- MongoDB is a document-oriented, non-relational database that scales horizontally and uses JSON-like documents with dynamic schemas. - It offers features like embedded documents, indexing, replication, and sharding. - Documents are stored and queried using simple statements in a JavaScript-like syntax interface.

1. open-source, high-performance, document-oriented database Antoine Girbal [email_address]

2. RDBMS (Oracle, MySQL) New Gen. OLAP (vertica, aster, greenplum) Non-relational Operational Stores (“NoSQL”) RDBMS Helper (MemCache, Application layer) Data Store Analytics

3. non-relational, next-generation operational datastores and databases focus on scalability ease of modeling and changing data (also no sql syntax, thanks!) NoSQL Really Means:

4. Horizontally Scalable Architectures no joins no complex transactions +

5. JSON-style Documents { hello : “world” } \x16\x00\x00\x00 \x02 hello\x00 \x06\x00\x00\x00world\x00 \x00 http://bsonspec.org represented as BSON Just like a light and friendly XML

6. Flexible “Schemas” In collection db.posts: {author: “mike”, links: 3 , date: "Sun Jul 18 2010 14:40:20 GMT-0700 (PDT)" text: “blah blah”} {author: “eliot”, date: "Sun Jul 18 2010 14:40:22 GMT-0700 (PDT)" text: “Here is MongoDB ...”, views: 10 } Potentially all documents in the same collection

7. Embedded Document { _id: ObjectId("4d1009c7262bb4b94af1cea4") author_id: “1346”, date: "Sun Jul 18 2010 14:40:20 GMT-0700 (PDT)", title: “my story” text: “once upon a time ...”, tags: [“novel”,”english”], Comments:[ {user_id: 234, text: “awesome dude”}, {user_id: 1235, text: “that made me cry”}] } little need for joins or transactions across documents!

8. Data Model Normalized: Many tables Row/Column Natural: Collections Documents Just BLOBS Can't scale As fast as BLOBs if well modeled, horizontal scalability Easy to scale (Dynamo), can easily use caching No feature Most regular SQL features (satisfies 90% of users) (too) many PERFORMANCE FEATURES

9. Features Complex querying Atomic updates with modifiers Indexing (unique, compound, Geo) Aggregation and Map / Reduce Capped Collections Powerful Shell (Javascript) GridFS: file storage

10. Replication master slave slave Using Replica Set: - pool of servers with 1 master - automatic master election and failover - distributed reads (slaveOk) slave Client Client

11. Sharding client mongos ... mongos mongod ... Shards mongod mongod mongod Config Servers mongod mongod mongod mongod mongod mongod mongod mongod ... For large datasets, or write heavy system

12. Support OS: Mac OS X, Windows, Linux, Solaris, 32/64 bits Drivers: C, C#, C++, Haskell, Java, Javascript, Perl, PHP, Python, Ruby, Scala... + community drivers Open-source project with active community, Wiki, Google Group, 10gen consulting / support

13. Production Examples Shutterfly Fourquare Craigslist bit.ly IGN Sourceforge Etsy the New York Times Business Insider Gilt Groupe Intuit College humor Evite Disqus Justin.tv Heartbeat Hot Potato Eventbrite Sugar crm Electronic Arts ...

14. New Post > post = {author: "mike", ... date: new Date(), ... text: "my blog post", ... tags: ["mongodb", "intro"]} > db.posts.save(post) > db.posts.findOne() { "_id" : ObjectId("4d2f944103e8fdbb36f6d205"), "author" : "mike", "date" : ISODate("2011-01-14T00:08:49.933Z"), "text" : "my blog post", "tags" : ["mongodb","intro"]}

15. A Quick Aside special key present in all documents unique across a Collection any type you want _id

16. Update > db.posts.update({_id: post._id}, ... { $set: {author: "tony"}}) > c = {author: "eliot", date: new Date(), text: "great post!"} > db.posts.update({_id: post._id}, ... { $push: {comments: c}}) > db.posts.update({_id: post._id}, ... { $inc: {views: 1}})

17. Querying > db.posts.findOne() { "_id" : ObjectId("4d2f944103e8fdbb36f6d205"), "author" : "tony", "comments" : [ { "author" : "eliot", "date" : ISODate("2011-01-14T00:13:52.463Z"), "text" : "great post!" } ], "date" : ISODate("2011-01-14T00:08:49.933Z"), "tags" : [ "mongodb", "intro" ], "text" : "my blog post", "views" : 1 }

18. More Querying Find by Author > db.posts.find({author: "tony"}) 10 most recent posts: > db.posts.find().sort({date: -1}).limit(10) Posts since April 1 st : > april_1 = new Date(2010, 3, 1) > db.posts.find({date: {$gt: april_1}}) Adding an index to speed up: > db.posts.ensureIndex({author: 1}) > db.posts.ensureIndex({date: 1})

19. More Querying Find with regexp: > db.posts.find({text: /post$/}) Find within array: > db.posts.find({tags: "intro"}) > db.posts.ensureIndex({tags: 1}) Find within embedded object: > db.posts.find({"comments.author": "eliot"}) > db.posts.ensureIndex({"comments.author": 1})

20. More Querying Counting: > db.posts.find().count() > db.posts.find({author: "tony"}).count() Paging: > page = 2 > page_size = 15 > db.post.find().limit(page_size).skip(page * page_size) Advanced operators: $gt, $lt, $gte, $lte, $ne, $all, $in, $nin, $where > db.posts.find({$where: "this.author == 'tony' || this.title == 'foo'"})

21. Download MongoDB http://www.mongodb.org and let us know what you think @mongodb Current 1.6, soon 1.8