257-phpapp01/75/Data-as-Documents-Overview-and-intro-to-MongoDB-59-2048.jpg)
Uploaded byMitch Pirtle
Data as Documents: Overview and intro to MongoDB
The document discusses the evolution of data management systems, highlighting the transition from simple data structures to more complex relational databases and the emergence of modern solutions like MongoDB. It covers the advantages and specific features of various database types, including key/value and document databases, and outlines deployment strategies for scalability. Additionally, it provides insights into MongoDB's data types, common tasks, indexing, and replication strategies.
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Data as Documents: Overview and intro to MongoDB
- 1.
- 2. ABOUTME •Moved from NYCto TO in 2011 •Recovering Joomla! founder •CTO @soundaymusic •Use primarily PHP (Lithium), Node.js •MongoDB Master
- 3. ABOUTTHISTALK •Background on databasehistory •Impact from the Web •Emerging solutions and technologies •Hands-on session •Close with Q&A
- 4. Are you donewith lunch?
- 5. INTHEBEGINNING • Data wassimple. • Performance was simpler. • Scale was a rare need.
- 6. BIRTHOFRELATIONALDATA • Applications gotmore complex. • Many apps, one database pushed logic into the data tier. • “Business rules” was the king buzzword.
- 7. BIRTHOFWEB • Very complex architecture •Very high scale requirements • Rapid application development
- 8. WRONGTOOLRIGHTJOB? •Was great fordata consistency and features, but... •Impossible to scale •Impedance mismatch with modern apps
- 9. ALTERNATIVES • Key /Value • Documents • Memory-only*
- 10. KEYVALUE •EXAMPLES: Memcache, Voldemort, Cassandra,Dynamo, Hibari, Riak •No schema needed •Blazing fast •Minimal features
- 11. DOCUMENT •EXAMPLES: MongoDB, SimpleDB, ElasticSearch,OrientDB •Rich datatypes matching modern apps •More features •Mostly JSON based
- 12.
- 13. MONGODB •Document database, usesJSON •Many user/developer features •Many deployment features •Designed specifically for modern scale challenges and programming languages
- 14. REDIS •Key-value database •Extended datatypes •Many features •Similar facilities for scale and performance
- 15.
- 16. HADOOP •Framework, not reallya database •Born from Google’s map reduce and distributed file system efforts
- 17.
- 18. DEDICATEDSYSTEMS •Low cost, simpleto setup •Great performance •Difficult to scale •Require constant management
- 19. TETHEREDCLOUD •Takes dedicated environmentand extends with cloud infrastructure for scale •Extremely flexible •Even more management and administration
- 20. FULLCLOUD •High initial effort •Muchsimpler to manage long term •Extreme scale •Possibility for equally extreme cost savings*
- 21.
- 22. (hang on aminute)
- 23.
- 24.
- 25. (ok now toget serious)
- 27. WORKINGWITHSQL • Crap, nowI need an ORM! • Disconnect between relational data and object languages • Tons of debugging fun!
- 28. WORKINGWITHMONGODB • Simplifies dataaccess • Simplifies code • Fewer execution steps make faster and lighter apps
- 29. COMMONTERMS •database <-> database •table<-> collection •result <-> document •column <-> property
- 30.
- 31. DOCUMENTDESIGN • strings • integers •arrays • objects • dates • boolean • regex • symbol • javascript • ObjectID • timestamps • GridFS MongoDB documents are BSON:
- 32. DOCUMENTDESIGN • strings • integers •arrays • objects • dates • boolean • regex • symbol • javascript • ObjectID • timestamps • GridFS MongoDB documents are BSON:
- 33. DATATYPE:OBJECTID •MongoDB’s ObjectID isa 12-byte BSON type, comprised of unix seconds from epoch (4 bytes), machine identifier (3 bytes), process id (2 bytes), and random counter (3 bytes).
- 34.
- 35. DATATYPE:OBJECTID •ObjectID is nota string. Always reference them as ObjectId(“...”) as your comparisons will not work if you do not.
- 36. DATATYPE:OBJECTID > x =ObjectId() ObjectId("51b73dff884498553b746046") > x.getTimestamp() ISODate("2013-06-11T15:10:55Z")
- 37. DATATYPE:DATE •MongoDB’s Date isa 64-bit integer that represents the Unix epoch in milliseconds. It is signed, negative values represents dates before 1970.
