Introduction: Relational to Graphs
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Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market. A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships. Ryan Boyd, Developer Relations at Neo4j Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
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Introduction: Relational to Graphs
- 1. RDBMS to Graphs Harnessing the Power of the Graph March 2015
- 2. Agenda • Origins of Neo4j • Benefits of Graphs • Your enterprise architecture & polyglot persistence • Query time! • Q&A
- 3. Neo Technology Overview Product • Neo4j - World’s leading graph database • 1M+ downloads, adding 50k+ per month • 150+ enterprise subscription customers including over 50 of the Global 2000 Company • Neo Technology, Creator of Neo4j • 80 employees with HQ in Silicon Valley, London, Munich, Paris and Malmö • $45M in funding from Fidelity, Sunstone, Conor, Creandum, Dawn Capital
- 4. Neo4j Adoption by Selected Verticals Financial Services Communications Health & Life Sciences HR & Recruiting Media & Publishing Social Web Industry & Logistics Entertainment Consumer Retail Information ServicesBusiness Services
- 5. How Customers Use Neo4j Network & Data Center Master Data Management Social Recom– mendations Identity & Access Search & Discovery GEO
- 6. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017” Neo4j Leads the Graph Database Revolution “Neo4j is the current market leader in graph databases.” “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” IT Market Clock for Database Management Systems, 2014 https://www.gartner.com/doc/2852717/it-market-clock-database-management TechRadar™: Enterprise DBMS, Q1 2014 http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801 Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates) http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/
- 7. Largest Ecosystem of Graph Enthusiasts • 1,000,000+ downloads • 20,000+ education registrants • 18,000+ Meetup members • 100+ technology and service partners • 150+ enterprise subscription customers including 50+ Global 2000 companies
- 8. High Business Value in Data Relationships Data is increasing in volume… • New digital processes • More online transactions • New social networks • More devices Using Data Relationships unlocks value • Real-time recommendations • Fraud detection • Master data management • Network and IT operations • Identity and access management • Graph-based search… and is getting more connected Customers, products, processes, devices interact and relate to each other Early adopters became industry leaders
- 9. Relational DBs Can’t Handle Relationships Well • Cannot model or store data and relationships without complexity • Performance degrades with number and levels of relationships, and database size • Query complexity grows with need for JOINs • Adding new types of data and relationships requires schema redesign, increasing time to market … making traditional databases inappropriate when data relationships are valuable in real-time Slow development Poor performance Low scalability Hard to maintain
- 10. Unlocking Value from Your Data Relationships • Model your data as a graph of data and relationships • Use relationship information in real- time to transform your business • Add new relationships on the fly to adapt to your changing business
- 11. Modeling as a Graph
- 12. The Whiteboard Model Is the Physical Model
- 13. CAR name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Property Graph Model Components Nodes • The objects in the graph • Can have name-value properties • Can be labeled Relationships • Relate nodes by type and direction • Can have name-value properties LOVES LOVES LIVES WITH PERSON PERSON
- 14. Relational Versus Graph Models Relational Model Graph Model KNOWS ANDREAS TOBIAS MICA DELIA Person FriendPerson-Friend ANDREAS DELIA TOBIAS MICA
- 15. Let’s Model! Customer, Supplier, and Product (Master Data) Orders (Activity)
- 16. The Domain Model
- 17. Except…
- 18. The Requisite Northwind Example! NOT JUST ANY
- 20. Building Relationships in Graphs SOLD Employee OrderOrder
- 24. Find the Join Tables
- 25. Simple Join Tables Becomes Relationships
- 26. Attributed Join Tables Become Relationships with Properties
- 27. Working Subset (Today’s Exercise)
- 30. Basic Query: Who do people report to? MATCH (:Employee{ firstName:“Steven”} ) -[:REPORTS_TO]-> (:Employee{ firstName:“Andrew”} ) REPORTS_TO Steven Andrew LABEL PROPERTY NODE NODE LABEL PROPERTY
- 31. Basic Query: Who do people report to? MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN *
- 32. Basic Query: Who do people report to?
- 33. Basic Query: Who do people report to?
- 34. MATCH (sub)-[:REPORTS_TO*0..3]->(boss), (report)-[:REPORTS_TO*1..3]->(sub) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, count(report) AS Total Express Complex Queries Easily with Cypher Find all direct reports and how many people they manage, up to 3 levels down Cypher Query SQL Query
- 35. “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10 to 100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” Volker Pacher Senior Developer
- 36. Who is in Robert’s (direct, upwards) reporting chain? MATCH p=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee) WHERE sub.firstName = ‘Robert’ RETURN p
- 37. Who is in Robert’s (direct, upwards) reporting chain?
