![It’s Pretty PainlessMySQL/* First go create the database, the table, the schema, etc. */mysql_connect("localhost", "username", "password") or die(mysql_error());mysql_select_db("test") or die(mysql_error());$sql = "INSERT INTO users (name, age) VALUES ('Janet', 23)";mysql_query($sql);$result = mysql_query("SELECT * FROM users WHERE age = 23");$row = mysql_fetch_assoc($result);echo "Oh, " . $row['name'] . "!"; // prints "Oh, Janet!"MongoDB$mongo = new Mongo(); // defaults to localhost with no auth$users = $mongo->test_db->users; // database and collection created implicitly$users->insert( array('name' => 'Brad', 'age' => 25) );$user = $users->findOne( array('age' => 25) );echo "Oh, " . $user->name . "!"; // prints "Oh, Brad!"](https://image.slidesharecdn.com/introtomongodb-100909163853-phpapp01/85/Intro-To-Mongo-Db-13-320.jpg)
![QueriesQueries are documentsQuery expression objects indicate a pattern to matchdb.users.find( {last_name: 'Smith'} )Several query objects for advanced queriesdb.users.find( {age: {$gte: 23} } )db.users.find( {age: {$in: [23,25]} } )](https://image.slidesharecdn.com/introtomongodb-100909163853-phpapp01/85/Intro-To-Mongo-Db-19-320.jpg)
![Bad Schema Design: ReferencesCollections for posts, authors, and commentsReferences by manually created IDpost = { id: 150, author: 100, text: 'This is a pretty awesome post.', comments: [100, 105, 112]}author = { id: 100, name: 'Michael Arrington' posts: [150]}comment = { id: 105, text: 'Whatever this sux.'}](https://image.slidesharecdn.com/introtomongodb-100909163853-phpapp01/85/Intro-To-Mongo-Db-28-320.jpg)
![Better Schema Design: EmbeddingCollection for postsEmbed comments, author namepost = { author: 'Michael Arrington', text: 'This is a pretty awesome post.', comments: [ 'Whatever this post sux.', 'I agree, lame!' ]}](https://image.slidesharecdn.com/introtomongodb-100909163853-phpapp01/85/Intro-To-Mongo-Db-29-320.jpg)
Intro To Mongo Db
- 1. Chris Kitechriskite.comIntro to MongoDB
- 2. In This Talk…What is MongoDB?Why use it?Documents and CollectionsQueryingJavaScript ShellSchema Design
- 4. Not a RDBMSMongo is not a relational database like MySQLNo transactionsNo referential integrityNo joinsNo schema, so no columns or rowsNoSQL
- 5. Not a Key-Value StoreMongo is not simply a key-value store like RedisStores structured dataRich query interfaceIndexesMap/ReduceAutomatic sharding, GridFS, geospatial indexing, etc.
- 6. Document-oriented DatabaseRecords are JSON documents (actually BSON)Stored in collectionsNo predefined schemaDocs in the same collection don’t even need to have the same fieldsAtomic in-place operators for contention-free updates$set, $inc, $push, $pop, etc.
- 7. Mongo Documentuser = { name: "Frank Furter", occupation: "A scientist", location: "Transylvania" }
- 9. It’s Stupid Fast!Anywhere from 2 to 10 times faster than MySQLDepends on which contrived benchmark you’re looking atHere’s one I just made up:
- 10. It’s Stupid Fast!About 50 times faster than CouchDBAccording to http://www.idiotsabound.com/did-i-mention-mongodb-is-fast-way-to-go-mongo2 important points:It’s pretty quickBenchmarks are worthless unless you do them on your actual workload
- 11. It’s Web Scale!Sharding built-in, automatic, and *Just Works™*Just Works™ guarantee applies only if you have a cluster of shard replica sets with config servers and routing servers and you define your own shard key(s) with appropriate uniformity and granularityAsynchronous replication for failover and redundancy
- 12. It’s Pretty PainlessSchemalessNo more configuring database columns with typesNo more defining and managing migrationsJust stick your data in there, it’s fineNoSQLORMs exist mostly because writing SQL sucksMongo’s query language is basically JSONThe Mongo driver for your favorite language is really nice and officially supportedHandy JavaScript shell for the CLI
- 13. It’s Pretty PainlessMySQL/* First go create the database, the table, the schema, etc. */mysql_connect("localhost", "username", "password") or die(mysql_error());mysql_select_db("test") or die(mysql_error());$sql = "INSERT INTO users (name, age) VALUES ('Janet', 23)";mysql_query($sql);$result = mysql_query("SELECT * FROM users WHERE age = 23");$row = mysql_fetch_assoc($result);echo "Oh, " . $row['name'] . "!"; // prints "Oh, Janet!"MongoDB$mongo = new Mongo(); // defaults to localhost with no auth$users = $mongo->test_db->users; // database and collection created implicitly$users->insert( array('name' => 'Brad', 'age' => 25) );$user = $users->findOne( array('age' => 25) );echo "Oh, " . $user->name . "!"; // prints "Oh, Brad!"
