Getting Started with Graph Databases
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This document provides an overview of graph databases, comparing them to traditional relational databases and highlighting their structure using vertices and edges to represent entities and relationships. It discusses the flexibility and efficiency of graph databases in handling complex relationships and dynamic data without rigid schema requirements. Additionally, it outlines practical use cases, such as recommendation engines and fraud detection, alongside technology insights related to Titan and DSE Graph.
Getting Started with Graph Databases
- 1. ©2013 DataStax Confidential. Do not distribute without consent. @rustyrazorblade Jon Haddad Technical Evangelist, DataStax Graph Databases 101 1
- 2. A Quick Look at Relational View the Full-Length Video & Tutorial https://academy.datastax.com/demos/ getting-started-graph-databases
- 3. Tables • Very structured data • Relational calculus • Various normal forms id name title country 1 jon evangelist usa 2 patrick chief evangelist usa 3 rebecca jr evangelist canada 4 billy ceo usa
- 4. Selecting and Filtering id name title country 1 jon evangelist usa 2 patrick chief evangelist usa 3 rebecca jr evangelist canada 4 billy ceo usa select * from people where country = 'canada';
- 5. Foreign Keys id name title country 1 jon evangelist 1 2 patrick chief evangelist 1 3 rebecca jr evangelist 2 4 billy ceo 1 id name 1 usa 2 canada 3 germany 4 japan
- 6. Joins select * from people join country on people.country = country.id; id name title country name 1 jon evangelist 1 usa 2 patrick chief evangelist 1 usa 3 rebecca jr evangelist 2 canada 4 billy ceo 1 usa
- 7. Many to Many Relationships id name title 1 jon evangelist 2 patrick chief evangelist 3 rebecca jr evangelist 4 billy ceo id name 1 usa 2 canada 3 germany 4 japan user country 1 1 1 2 2 1 3 2 4 1
- 8. Joining select * from people join people_country on people.id = people_country.user join country on people_country.country = country.id; id name title country name 1 jon evangelist 1 usa 1 jon evangelist 2 canada 2 patrick chief evangelist 1 usa 3 rebecca jr evangelist 2 canada 4 billy ceo 1 usa
- 9. A Fun, Real World Example
- 11. Problems • Every entity gets a table • Lots of many to many tables • Very rigid structure • Going from one to many requires a migration and new data model
- 12. How is this Solved with a Graph?
- 13. Vertex • A vertex represents a "thing" • For example: Movie, Person JCVD Time cop
- 14. JCVD Time cop Edge • Edges are labeled relationships • Edges have direction ActedIn
- 15. Edges are always many to many JCVD Time copActedIn Blood sport ActedIn
- 16. Properties • Similar to fields in a table • Much more flexible • Meta properties - properties on properties • Can be on an edge or a vertex • Special property - a label JCVD status amazing charm infinite odds of rendezvous 99.6% enemies decapitated 108747
- 17. Tinkerpop 3 • Cross database graph query language • API for graph • Gremlin Server
- 18. Adding Data 1 graph = TitanFactory.build().set('storage.backend', 'inmemory').open() 2 3 g = graph.traversal() 4 5 jcvd = graph.addVertex(label, "actor", 6 "name", "jean claude van damme") 7 8 kick = graph.addVertex(label, "movie", "name", "Kickboxer", 9 "year", 1989) 10 11 blood = graph.addVertex(label, "movie", "name", "Bloodsport", 12 "year", 1988) 13 14 jcvd.addEdge("acted_in", kick) 15 jcvd.addEdge("acted_in", blood)
- 19. Summary • We don't define tables • We create vertices to represent real world objects • Relationships don't need to be explicitly modeled • Expressing complex relationships is not hard • Adding new edge types is easy
- 21. Finding Vertices single vertex g.V(4160) matching a property g.V().has("name", "jean claude van damme") range filtering g.V().has("year", between(1980, 1990))
- 22. Traversals Bloodsport (1988) Kickboxer (1989) Timecop (1994)jcvd.out()
- 23. Traversals Bloodsport (1988) Kickboxer (1989) Timecop (1994) jcvd.outE()
- 24. Traversals to other vertices g.V().has("name", "jean claude van damme").out() to edges g.V().has("name", "jean claude van damme").outE() filtering with traversals g.V().has("name", "jean claude van damme").out(). has("year", between(1980, 1990))
- 26. Use Cases
- 27. Recommendation Engines • What do I like? • What do others like? • What do others listen to? • Find correlations
- 28. Fraud & Anomaly Detection
- 30. Titan • Open Source • Works with Cassandra to scale to massive graphs • OLTP + OLAP graph queries via Spark • Works with Solr or Elastic Search
- 31. DSE Graph • Next generation of distributed graph • Benefits of Titan but tightly integrated with DataStax Enterprise
- 32. ©2013 DataStax Confidential. Do not distribute without consent. 32