Graph Modelling
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The document discusses Neo4j, a graph database that utilizes a Cypher query language for data storage and management, distinguishing itself from traditional SQL databases. It outlines the structure of graphs, including nodes and edges, and highlights use cases, community support, and visualization tools available for Neo4j. The presentation concludes by emphasizing the efficacy of graphs in modeling various domains and encourages exploration of the Neo4j ecosystem.
![W H AT I S A G R A P H ?
{ (V, E) : V = [n], E ⊆ V(2) }](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-6-320.jpg)
![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”](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-7-320.jpg)
![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](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-8-320.jpg)
![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)](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-10-320.jpg)
![A S C I I A R T S Y N TA X : E D G E S
-[r:starred_in]->
<-[r:starred_in]-
-[r:starred_in]-](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-46-320.jpg)
![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)](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-47-320.jpg)
![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)
<-[r:starred_in]-(a:Actor)](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-48-320.jpg)
![M AT C H & R E T U R N
MATCH (n:Actor)-[r:starred_in]->(m:Movie)
RETURN n, r, m](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-50-320.jpg)
![M AT C H & R E T U R N
MATCH (n:Actor {name: ”Kevin Bacon”})
-[r:starred_in]->(m:Movie)
RETURN m](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-51-320.jpg)
![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](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-53-320.jpg)
![A G G R E G AT I O N
MATCH (n:Actor)<-[:starred_in]-(m:Movie)
RETURN n, sum(m.revenue)](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-54-320.jpg)
![L O A D F R O M C S V
LOAD CSV FROM 'foo.csv' AS line
CREATE (:Actor { name: line[1]})](https://image.slidesharecdn.com/graphmodelling-150603122954-lva1-app6892/85/Graph-Modelling-55-320.jpg)
Graph Modelling
- 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 Webber’s 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 Ö N I G S B E R G
- 14. B R I D G E S AT K Ö 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 …
- 20. 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
- 21. E L E C T I O N D ATA
- 22. E L E C T I O N D ATA
- 23. 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
- 24. 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
- 25. W O R L D ’ S L E A D I N G G R A P H D B :
- 26. 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.
- 27. "embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables"
- 28. D ATA S T O R A G E
- 29. D ATA S T O R A G E
- 30. 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
- 31. C O M M U N I T Y E D I T I O N • Free for hacking around in
- 32. 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
- 33. O T H E R G R A P H D ATA B A S E S • ArangoDB • OrientDB • New: Graph Engine
- 34. W H AT ’ S W R O N G W I T H S Q L ? B U T…
- 35. N O T H I N G *
- 36. N O T H I N G * *If you use it for the right job
- 37. 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)
- 38. 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
- 39. N E O 4 J U S E R S
- 40. C A S E S T U D I E S Real-time Recommendations
- 41. C A S E S T U D I E S Logistics & Delivery Organisation
- 42. C A S E S T U D I E S Online Dating
- 43. 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
- 44. W H AT I S C Y P H E R ? • Neo4j’s own query language • Declarative • Designed to be readable and easy to learn
- 45. 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”})
- 46. A S C I I A R T S Y N TA X : E D G E S -[r:starred_in]-> <-[r:starred_in]- -[r:starred_in]-
- 47. 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)
- 48. 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) <-[r:starred_in]-(a:Actor)
- 49. M AT C H & R E T U R N MATCH {pattern} RETURN {variables}
- 50. M AT C H & R E T U R N MATCH (n:Actor)-[r:starred_in]->(m:Movie) RETURN n, r, m
- 51. M AT C H & R E T U R N MATCH (n:Actor {name: ”Kevin Bacon”}) -[r:starred_in]->(m:Movie) RETURN m
- 52. P E R S I S T E N C E CREATE (n: Actor {name: “David”}) RETURN n
- 53. 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
- 54. A G G R E G AT I O N MATCH (n:Actor)<-[:starred_in]-(m:Movie) RETURN n, sum(m.revenue)
- 55. L O A D F R O M C S V LOAD CSV FROM 'foo.csv' AS line CREATE (:Actor { name: line[1]})
- 56. 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
- 57. 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
- 58. D I F F E R E N T L A N G U A G E S U P P O R T • Java • Spring Data for full ORM • Hibernate OGM • Embedded Java API • Kundera
- 59. D I F F E R E N T L A N G U A G E S U P P O R T • .NET - Neo4jClient • JavaScript - Seraph.js, node-neo4j • Clojure - Neocons • Haskell, Go, PHP and more…
- 60. G R A P H E N E D B • Remote hosting of neo4j on Heroku, AWS or Azure • Monitoring, support, back- ups, scalability
- 61. V I S U A L I S AT I O N T O O L S • Lots of tools out there to take subgraphs and turn them into pretty views.
- 62. V I S U A L I S AT I O N T O O L S : A L C H E M Y J S
- 63. V I S U A L I S AT I O N T O O L S : L I N K U R I O U S
- 64. G R A P H A WA R E • Java libraries that make developing with graphs easier: • “TimeTree” • “GraphGen” • “Reco”
- 65. I N C O N C L U S I O N … • Graphs more accurately model a lot of domains • Neo4j is a robust and mature way of storing this • It’s got a thriving ecosystem and community • Go forth and play!
- 66. A N Y Q U EST I O N S ? @ Swa m Wi t h Tu rt l e s s wa m w i t h t u rt l e s . co m