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Graph Modelling

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1. G R A P H DATA B A S ES 2. W H O A M I ? David Simons @SwamWithTurtles github.com/ SwamWithTurtles Technical Lead at Softwire and part-time hacker Statistician in a past life 3. G ra p h i ca l M o d e l l i n g N e o 4 J : T h e W h at A n d W h y ? Cy p h e r Q u e ry La n g u a g e Le t s S e e I t A ct i o n ! T h e G ra p h i n g Eco s y ste m 4. G ra p h i ca l M o d e l l i n g N e o 4 J : T h e W h at A n d W h y ? Cy p h e r Q u e ry La n g u a g e Le t s S e e I t A ct i o n ! T h e G ra p h i n g Eco s y ste m 5. W H AT I S A G R A P H ? Taken from Jim Webbers Dr. Who Dataset 6. W H AT I S A G R A P H ? { (V, E) : V = [n], E V(2) } 7. W H AT I S A G R A P H ? { (V, E) : V = [n], E V(2) } Made up of two parts, V and E 8. W H AT I S A G R A P H ? { (V, E) : V = [n], E V(2) } V is a set of n items 9. W H AT I S A G R A P H ? Vertex Set 10. W H AT I S A G R A P H ? { (V, E) : V = [n], E V(2) } E is made up of pairs of elements of V (Ordered and not necessarily distinct) 11. W H AT I S A G R A P H ? Edge Set 12. G I V I N G R E A L W O R L D M E A N I N G S T O V A N D E W H A T I S G R A P H I C A L M O D E L L I N G ? 13. B R I D G E S AT K N I G S B E R G 14. B R I D G E S AT K N I G S B E R G V = bits of land E = bridges 15. K E V I N B A C O N S I X D E G R E E S O F 16. T H E R E I S N O O P E N E L E C T I O N D ATA T H E P R O B L E M 17. E L E C T I O N D ATA 18. E L E C T I O N D ATA 19. E L E C T I O N D ATA E = (e.g.) member of, held in, stood in V = elections, constituencies, years, politicians and parties 20. G ra p h i ca l M o d e l l i n g N e o 4 J : T h e W h at A n d W h y ? Cy p h e r Q u e ry La n g u a g e Le t s S e e I t A ct i o n ! T h e G ra p h i n g Eco s y ste m 21. W O R L D S L E A D I N G G R A P H D B : 22. W H E R E D I D I T C O M E F R O M ? First version 2010, v2 came out December 2013. 23. "embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables" 24. D ATA S T O R A G E 25. D ATA S T O R A G E 26. D ATA S T O R A G E Nodes and edges are all: Stored as first-class objects on the file system typed Key-value stores 27. C O M M U N I T Y E D I T I O N Free for hacking around in 28. E N T E R P R I S E E D I T I O N Bespoke Prices, but includes: Higher performance for concurrent querying Clustering Hot backups Advanced Monitoring 29. O T H E R G R A P H D ATA B A S E S ArangoDB OrientDB New: Graph Engine 30. W H AT S W R O N G W I T H S Q L ? B U T 31. N O T H I N G * 32. N O T H I N G * *If you use it for the right job 33. D ATA I N T H E R E L AT I O N S Joins are first class objects in the database that can be queried at no additional cost Certain queries become trivial (e.g. Joins) 34. P R O T O T Y P I N G Easy to see and work with data Schemaless Active community with a lot of libraries 35. N E O 4 J U S E R S 36. C A S E S T U D I E S Real-time Recommendations 37. C A S E S T U D I E S Logistics & Delivery Organisation 38. C A S E S T U D I E S Online Dating 39. G ra p h i ca l M o d e l l i n g N e o 4 J : T h e W h at A n d W h y ? Cy p h e r Q u e ry La n g u a g e Le t s S e e I t A ct i o n ! T h e G ra p h i n g Eco s y ste m 40. W H AT I S C Y P H E R ? Neo4js own query language Declarative Designed to be readable and easy to learn 41. A S C I I A R T S Y N TA X : N O D E S (n) (n:Actor) (n:Actor {name:Kevin Bacon}) 42. A S C I I A R T S Y N TA X : E D G E S -[r:starred_in]-> (m:Movie) 44. A S C I I A R T S Y N TA X : E D G E S (n:Actor)-[r:starred_in]->(m:Movie) (m:Movie) RETURN n, r, m 47. M AT C H & R E T U R N MATCH (n:Actor {name: Kevin Bacon}) -[r:starred_in]->(m:Movie) RETURN m 48. P E R S I S T E N C E CREATE (n: Actor {name: David}) RETURN n 49. P E R S I S T E N C E MATCH (m:Movie), (a:Actor {name =David}) CREATE (a)-[:starred_in]->(m) RETURN a, m

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