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Back to Basics 2016 : Webinar 1

Introduction to NoSQLJoe Drumgoole

Director of Developer Advocacy, EMEAMongoDB

@jdrumgoole

V1.0

Welcome!

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Course Agenda

Date Time Webinar05-May-2016 14:00 GMT Introduction to NoSQL24-May-2016 14.00 GMT Your First MongoDB Application14-Jun-2016 14:00 GMT Schema Design – Thinking in Documents05-July-2016 14:00 GMT Advanced Indexing : Text and Geo-Spatial Indexes14-July-2016 14:00 GMT Introduction to the Aggregation Framework11-Aug-2016 14:00 GMT Production Deployment

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Agenda for Today

• Why NoSQL• The different types of NoSQL database• Detailed overview of MongoDB• MongoDB data durability – Replica Sets• MongoDB scalability – Sharding• Q&A

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Relational

Expressive Query Language& Secondary Indexes

Strong Consistency

Enterprise Management& Integrations

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The World Has Changed

Data Risk Time Cost

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NoSQL

Scalability& Performance

Always On,Global Deployments

FlexibilityExpressive Query Language& Secondary Indexes

Strong Consistency

Enterprise Management& Integrations

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Nexus Architecture

Scalability& Performance

Always On,Global Deployments

FlexibilityExpressive Query Language& Secondary Indexes

Strong Consistency

Enterprise Management& Integrations

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Types of NoSQL Database

• Key/Value Stores• Column Stores• Graph Stores• Multi-model Databases• Document Stores

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Key Value Stores

• An associative array• Single key lookup• Very fast single key lookup• Not so hot for “reverse lookups”

Key Value

12345 4567.3456787

12346 { addr1 : “The Grange”, addr2: “Dublin” }

12347 “top secret password”

12358 “Shopping basket value : 24560”

12787 12345

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Revision : Row Stores (RDBMS)

• Store data aligned by rows (traditional RDBMS, e.g MySQL)• Reads retrieve a complete row everytime• Reads requiring only one or two columns are wasteful

ID Name Salary Start Date

1 Joe D $24000 1/Jun/1970

2 Peter J $28000 1/Feb/1972

3 Phil G $23000 1/Jan/1973

1 Joe D $24000 1/Jun/1970 2 Peter J $28000 1/Feb/1972 3 Phil G $23000 1/Jan/1973

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How a Column Store Does it

1 2 3

ID Name Salary Start Date

1 Joe D $24000 1/Jun/1970

2 Peter J $28000 1/Feb/1972

3 Phil G $23000 1/Jan/1973

Joe D Peter J Phil G $24000 $28000 $23000 1/Jun/1970 1/Feb/1972 1/Jan/1973

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Why is this Attractive?

• A series of consecutive seeks can retrieve a column efficiently• Compressing similar data is super efficient• So reads can grab more data off disk in a single seek• How do I align my rows? By order or by inserting a row ID• IF you just need a small number of columns you don’t need to

read all the rows• But:

– Updating and deleting by row is expensive• Append only is preferred• Better for OLAP than OLTP

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Graph Stores

• Store graphs (edges and vertexes)• E.g. social networks• Designed to allow efficient traversal• Optimised for representing connections• Can be implemented as a key value stored with the ability to store

links• If your use case is not a graph you don’t need a graph database

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Multi-Model Databases

• Combine multiple storage/access models• Often Graph plus “something else”• Fixes the “polyglot persistence” issue of keeping multiple

independent databases consistent• The “new new thing” in NoSQL Land• Expect to hear more noise about these kinds of databases

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Document Store• Not PDFs, Microsoft Word or HTML• Documents are nested structures created using Javascript Object Notation (JSON)

{ name : “Joe Drumgoole”,title : “Director of Developer Advocacy”,Address : {

address1 : “Latin Hall”,address2 : “Golden Lane”,eircode : “D09 N623”,

}expertise: [ “MongoDB”, “Python”, “Javascript” ],employee_number : 320,location : [ 53.34, -6.26 ]

}

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MongoDB Documents are Typed

{

name : “Joe Drumgoole”,

title : “Director of Developer Advocacy”,

Address : {

address1 : “Latin Hall”,

address2 : “Golden Lane”,

eircode : “D09 N623”,

}

expertise: [ “MongoDB”, “Python”, “Javascript” ],

employee_number : 320,

location : [ 53.34, -6.26 ]

}

Strings

Nested Document

Array

Integer

Geo-spatial Coordinates

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MongoDB Understands JSON Documents

• From the very first version it was a native JSON database• Understands and can index the sub-structures• Stores JSON as a binary format called BSON• Efficient for encoding and decoding for network transmission• MongoDB can create indexes on any document field• (We will cover these areas in detail later on in the course)

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Why Documents?• Dynamic Schema• Elimination of Object/Relational Mapping Layer• Implicit denormalisation of the data for performance

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Why Documents?• Dynamic Schema• Elimination of Object/Relational Mapping Layer• Implicit denormalisation of the data for performance

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MongoDB is Full Featured

Rich Queries

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

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 in China?)

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High Availability and Data Durability – Replica Sets

SecondarySecondary

Primary

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Replica Set Creation

SecondarySecondary

Primary

Heartbeat

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Replica Set Node Failure

SecondarySecondary

Primary

No Heartbeat

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Replica Set Recovery

SecondarySecondary

HeartbeatAnd Election

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New Replica Set – 2 Nodes

SecondaryPrimary

HeartbeatAnd New Primary

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Replica Set Repair

SecondaryPrimary

Secondary

Rejoin and resync

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Replica Set Stable

SecondaryPrimary

Secondary

Heartbeat

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Scalability with Sharding

Shard 1 Shard 2 Shard N

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Scalability with Sharding

• Shard key partitions the content• MongoDB automatically balances the cluster• Shards can be added dynamically to a live system• Rebalancing happens in the background• Shard key is immutable• Shard key can vector queries to a specific shard• Queries without a shard key are sent to all members

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Scalability with ShardingMongoS MongoS

Shard 1 Shard 2 Shard N

Shard Key

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Query Routing

• With a sharded cluster we use a routing layer to guide queries• We use a daemon called MongoS (Mongo Shard Router)• Daemon is stateless• Can run as many as required• Typically one per app server

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Summary

• Why NoSQL exists• The types of NoSQL database• The key features of MongoDB• Data durability in MongoDB• Scalability in MongoDB

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Next Webinar – Your First MongoDB Application

• 24th May 2016 – 14:00 GMT.• Make sure to register if you haven’t already• Learn how to build your first MongoDB application• Create databases and collections• Look at queries• Build indexes• Start to understand performance• Register at: http://bit.ly/1UA4BGM• Send feedback to back-to-basics@mongodb.com

Q&A