- 14. All the Cool Kids Are Doing Ithttp://www.mongodb.org/display/DOCS/Production+Deployments
- 16. Documents and CollectionsDocuments are the recordsLike objects in OOP, or rows in RDBMSCollections are groups of documentsUsually represent a top-level class in your appHeterogeneous setUnlike RDBMS tables, no predefined schemaNo foreign keys, so how do we reference other objects?Don't! Just embed the sub-item in the parent docOr, use a key for references and deal with the fact that you don't get integrity or joins
- 17. Embedded ObjectsDocuments can embed other documentsUsed to efficiently represent a relationFor example:{ name: 'Brad Majors', address: { street: 'Oak Terrace', city: 'Denton' }}
- 18. Querying
- 19. QueriesQueries are documentsQuery expression objects indicate a pattern to matchdb.users.find( {last_name: 'Smith'} )Several query objects for advanced queriesdb.users.find( {age: {$gte: 23} } )db.users.find( {age: {$in: [23,25]} } )
- 20. Querying Embedded ObjectsExact match an entire embedded objectdb.users.find( {address: {street: 'Oak Terrace', city: 'Denton'}} )Dot-notation for a partial matchdb.users.find( {"address.city": 'Denton'} )
- 21. JavaScript Shell
- 22. JS ShellComes with MongoDBLaunch it with 'mongo' on the command-lineTry a simplified version at http://www.mongodb.org/Great fit since Mongo docs are basically JSON
- 23. Live DemoIf the tech demo gods allow it
- 24. Schema Design
- 25. I thought you said no schema?There is no predefined schemaYour application creates an ad-hoc schema with the objects it createsThe schema is implicit in the queries your application runs
- 26. Schema DesignUse collections to represent the top-level classes of your applicationBut don't just make a collection for every object typeThese aren't like tables in an RDBMSLess normalization, more embedding
- 27. Obligatory Blog Post ExampleA blog post has an author, some text, and many commentsThe comments are unique per post, but one author has many postsHow would you design this in SQL?Let's look at how we might design it in Mongo
- 28. Bad Schema Design: ReferencesCollections for posts, authors, and commentsReferences by manually created IDpost = { id: 150, author: 100, text: 'This is a pretty awesome post.', comments: [100, 105, 112]}author = { id: 100, name: 'Michael Arrington' posts: [150]}comment = { id: 105, text: 'Whatever this sux.'}
- 29. Better Schema Design: EmbeddingCollection for postsEmbed comments, author namepost = { author: 'Michael Arrington', text: 'This is a pretty awesome post.', comments: [ 'Whatever this post sux.', 'I agree, lame!' ]}
- 30. BenefitsEmbedded objects brought back in the same query as parent objectOnly 1 trip to the DB server requiredObjects in the same collection are generally stored contiguously on diskSpatial locality = fasterIf the document model matches your domain well, it can be much easier to comprehend than nasty joins
- 31. IndexesMongo supports indexes to greatly improve query performanceNo need to create in advanceCreate idempotent indexes in your app with "ensure_index"
- 32. Schema Design LimitationsNo referential integrityHigh degree of denormalization means updating something in many places instead of oneLack of predefined schema is a double-edged swordHopefully you have a model in your appObjects within a collection can be completely inconsistent in their fields
- 33. Final Thoughts
- 34. Final ThoughtsMongoDB is fast no matter how you slice itIt achieves high performance by literally playing fast and loose with your dataThat's not necessarily a bad thing, just a tradeoffVery rapid development, open sourceDocument model is simple but powerfulAdvanced features like map/reduce, geospatial indexing etc. are very compellingSurprisingly great drivers for most languages
- 35. Questions?
- 36. Thanks!These slides are online:http://bit.ly/intro_to_mongo