Graph Databases: Solving Problems with Connected Data

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Jennifer Reif Email: [email protected] Twitter: @JMHReif LinkedIn: linked.com/in/jmhreif Github: github.com/JMHReif Website: jmhreif.com Graph Databases Solving Problems with Connected Data

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Who Am I? • Developer + Advocate • Continuous learner • Blogger • Speaker • Other: geek Jennifer Reif Email: [email protected] Twitter: @JMHReif LinkedIn: linked.com/in/jmhreif Github: github.com/JMHReif Website: jmhreif.com

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It’s all about data Everything is built on data • Businesses, industry, governments, economics, people • Our understanding of the world • Data is how we learn • Trial and error • Asking questions • Accumulated over time

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Domain-Driven Design (DDD) • Mutual understanding of domain, concepts • Iterations • Understanding data = better understanding of result • Better product, minimum functionality • Sanity check code

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Databases - another one?!

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Relational database • Pros: • Slices of data that can be easily assembled with queries • Handles data integrity and unique rows, low data duplication • Cons: • All relationships must be assembled with JOINs (with NF adherence) • JOINs are constructed from human knowledge Goal: Assemble data into meaningful sets

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Entity-Relationship Diagram

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NoSQL/Document database • Pros: • Can handle varied (unstructured) data more readily • Groups related info in a single place • Cons: • Designed to avoid JOINs and SQL • Makes aggregation operations cumbersome Goal: Group related info together

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NoSQL/Document Schema

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Graph database Goal: Best of both worlds • Pros: • Relationships stored with related entities, no assembly required • Make JOIN operations easy, visual even • Cons: • Storing relationships requires some write operation overhead • Di ff erent mentality for modeling, querying, etc

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Graph databases

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Have to pick just one? • Polyglot persistence: • Using multiple persistence strategies, each serving a di ff erent need • “Using the right tool for the job”

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What is a graph?

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Graph History Leonard Euler Seven Bridges of Konigsberg problem. Leonhard Euler, 1735

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Graph databases 2010 New tech arises from… • Solution need • Missing solution in the technology space • Looking for a “better way” to handle certain problems

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What Does It Solve? Product use case • Document path (not just data) • Answering how and why • Understanding connections • Find alternates, impacts, etc. • Produce better assumptions, decisions

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Graphs add context and meaning Not just point-to-point, but HOW they connect

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Misconceptions “Gotchas” • Database - physical storage of data • Not just analysis tool • Not just visualization • Can be “system of record” / “source of truth” db • Not just analytics workloads, transactional too • Not a silver bullet (like any technology) • There are things it does NOT do well

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Graph Structure

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Whiteboard Friendliness Easy to design and model direct representation of the data

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Data model drawn = real-world data model

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Data model drawn = data stored in graph

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Graph components • Node (vertex) • Relationship (edge)

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Nodes Graph Components • Represent objects or entities • Can be labeled Order Product

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Nodes Graph components • Represent objects or entities • Can be labeled • May have properties Order Product Employee orderId: 162468 total: $8320.48 productId: 08746589 name: “Ipoh Coffee” id: 247924 startDate: 2016-05-04 role: “Salesperson”

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Relationships Graph components • Must have a type • Must have a direction Order Product Employee orderId: 162468

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Relationships Graph components • Must have a type • Must have a direction • May have properties Order Product Employee orderId: 162468 commission: .02

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Relationships Graph components • Must have a type • Must have a direction • May have properties • Nodes can share multiple relationships Order Product Employee orderId: 162468

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Label Graph Components • A group of nodes • Like a category Person

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Label Graph Components • A group of nodes • Like a category Person Employee Customer

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Property Graph components • Key-value pair • Associated with node or relationship • Like a variable name and value Order Employee orderId: 162468 id: 247924 SOLD total: $8320.48 date: 2022-08-16 commission: .02 startDate: 2016-05-04 role: “Salesperson”

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Query Language

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SQL vs Cypher SELECT o.OrderId, o.Total, o.Items, op.Quantity, op.Price, p.Name FROM Order o LEFT JOIN OrderedProduct op ON op.OrderId = o.OrderId LEFT JOIN Product p ON op.ProductId = p.ProductId; SQL Cypher MATCH (o:Order)-[r:CONTAINS]->(p:Product) RETURN o, r, p; Order Product OrderedProduct Order Product

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Cypher • Functional and visual • Based on ASCII-art • Declarative query language • Focuses on what to retrieve • Not how A B LIKES MATCH (A)-[:LIKES]->(B)

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Cypher: powerful and expressive Jennifer Neo4j WORKS_FOR CREATE (:Person { name: ‘Jennifer’}) -[:WORKS_FOR]-> (:Company { name: ‘Neo4j’}) NODE PROPERTY NODE PROPERTY LABEL LABEL

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Cypher: read Jennifer Neo4j WORKS_FOR MATCH (:Person { name: ‘Jennifer’} ) -[:WORKS_FOR]-> ( whom )   RETURN whom

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Cypher in 20 seconds… • Nodes look like this: • (var:Label) OR (var:Label {propKey: propValue}) • Relationships look like this: • -[var:REL_TYPE]-> OR -[var:REL_TYPE {propKey: propValue}]-> • Using Cypher is looking for patterns of those nodes/rels: • (var1:Label)-[var2:REL_TYPE]->(var3:Label)

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Building Applications

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Building applications • Drivers: lots of languages supported • Plugins/extensions/libraries: • APOC utilities • Graph Data Science • GraphQL • …and more! • Integrations/frameworks: • Spring, Micronaut, Quarkus • Kettle, Spark, PowerBI, JDBC, etc.

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Demo!

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Resources • Neo4j AuraDB Free: dev.neo4j.com/aura-java • Neo4j GraphAcademy: dev.neo4j.com/learn • Neo4j blog: dev.neo4j.com/blog • Neo4j YouTube: dev.neo4j.com/videos Jennifer Reif Email: [email protected] Twitter: @JMHReif LinkedIn: linked.com/in/jmhreif Github: github.com/JMHReif Website: jmhreif.com