Solutions Architect, 10gen

Blossom Coryat

Building your First App with MongoDB

Today’s Talk

• Introduction to MongoDB

• Discuss data modeling, queries, indexes, updates and aggregation in MongoDB within the context of building a location-based app

What is MongoDB?

MongoDB is a ___________ database• Document Oriented

• Open source

• High performance

• Highly Available

• Horizontally scalable

• Full featured

Document Oriented

• A document is essentially an associative array

• Document == JSON object

• Document == PHP Array

• Document == Python Dict

• Document == Ruby Hash

• Etc.

Open Source

• MongoDB is an open source project– Started and maintained by 10gen

• Licensed under the AGPL

• Commercial licenses available

• On GitHub

• Contributions welcome

High Performance

• Written in C++

• Runs nearly everywhere

• Extensive use of memory-mapped files

• Data serialized as BSON (fast parsing)

• Full support for primary & secondary indexes

• Document model = less work

Full Featured

• Ad Hoc queries

• Real time aggregation

• Rich query capabilities

• Flexible schema

• Geospatial features

• Support for most programming languages

MongoDB drivers

• Official Support for 12 languages

• Community drivers for tons more

• Drivers connect to mongo servers

• Drivers translate BSON into native types

• Installed using typical means (npm, pecl, gem, pip)

Highly Available

Replica Sets

• Redundancy and Failover

• Multi-DC deployments

• Zero downtime for upgrades and maintenance

Horizontally Scalable

Sharding

• Partition your data

• Auto-balancing

• Scale write throughput

• Increase capacity

Document Database

RDBMS MongoDBRow ➜ DocumentTable, View

➜ Collection

Index ➜ IndexJoin ➜ Embedded

DocumentForeign Key

➜ Reference

Partition ➜ ShardTerminology

Typical (relational) ERD

MongoDB ERD

Example Document{

_id : ObjectId("4c4ba5c0672c685e5e8aabf3"),

author : "Blossom",

date : ISODate("2012-11-02T11:52:27.442Z"),

title: "Intro to MongoDB",

tags : [ "tech", "databases”, ”documents" ],

comments : [{

author : "Fred",

date : ISODate("2012-11-03T17:22:21.124Z"),

text : ”interesting article, lots of great nuggets of wisdom"

}],

comment_count : 1

}

Building a check-in web app

Requirements

• Users should be able to check in to a location

• Should be able to view check-ins by location

• Ability to assign categories to places

• Find nearby places

• Generate check-in statistics

First step in any application isDetermine your entities

Check-in App Entities

• People checking in

• Places to check in to

• Check-in events

In a relational based appWe would start by doing schema design

Relational schema design• Large entity relationship diagrams

• Complex create table statements

• Tables just to join tables together

• ORMs to map tables to objects

• Lots of revisions until we get it just right

In a MongoDB based appWe start building our appand let the schema evolve

MongoDB collections

• Users

• Places

• Check-ins– Separate collection to manage discrete check-in

events and support high workload of writes

We’ll use the JS shell to get started

> user = {

firstname: 'fred',

lastname: 'jones',

}

Start with an object (or array, hash, dict, etc)

> db.users.insert(user)

Insert the record

No collection creation needed

> db.users.findOne()

{

"_id" : ObjectId("50804d0bd94ccab2da652599"),

"firstname" : "fred",

"lastname" : "jones"

}

Querying for the user

_id

• _id is the primary key in MongoDB

• Automatically indexed

• Automatically created as an ObjectId if not provided

• Any unique immutable value could be used

ObjectId

• ObjectId is a special 12 byte value

• Guaranteed to be unique across your cluster

• ObjectId("50804d0bd94ccab2da652599") |---------||-----||-----||--------| ts mac pid inc

> db.places.insert({

name: “Exploratorium”,

address: “3601 Lyon St”

city: “San Francisco”,

postcode: 94123

}

Places v1

> db.places.insert({

name: “Exploratorium”,

address: “3601 Lyon St”

city: “San Francisco”,

postcode: 94123

}

> db.places.find({name:”Exploratorium”})

Places v1

> db.places.insert({

name: “Exploratorium”,

address: “3601 Lyon St”

city: “San Francisco”,

postcode: 94123,

categories: [“Social”, “Arts”, “Museums”]

}

Places v2

> db.places.insert({

name: “Exploratorium”,

address: “3601 Lyon St”

city: “San Francisco”,

postcode: 94123,

categories: [“Social”, “Arts”, “Museums”]

}

> db.places.ensureIndex({categories: 1})

> db.places.find({categories: ”Social”})

Places v2

> db.places.insert({

name: “Exploratorium”,

address: “3601 Lyon St”

city: “San Francisco”,

postcode: 94123,

categories: [“Social”, “Arts”, “Museums”],

location: [37.803963,-122.448531]

}

> db.places.ensureIndex({location: “2d”})

> db.places.find({location:{$near:[37.81,-122.45]}})

Places v3

Indexes

• Can be created on any field– Including fields within sub-documents or

embedded arrays

• Support for compound indexes on any combination of keys

• Indexes should exist for primary, common, and user-facing queries

// creating indexes

> db.places.ensureIndex({name:1})

> db.places.ensureIndex({categories:1})

> db.places.ensureIndex({location:”2d”})

// finding by regular expression

> db.places.find({name:/^explor/i})

// finding nearby

> db.places.find({location:{$near:[37.81,-122.45]}})

// finding by category

> db.places.find({categories: ”Social”})

Indexes on Places

Updating Documents

• MongoDB supports atomic, in-place document updates

• $inc

• $set, $unset

• $push, $pushAll

• $addToSet, $each

• $pop, $pull, $pullAll

• $rename

• $bit

> db.checkins.insert({

place_id: pid,

user_id: uid,

datetime: new Date()

})

// update the “checkins” count on place and user

// “checkins” will be added if it doesn’t exist

> db.places.update({_id: pid}, {$inc: {checkins: 1}})

> db.users.update({_id: uid}, {$inc: {checkins: 1}})

Updating

// find the last 10 checkins for a place:

> p = db.places.find({name:”Exploratorium”})

> db.checkins.find({place_id:p[‘_id’]})

.sort({datetime:-1}).limit(10)

// how many people checked in today

> db.checkins.find({place_id:p[‘_id’],

datetime: {$gt: midnight}}).count()

Simple Statistics

Aggregation Framework

• Documents pass through a pipeline of operations

• Mechanism to calculate aggregate values across your data

• Similar functionality to GROUP BY and related SQL operators

• Along with computation, data can be reshaped– Add computed fields– Create virtual sub-objects– Extract sub-fields

Aggregation Framework Operators

• $project

• $match

• $limit

• $skip

• $unwind

• $group

• $sort

// checkins per place per day of the week

> db.checkins.aggregate(

{$project: {

place_id:1,

dotw: { $dayOfWeek: ”$datetime”}

}},

{$group: {

_id: {place_id: “$place_id”, dotw: “$dotw”},

count: {$sum: 1}

}})

Advanced Statistics

Deployment

P

Deployment

• Single server- need a strong backup plan

Deployment

• Single server- need a strong backup plan

• Replica sets- High availability- Automatic failover

P

P S S

• Single server- need a strong backup plan

• Replica sets- High availability- Automatic failover

• Sharded- Horizontally scale- Auto balancing

Deployment

P S S

P S S

P

P S S

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