- 38. Who’s the Big Boss? MATCH p=(e:Employee) WHERE NOT (e)<-[:REPORTS_TO]->() RETURN e.firstName as bigBoss
- 39. Who’s the Big Boss?
- 40. Product Cross-Sell MATCH (choc:Product {productName: 'Chocolade'}) <-[:PRODUCT]-(:Order)<-[:SOLD]-(employee), (employee)-[:SOLD]->(o2)-[:PRODUCT]->(other:Product) RETURN employee.firstName, other.productName, count(distinct o2) as count ORDER BY count DESC LIMIT 5;
- 42. High Performance
- 43. Neo4j Clustering Architecture Optimized for Speed & Availability at Scale 43 Performance Benefits • No network hops within queries • Real-time operations with fast and consistent response times • Cache sharding spreads cache across cluster for very large graphs Clustering Features • Master-slave replication with master re-election and failover • Each instance has its own local cache • Horizontal scaling & disaster recovery Load Balancer Neo4jNeo4jNeo4j
- 44. Getting Data into Neo4j Cypher-Based “LOAD CSV” Capability • Transactional (ACID) writes • Initial and incremental loads of up to 10 million nodes and relationships Command-Line Bulk Loader neo4j-import • For initial database population • For loads with 10B+ records • Up to 1M records per second 4.58 million things and their relationships… Loads in 100 seconds!
- 45. MIGRATE ALL DATA MIGRATE GRAPH DATA DUPLICATE GRAPH DATA Non-graph data Graph data Graph dataAll data All data Relational Database Graph Database Application Application Application Three Ways to Load Data into Neo4j
- 47. Data Storage and Business Rules Execution Data Mining and Aggregation Neo4j Fits into Your Enterprise Environment Application Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analytic Infrastructure Graph Compute Engine EDW … Data Scientist End User Databases Relational NoSQL Hadoop
- 49. Users Love Neo4j
- 50. Users Love Neo4j
- 51. Learn the Way of the Graph Quickly and Easily
- 52. Quick Start: Plan Your Project 1 2 3 4 5 6 7 8 Learn Neo4j Decide on Architecture Import and Model Data Build Application Test Application Deploy your app in as little as 8 weeks PROFESSIONAL SERVICES PLAN
- 53. There Are Lots of Ways to Easily Learn Neo4j
- 54. Summary Only Neo4j Unlocks the Value in Your Data Relationships Data is increasing in volume… • New digital processes • More online transactions • New social networks • More devices … and is getting more connected Customers, products, processes, devices interact and relate to each other
- 55. RDBMS to Graphs Harnessing the Power of the Graph End of Presentation
Notes de l'éditeur
- Presenter Notes - Higher Level Value Proposition Everyday, new data is being created at a volume never seen before. And we see that this data is getting even more connected. People communicating as customers, employees, friends, influencers. Customers purchasing products, services or content, expressing their likes and dislikes. Digitization of processes and more data elements for each step. And with Internet of Things (IoT), we have the same thing repeating but with machines talking to each other. There is tremendous value in the knowledge of this relationship information for real-time applications. Examples are Connect a user’s profile and purchases to other users and increase revenue through recommendations for new products and services Reimagine your master data - HR, Customer or Product as a connected model and identify ways to reach customers, improve their experience, identify the best people to staff on projects and more View your individual data elements as part of a process to determine fraud detection or process bottlenecks Companies like Google, LinkedIn and PayPal have done exactly that. Reimagine their data as a network (or a graph) and use the relationship information
- Presenter Notes - Challenges with current technologies? Database options are not suited to model or store data as a network of relationships Performance degrades with number and levels of relationships making it harder to use for real-time applications Not flexible to add or change relationships in realtime
- Presenter Notes - How does one take advantage of data relationships for real-time applications? To take advantage of relationships Data needs to be available as a network of connections (or as a graph) Real-time access to relationship information should be available regardless of the size of data set or number and complexity of relationships The graph should be able to accommodate new relationships or modify existing ones
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- It’s easy to learn Neo4j, especially when your team already knows SQL. We partner with you every step of the way in a professional services plan tailored to your needs.
- In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j