Getting Started with Graph Databases rik@neotechnology.com Agenda - - PowerPoint PPT Presentation

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Getting Started with Graph Databases rik@neotechnology.com Agenda - - PowerPoint PPT Presentation

Jul 15, 2023 341 likes •818 views

Getting Started with Graph Databases rik@neotechnology.com Agenda Introduction NO-SQL context What is Neo4j? When/why should I use it? Graph Queries Cypher query language Create and query data Graph Visualisations

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Getting Started with Graph Databases

rik@neotechnology.com

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Agenda

  • Introduction

– NO-SQL context – What is Neo4j? – When/why should I use it?

  • Graph Queries

– Cypher query language – Create and query data

  • Graph Visualisations
  • Case Studies
  • Q&A
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Introduction

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Not Only SQL

NOSQL is simply…

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What’s so bad about

Relational

VOLUME

Complexity

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The Relational Crossroads

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Four NOSQL Categories

arising from the “relational crossroads”

KV CF Doc

Graph

Denormalise Normalise

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So what is a graph database?

  • OLTP database

– “end-user” transactions

  • Model, store, manage data as a graph
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http://en.wikipedia.org/wiki/File:Leonhard_Euler_2.jpg

Meet Leonhard Euler

  • Swiss mathematician
  • Inventor of Graph

Theory (1736)

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Königsberg (Prussia) - 1736

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A B D C

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A B D C

1 2 3 4 7 6 5

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What is a graph?

Vertice Edge

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What is a graph?

Node

Relationship

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What are graphs good for?

Complexity

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Data Complexity

complexity = f(size, semi-structure, connectedness)

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Size

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complexity = f(size, semi-structure, connectedness)

The Real Complexity

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Semi-Structure

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Semi-Structure

Email: rik@neotechnology.com Email: rik@vanbruggen.be Twitter: @rvanbruggen Skype: rvanbruggen

USER CONTACT

CONTACT_TYPE

FIRST_NAME LAST_NAME USER_ID EMAIL_1 EMAIL_2 TWITTER FACEBOOK SKYPE Rik Van Bruggen 315

rik@neotechnology.com rik@vanbruggen.be @rvanbruggen

NULL

rvanbruggen
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complexity = f(size, semi-structure, connectedness)

The Real Complexity

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Examples of Connectedness

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Graphs Are Everywhere

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Neo4j is a Graph Database

  • JVM based
  • ACID transactions
  • Query language
  • Rich Java APIs
  • Using the Property Graph

model

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Describing Graphs

Brouweri j Bockor

8

BREWS HAS_ALCOHOL_PERCENTAGE

hoge gisting, blond

IS_A

NeoBeer

PRECEDES

BeerBrand Brewery

Alcohol Percentage

BeerType

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A Hairball!

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Querying a Graph

  • Graph local

– Contextualized “ego-centric” queries

  • “Parachute” into graph

– Start node(s)

  • Found through Index lookups
  • Crawl the surrounding graph

– 2 million+ joins per second

  • No more Index lookups:

Index-free adjacency

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Queries: Pattern Matching

Pattern

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Don’t worry (as much) about the

Start Node

Pattern

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Match

Pattern

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Match

Pattern

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Match

Pattern

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Non-Match

Pattern

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Non-Match

Pattern Not anchored to start node

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Describing Graphs

Brouweri j Bockor

8

BREWS HAS_ALCOHOL_PERCENTAGE

hoge gisting, blond

IS_A

NeoBeer

8.1 7.9

PRECEDES PRECEDES

BeerBrand Brewery

Alcohol Percentage Alcohol Percentage Alcohol Percentage

BeerType

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Graph Visualisations

  • Web Admin
  • Neography / Neovigator
  • Neoclipse
  • Gephi, Prefuse, d3.js, etc….
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Neo4j Web Admin

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Neoclipse

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Neovigator

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Other Visualisation options:

  • Linkurio.us
  • KeyLines
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Case Studies

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Contact Us

  • Neo Technology UK

5-11 Lavington Street London SE1 0NZ ENGLAND +44 808 189 0493

  • rik@neotechnology.com or
  • +32 478 686800