Application Development SeriesBack to Basics – Introduction

Daniel Roberts@dmroberts

#MongoDBBasics

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• About the Webinar Series

• Data Model

• Query Model

• Scalability

• Availability

• Deployment Architectures

• Performance

• Next Session

Introduction

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• Split into 2 sections– Application Development (4 parts)

• Schema Design• Interacting with the database query and update operators• Indexing• Reporting

– Operations (3 parts)• Deployment – scale out and high availability• Monitoring and performance tuning• Backup and recovery

Series Outline & Approach

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• Content Management System– Will utilise :

• Query & update operators• Aggregation Framework• Geospatial queries• Pre Aggregated reports for fast analytics• Polymorphic documents• And more…

• Take away framework

• An approach that you can reuse in your own applications

Application Overview

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• Virtual Genius Bar

– Use the chat to post questions

– EMEA Solution Architecture team are on hand

– Make use of them during the sessions!!!

Q & A

MongoDB

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Operational Database

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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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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

Query Model

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ShellCommand-line shell for interacting directly with database

Shell and Drivers

DriversDrivers 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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MongoDB is full featured

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: {

type: “Point”, coordinates :

[-0.128, 51.507]

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

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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

}})

{ 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, … } }}

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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.

{ 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, … } }}

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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

{ 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, … } }}

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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• High Availability – Ensure application availability during many types of failures

• Disaster Recovery – Address the RTO and RPO goals for business continuity

• Maintenance – Perform upgrades and other maintenance operations with no application downtime

Availability Considerations

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

• MongoDB– Rich general purpose database– Built in High Availability and Failover– Built in scale out

Summary

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• Matt Bates – Schema design for the CMS application

• Collections• Design decisions

– Application architecture• Example technologies• RESTful interface• We’ve chosen python for the examples

– Code Examples

Next Week – 6th February