Cost effective scalinCost effective scalinng for the enterpriseng for the enterprise

ou just eleased youeleased youew productp

urur

But there arsigsig

nfrastructure costs rows faster than evenue

UsersUsers

re troubling gnsgns

• Development timeline slows dow

osts go up

roductivity goes down

SummWe use relational databa

ur developers productiv

ut relational databases

Our hardware costs go uOur hardware costs go uevenue growth is linear

nd we start taking awamade our developers pr

maryases because they mave

s don’t scale up well

up exponentially whileup exponentially while r

y those features that oductive

Database RDBMRDBM

OraclMySQMySQ

PostgreS

OLAPNetezza,

Aster, Hadoop, InfobrightInfobright

EvolutionMSMSe,

QL,QL, SQL

NoSQLMongoDB,

Couch, HBase

MongoDBasy to start Open sourceDrivers in every major language

asy to developSchemaless document modelFlexible query languageSecondary indexes

asy to scaleBuilt in sharding

Downloads per Month

80 000

100,000

120,000Downloads per Month

40,000

60,000

80,000

0

20,000

9-02

9-03

9-04

9-05

9-06

9-07

9-08

9-09

9-10

9-11

9-12

0-01

0-02

0-03

0-04

0-05

0-06

0-07

0-08

0-09

0-10

0-11

Tables to DDocuments{ title: ‘MongoDB’title: MongoDB ,contributors: [ { name: ‘Eliot Horowitz’{ name: Eliot Horowitz ,email: ‘eliot@10gen.com

{ name: ‘Dwight Merrima{ gemail: ‘dwight@10gen.c

}]],model: {

relational: falserelational: false, awesome: true

}}}

ATA AS DOCUMENTSS

TerminDBMSble, View

ow(s)dexiin

rtitionrtition Key

nologyMongogCollectionJSON DocumentIndexE b dd dEmbedded DocumentDocumentShardShard Key

Documog Post Document

= { author: “roger”= { author: roger ,date: new Date(),text: “Spirited Away”tags: [“Tezuka”, “Matags: [ Tezuka , Ma

b posts save(p)b.posts.save(p)

ments

”,anga”]}anga ]}

Querb.posts.find()

{ id : ObjectId("4c4ba5{ _id : ObjectId( 4c4ba5author : "roger", date : "Sat Jul 24 2010

DT)",DT) , text : "Spirited Away", tags : [ "Tezuka" "Matags : [ "Tezuka", "Ma

otes:- id is unique but can

rying

5c0672c685e5e8aabf35c0672c685e5e8aabf3

0 19:47:11 GMT-0700

nga" ] }nga" ] }

n be anything you’d like

Secondaryeate index on any Field

/ 1 means ascending/ 1 means ascending,

>db.posts.ensureIndex(

>db.posts.find({author: '

_id : jectId("4c4ba5c0672c6author : "roger"

y Indexesd in Document

-1 means descending-1 means descending

({author: 1})

'roger'})

685e5e8aabf3"),

Query O• Conditiona

–$all, $exists, $mod, $ne$$

–$lt, $lte

// find posts> db posts find( {ta> db.posts.find( {ta

// f// find posts matchin> db.posts.find(

// count po

peratorsal Operators e, $in, $nin, $nor, $or, $siz$type$ ype, $gt, $gte

s with any tags ags: {$exists: true }} )ags: {$exists: true }} )

g a regular expression{author: /^rog*/i } )

sts by author

Atomic Oset, $unset, $inc, $push, $

omment = { author: “fred”{date: new Dattext: “Best Motext: Best Mo

db posts update( { id: “db.posts.update( { _id: ...{$push: {

perations $pushAll, $pull, $pullAll, $

, ,te(),

ovie Ever”}ovie Ever }

” } }, {comments: comment}} );

Nested Do_id : ObjectId("4c4ba5cauthor : "roger",date : "Sat Jul 24 2010date : Sat Jul 24 2010 DT)", text : "Spirited Away",tags : [ "Tezuka", "Mantags : [ Tezuka , Mancomments : [

