Introduction to MongoDB

open-source, high-performance, document-oriented database

Antoine [email protected]

RDBMSRDBMS(Oracle, MySQL)(Oracle, MySQL)

New Gen. New Gen. OLAPOLAP

(vertica, aster, (vertica, aster, greenplum)greenplum)

Non-relationalNon-relationalOperational StoresOperational Stores

(“NoSQL”)(“NoSQL”)

RDBMS RDBMS HelperHelper

(MemCache, (MemCache, Application Application

layer)layer)

Data Store Analytics

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:

Horizontally ScalableArchitectures

no joins

no complex transactions+

JSON-style Documents

{ hello: “world” }

\x16\x00\x00\x00\x02hello\x00\x06\x00\x00\x00world\x00\x00

http://bsonspec.org

represented as BSON

Just like a light and friendly XML

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

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 joinsor transactions across documents!

Data Model

Normalized:Many tablesRow/Column

Natural:CollectionsDocuments

JustBLOBS

PERFORMANCE

FEATURES

Can't scaleAs fast as BLOBs if

well modeled, horizontal scalability

Easy to scale (Dynamo), can

easily use caching

No featureMost regular SQL features (satisfies

90% of users)

(too) many

Features

•Complex querying

•Atomic updates with modifiers

•Indexing (unique, compound, Geo)

•Aggregation and Map / Reduce

•Capped Collections

•Powerful Shell (Javascript)

•GridFS: file storage

Replication

master

slave slave

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

slave

Client Client

Sharding

client

mongos ...mongos

mon

god

...

Shards

mongod

mongod

mongod

ConfigServers

mon

god

mon

god

mon

god

mon

god

mon

god

mon

god

mon

god

mon

god ...

For large datasets, or write heavy system

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

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 ...

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"]}

A Quick Aside

•special key

•present in all documents

•unique across a Collection

•any type you want

_id

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}})

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

}

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 1st:> 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})

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})

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'"})

Download MongoDB

http://www.mongodb.org

and let us know what you think@mongodb

Current 1.6, soon 1.8