- 38. DATATYPE:DATE > when =new Date() ISODate("2013-06-11T15:18:30.241Z") > when.toString() Tue Jun 11 2013 17:18:30 GMT+0200 (CEST) > when.getMonth() 5
- 39. DATATYPE:GRIDFS •MongoDB’s GridFS isa facility that allows you to store binary files within the database, and allows you to extend them with JSON metadata.
- 40. (ok this partis easier on the command line. more on this later in this class.)
- 41. COMMONTASKS • find(), findOne() •findAndModify() • ensureIndex() • drop() • insert() • update() • upsert() • save() • remove() • stats()
- 42. INDEXES •MongoDB’s indexes supporta variety of types and needs •Indexing overview
- 43. INDEXTYPES • Standard (_id) •Secondary • Subdocuments • Embedded fields • Compound • ASC and DESC keys • Multikeys • Unique • Sparse • Hash
- 44.
- 45.
- 46. INDEXCREATION •Standard: db.people.ensureIndex( { zipcode:1} ) •Background:
- 47. INDEXCREATION •Standard: db.people.ensureIndex( { zipcode:1} ) •Background: db.people.ensureIndex( { zipcode: 1}, {background: true } )
- 48. INDEXCREATION •Standard: db.people.ensureIndex( { zipcode:1} ) •Background: db.people.ensureIndex( { zipcode: 1}, {background: true } ) •Background Sparse:
- 49. INDEXCREATION •Standard: db.people.ensureIndex( { zipcode:1} ) •Background: db.people.ensureIndex( { zipcode: 1}, {background: true } ) •Background Sparse: db.people.ensureIndex( { zipcode: 1}, {background: true, sparse: true } )
- 50. GRIDFS •Drivers support GridFSwith helper methods, as well as the mongofiles command line tool that is distributed with MongoDB. •Crazy, whack-daddy fast. •Dead simple to use.
- 51.
- 52. NOTE: MongoDB providesmany command line tools to work with your database. They are listed and documented in great detail online.
- 53.
- 54. REPLICATION •MongoDB’s Replica Setsallow you to add multiple masters for write performance, slaves for read performance •Many tutorials and procedures
- 55. REPLICATION M1 M2 M3 H1D1 D2 (M)ember (H)idden (D)elayed
- 56. AGGREGATIONFRAMEWORK •Aggregation Framework provides GROUPBY like functionality without map reduce •Many examples •Detailed reference
- 57. { ! "_id" :ObjectId("51b833cd884498553b746047"), ! "title" : "Book 1", ! "author" : "Ima Writer", ! "tags" : [ ! ! "awesome", ! ! "ok", ! ! "lousy", ! ! "ok", ! ! "meh", ! ! "meh" ! ] } { ! "_id" : ObjectId("51b833ee884498553b746048"), ! "title" : "Book 2", ! "author" : "Heesan Author", ! "tags" : [ ! ! "awesome", ! ! "ok", ! ! "lousy", ! ! "awesome", ! ! "good", ! ! "good" ! ] }
- 58. db.articles.aggregate( { $project :{ author : 1, tags : 1, } }, { $unwind : "$tags" }, { $group : { _id : { tags : "$tags" }, authors : { $addToSet : "$author" } } } );
- 59. { ! "result" :[ ! ! { ! ! ! "_id" : { ! ! ! ! "tags" : "good" ! ! ! }, ! ! ! "authors" : [ ! ! ! ! "Heesan Author" ! ! ! ] ! ! }, ! ! { ! ! ! "_id" : { ! ! ! ! "tags" : "meh" ! ! ! }, ! ! ! "authors" : [ ! ! ! ! "Sheesan Author", ! ! ! ! "Ima Writer" ! ! ! ] ! ! } ! ], ! "ok" : 1 }
- 60. SHARDING •MongoDB’s Sharding allowsyou to scale your data beyond one physical machine: - need more RAM - need more CPU - need more disk
- 61. SHARDINGDEPLOYMENT S1 S2 S3 M1M2 M3 (C)onfig (S)hard server (mongos) (M)ongo shard node (mongod) C1
- 62. MAPREDUCE •MongoDB’s mapReduce performs complexaggregation operations •Many examples •Even more fun than regex!
- 63. Map Reduce iscovered in detail in a later class at BIGDIVE
- 64.
- 65.