{{author : "Fred",date : "Sat Jul 24

DT)"

ocumentsc0672c685e5e8aabf3")

19:47:11 GMT-070019:47:11 GMT-0700

ga" ],ga ],

2010 20:51:03 GMT-0

g> map = function() {

this tags forEathis.tags.forEaemit( tag,

});};};

> reduce = function( keyl 0var total = 0;

values.forEactotal += v

});});return { count

};> out = db posts mapRe

Map Reduach( function(tag) {ach( function(tag) { {count:1} )

y, values ) {

ch( function(value) { ( ( ) {alue.count;

t: total };

educe(m r);

Map R> db[out.r

{ _id: ‘Tezuka’, v{ id: ‘Manga’ v{ _id: Manga , v

Reduceresult].find() value : { count: 3 } }value : { count: 4 } }value : { count: 4 } }

Inde/ Index nested documents

> db.posts.ensureIndex( “cdb.posts.find({‘commentsp ({

/ Index on tags/ Index on tags> db.posts.ensureIndex( ta> db posts find( { tags: ’Ma> db.posts.find( { tags: Ma

// geospatial index> db.posts.ensureIndex( “a> db.posts.find( “author.loc

exesscomments.author”:1 )s.author’:’Fred’})})

ags: 1)anga’ } )anga } )

author.location”: “2d” )cation” : { $near : [22,42]

EPLOYMENT & SCALLING

Replica

PrimaWrite

PrimaRead

SeconReadryry

Read

Seconry

Readryry

a Sets

aryary

ndaAsynchronous

Replication

nda

Replica

PrimaWrite

PrimaRead

SeconReadryry

Read

Seconry

Readryry

a Sets

aryary

nda

nda

Replica

PrimaPrima

PrimaWrite

Read

Seconry

Readryry

a Sets

aryary

aryAutomatic

Leader Electio

nda

Replica

Seconry

Seconry

Read

PrimaWrite

Read

Seconry

Readryry

a Sets

ndanda

ary

nda

Shar

K R K RKey Range0..10

Key Range31..60, 91..95

Primary Primary

Secondary

Secondaryyy

Secondar

yy

Secondaryy yy

rding

K R K RKey Range20..30

Key Range95..100

Primary Primary

Secondary

Secondaryyy

Secondar

yy

Secondaryy yy

WriteWrite

MongoS MongoS

Key Range0..30

Key Range31..60

Primary Primary

0..30 31..60

Primary

S d

Primary

S dy

Secondary y

Secondary

ReadRead

MongoS MongoS

Key Range61..90

Key Range91.. 100

Primary Primary

61..90 91.. 100

Primary

S d

Primary

S dy

Secondary y

Secondary

Photblem:blem:siness needed more flexibility

tion:ed MongoDBMongoDB instead of Orac

ultsults::veloped application in one spveloped application in one sp0% cost reduction compared 0% performance improvemen0% performance improvemen

to Meta-Da

y than Oracle could deliver

cle

print cycleprint cycleto Oracle

nt compared to Oraclent compared to Oracle

CAA

blem:blem:al with massive data volume

tion:ed MongoDBMongoDB to replace Goo

ultsults::ss than1week to build prototyss than1week to build prototypid deployment of new featur

Customer AnalyticsAnalytics

across all customer sites

ogle Analytics / Omniture opti

ype and prove business caseype and prove business caseres (1/day, 1/week)

Onlinblem:blem:SQL could not scale to hand

tion:itched from MySQL to MongMong

ultsults::ssive simplification of code bssive simplification of code bminated need for external cacx performance improvement ox performance improvement o

ne Dictiona

le their 5B+ documents

oDBoDB

basebase ching systemover MySQLover MySQL

SummC

ost

Data

maryRDBMSRDBMS

MongoDB

a

•Easy to s•Easy to d•Easy to s

startdevelopscale