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 â 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 â V(2) }
Made up of two parts,
âVâ and âEâ](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 â 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 â 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: âKevin Baconâ})
-[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 =âDavidâ})
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 ? âą David Simons âą @SwamWithTurtles âą github.com/ SwamWithTurtles âą Technical Lead at Softwire and part-time hacker âą 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 Webberâs Dr. Who Dataset
- 6. W H AT I S A G R A P H ? { (V, E) : V = [n], E â V(2) }
- 7. W H AT I S A G R A P H ? { (V, E) : V = [n], E â V(2) } Made up of two parts, âVâ and âEâ
- 8. W H AT I S A G R A P H ? { (V, E) : V = [n], E â 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 â 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 Ă N I G S B E R G
- 14. B R I D G E S AT K Ă 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 âŠ
- 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 â 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 ? âą 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 âą Nodes and edges are all: âą Stored as first-class objects on the file system âą âtypedâ âą Key-value stores
- 31. C O M M U N I T Y E D I T I O N âą Free for hacking around in
- 32. E N T E R P R I S E E D I T I O N âą Bespoke Prices, but includes: âą Higher performance for concurrent querying âą Clustering âą Hot backups âą Advanced Monitoring
- 33. O T H E R G R A P H D ATA B A S E S âą ArangoDB âą OrientDB âą New: Graph Engine
- 34. W H AT â S W R O N G W I T H S Q L ? B U TâŠ
- 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 âą âJoinsâ are first class objects in the database that can be queried at no additional cost âą Certain queries become trivial (e.g. Joins)
- 38. P R O T O T Y P I N G âą Easy to see and work with data âą Schemaless âą 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 ? âą Neo4jâs own query language âą Declarative âą 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:âKevin Baconâ})
- 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: âKevin Baconâ}) -[r:starred_in]->(m:Movie) RETURN m
- 52. P E R S I S T E N C E CREATE (n: Actor {name: âDavidâ}) RETURN n
- 53. P E R S I S T E N C E MATCH (m:Movie), (a:Actor {name =âDavidâ}) 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 ? â Cy p h e r â Q u e ry La n g u a g e Le t âs 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 âą Java âą Spring Data for full ORM âą Hibernate OGM âą Embedded Java API âą 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 âą .NET - Neo4jClient âą JavaScript - Seraph.js, node-neo4j âą Clojure - Neocons âą Haskell, Go, PHP and moreâŠ
- 60. G R A P H E N E D B âą Remote hosting of neo4j on Heroku, AWS or Azure âą Monitoring, support, back- ups, scalability
- 61. V I S U A L I S AT I O N T O O L S âą 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 âą Java libraries that make developing with graphs easier: âą âTimeTreeâ âą âGraphGenâ âą âRecoâ
- 65. I N C O N C L U S I O N ⊠⹠Graphs more accurately model a lot of domains âą Neo4j is a robust and mature way of storing this âą Itâs got a thriving ecosystem and community âą 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