An Introduction to Mongo DB

Introduction to MongoDB Matthew Bates Solution Architect MongoDB EMEA

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MongoDB

The leading NoSQL database

Document Database

Open-Source

General Purpose

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Relational Database Challenges

Data Types

•  Unstructured data

•  Semi-structured data

•  Polymorphic data

Volume of Data

•  Petabytes of data

•  Trillions of records

•  Millions of queries per second

Agile Development

•  Iterative

•  Short development cycles

•  New workloads

New Architectures

•  Horizontal scaling

•  Commodity servers

•  Cloud computing

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Document Data Model

Relational - Tables { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {!

!type: “Point”, !coordinates :!! ![-0.128, 51.507]!!},!

cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }!}!

Document - Collections

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Dynamic Schema Documents

name: “jeff”, eyes: “blue”, height: 72, boss: “ben”}

{name: “brendan”, aliases: [“el diablo”]}

name: “ben”, hat: ”yes”}

{name: “matt”, pizza: “DiGiorno”, height: 72, boss: 555.555.1212}

{name: “will”, eyes: “blue”, birthplace: “NY”, aliases: [“bill”, “la ciacco”], gender: ”???”, boss: ”ben”}

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Document Model

•  Agility and flexibility – dynamic schema –  Data models can evolve easily

–  Companies can adapt to changes quickly

•  Intuitive, natural data representation –  Remove impedance mismatch

–  Many types of applications are a good fit

•  Reduces the need for joins, disk seeks –  Programming is more simple

–  Performance can be delivered at scale

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Simplify Development

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Simplify Development

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Rich Database Interaction

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MongoDB: An Operational Database

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Shell Command-line shell for interacting directly with database

Shell and Drivers

Drivers Drivers for most popular programming languages and frameworks

> db.collection.insert({company:“10gen”, product:“MongoDB”}) > > db.collection.findOne() {

“_id” : ObjectId(“5106c1c2fc629bfe52792e86”), “company” : “10gen” “product” : “MongoDB”

}

Java

Python

Perl

Ruby

Haskell

JavaScript

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•  Ad-hoc queries •  Real-time aggregation •  Rich query capabilities

•  Index support - secondary, compound, text, geo and more

•  Strong (traditional) consistency •  Geospatial features •  Support for most programming languages

•  Flexible schema

MongoDB is Full Featured

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Features In Practice

Queries •  Find Paul’s cars •  Find everybody in London with a car

built between 1970 and 1980

Geospatial •  Find all of the car owners within 5km of Trafalgar Sq.

Text Search •  Find all the cars described as having leather seats

Aggregation •  Calculate the average value of Paul’s car collection

Map Reduce •  What is the ownership pattern of colors

by geography over time? (is purple trending up in China?)

{ first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {!



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Query Example

Rich Queries •  Find Paul’s cars •  Find everybody in London with a car

built between 1970 and 1980

db.cars.find({ first_name: ‘Paul’!

}) db.cars.find({

!city: ‘London’, ”cars.year" : { $gte : 1970, $lte : 1980 }

}) !




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Geo Spatial Example

db.cars.find( { location:

{ $near : { $geometry : { type: 'Point' , coordinates :

[-0.128, 51.507] } },

$maxDistance :5000 } } ) !

Geospatial •  Find all of the car owners within 5km of Trafalgar Sq.




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Aggregation Framework Example

db.cars.aggregate( [

{$match : {"first_name" : "Paul"}}, {$project : {"first_name":1,"cars":1}}, {$unwind : "$cars"}, { $group : {_id:"$first_name", average : { $avg : "$cars.value"}}}

]) { "_id" : "Paul", "average" : 215000 } !

Aggregation •  Calculate the average value of Paul’s car collection




Scalability

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Automatic Sharding

•  Three types of sharding: hash-based, range-based, tag-aware

•  Increase or decrease capacity as you go

•  Automatic balancing

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Query Routing

•  Multiple query optimization models

•  Each sharding option appropriate for different apps

Availability

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Replica Sets

•  Replica Set – two or more copies

•  “Self-healing” shard

•  Addresses many concerns:

-  High Availability

-  Disaster Recovery

-  Maintenance

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Replica Set Benefits

Business Needs Replica Set Benefits

High Availability Automated failover

Disaster Recovery Hot backups offsite

Maintenance Rolling upgrades

Low Latency Locate data near users

Workload Isolation Read from non-primary replicas

Data Privacy Restrict data to physical location

Data Consistency Tunable Consistency

Performance

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Better Data Locality

Performance

In-Memory Caching

In-Place Updates

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Disk Seeks and Data Locality

Seek = 5+ ms Read = really really fast

User Comment

Article

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Disk Seeks and Data Locality

Article

User

Comment Comment Comment Comment Comment

Use Cases

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MongoDB Use Cases

Big Data Product & Asset Catalogs

Security & Fraud

Internet of Things

Database-as-a-Service

Mobile Apps

Customer Data Management

Data Hub

Social & Collaboration

Content Management

Intelligence Agencies

Top Investment and Retail Banks

Top US Retailer

Top Global Shipping Company

Top Industrial Equipment Manufacturer

Top Media Company

Top Investment and Retail Banks

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•  Document Model –  Simplify development –  Simplify scale out –  Improve performance

•  MongoDB – leading NoSQL database –  Rich general purpose database –  Fully featured –  Built-in HA (High Availability) and automated failover –  Built-in horizontal scale-out

Summary

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Getting Started

http://www.mongodb.org/downloads

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Online Documentation

http://docs.mongodb.org

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MongoDB University

http://university.mongodb.com

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For More Information

Resource Location

MongoDB Downloads mongodb.com/download

Free Online Training education.mongodb.com

Webinars and Events mongodb.com/events

White Papers mongodb.com/white-papers

Case Studies mongodb.com/customers

Presentations mongodb.com/presentations

Documentation docs.mongodb.org

Additional Info [email protected]

Resource Location

An Introduction to Mongo DB

Technology

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