Introduction to MongoDB

Post on 14-Jan-2015

1,164 views 1 download

Tags:

description

Introduction to MongoDB. Uses example of a simple location-based application to introduce schema design, queries, updates, map-reduce, deployment

transcript

http://mongodb.orghttp://10gen.com

Building applications with MongoDB – An introductionMongoNYC – June 7, 2011

Nosh Petigaranosh@10gen.com

@noshinosh

Today’s Talk•MongoDB: Data modeling, queries,

geospatial, updates, map reduce

•Using a location-based app as an example

•Example Works in MongoDB JS shell

Application Goals

PlacesCheck

ins

(1)Q: Current locationA: Places near location

(2) Add user generated content

(3) Record user checkins

(4) Stats about checkins

Documents

doc1 = {_id: 4b97e62bf1d8c7152c9ccb74,key1: value1,key2: value2,

key3: {..., ..., ...},

key4: [..., ..., ]

}

Collections

doc1, doc2, doc3

Places Users Checkins

doc3, doc4, doc5

doc6, doc7, doc8

place1 = {name: "10gen HQ”,address: ”134 5th Avenue 3rd Floor”,city: "New York”,zip: "10011”

}

db.places.find({zip:”10011”}).limit(10)

Places v1

place1 = {name: "10gen HQ”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,

tags: [“business”, “recommended”]

}

db.places.find({zip:”10011”, tags:”business”})

Places v2

place1 = {name: "10gen HQ”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,

tags: [“business”, “cool place”],

latlong: [40.0,72.0]

}

db.places.ensureIndex({latlong:”2d”})db.places.find({latlong:{$near:[40,70]}})

Places v3

place1 = {name: "10gen HQ”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,latlong: [40.0,72.0],

tags: [“business”, “cool place”],

tips: [{user:"nosh", time:6/26/2010, tip:"stop by for office

hours on Wednesdays from 4-6pm"}, {.....}, {.....}]

}

Places v4

Creating your indexesdb.places.ensureIndex({tags:1})db.places.ensureIndex({name:1})db.places.ensureIndex({latlong:”2d”})

Finding places:db.places.find({latlong:{$near:[40,70]}})

With regular expressions:db.places.find({name: /^typeaheadstring/)

By tag:db.places.find({tags: “business”})

Querying your Places

Initial data load:db.places.insert(place1)

Updating tips:db.places.update({name:"10gen HQ"},

{$push :{tips: {user:"nosh", time:6/26/2010,

tip:"stop by for office hours on Wednesdays from 4-6"}}}}

Inserting and updating places

$set, $unset, $rename

$push, $pop, $pull, $addToSet

$inc

Atomic Updates

Application Goals

PlacesCheck

ins

(1)Q: Current locationA: Places near location

(2) Add user generated content

(3) Record user checkins

(4) Stats about checkins

user1 = {name: “nosh”email: “nosh@10gen.com”,...checkins: [4b97e62bf1d8c7152c9ccb74,

5a20e62bf1d8c736ab]}

checkins [] = ObjectId reference to checkin collection

Users

checkin1 = {place: “10gen HQ”,

ts: 6/7/2011 10:12:00,

userId: <objectid of user>}

Check-in = 2 opsInsert check in object [checkin collection]Update ($push) user object [user collection]

Indexes:db.checkins.ensureIndex({place:1, ts:1})db.checkins.ensureIndex({ts:1})

Checkins

Application Goals

PlacesCheck

ins

(1)Q: Current locationA: Places near location

(2) Add user generated content

(3) Record user checkins

(4) Stats about checkins

Simple Statsdb.checkins.find({place: “10gen HQ”)

db.checkins.find({place: “10gen HQ”}).sort({ts:-1}).limit(10)

db.checkins.find({place: “10gen HQ”, ts: {$gt: midnight}}).count()

db.checkins.find().sort(ts:-1)}.limit(50)

Stats with MapReduce

mapFunc = function() {emit(this.place, 1);}

reduceFunc = function(key, values) {return Array.sum(values);

}

db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}, out: “result”})

result = [{_id:”10gen HQ”, value: 17}, {…..}, {….}]

db.result.find({ value: {$gt: 15}})

Application Goals

PlacesCheck

ins

(1)Q: Current locationA: Places near location

(2) Add user generated content

(3) Record user checkins

(4) Stats about checkins

Single Master Deployments

Primary/Master

Secondary/Slave• Configure as a replica set for automated

failover

• Add more secondaries to scale reads

Auto Sharded Deployment

Primary/Master

Secondary/Slave

MongoS

•Autosharding distributes data among two or more replica sets• Mongo Config Server(s) handles distribution & balancing

• Transparent to applications

MongoConfig

Use Cases•RDBMS replacement for high-traffic web

applications

•Content Management-type applications

•Real-time analytics

•High-speed data logging

Web 2.0, Media, SaaS, Gaming, Finance, Telecom, Healthcare

Nosh Petigaranosh@10gen.comDirector of Product Strategy, 10genhttp://mongodb.orghttp://10gen.com

-We are hiring!-@mongodb

nosh@10gen.com@noshinosh

MongoDB in Production