Graph Databases, a little connected tour (Codemotion Rome)

Slides of my talk at Codemotion Rome 2014 http://rome.codemotionworld.com/2014/

Transcript

1. Graph Databases A little connected tour ! @fcofdezc
2. Francisco Fernández Castaño @fcofdezc Sw Engineer @biicode
3. Beginning
5. The old town of Königsberg has seven bridges: Can you take a walk through town, visiting each part of the town and crossing each bridge only once?
6. El origen G = (V, E)
8. What is a Graph DB?
9. Graph Nodes Relationships Properties Store Store Connect Have Have
10. Written in Java ACID Rest interface Cypher
11. Why NOSQL?
12. The value of Relational Databases
13. Ventajas de BD Relacionales Concurrencia Persistencia Integración Estándar Persistence
14. Ventajas de BD Relacionales Concurrencia Persistencia Integración Estándar Concurrency
15. Ventajas de BD Relacionales Concurrencia Persistencia Integración Estándar Integration
16. Ventajas de BD Relacionales Concurrencia Persistencia Integración Estándar Standard
17. inconveniences Relational DBs
18. El Origen Impedance Mismatch
19. class Client < ActiveRecord::Base has_one :address has_many :orders has_and_belongs_to_many :roles end
20. DesVentajas de BD Relacionales Fricción! Interoperabilidad Adaptación al cambio Escalabilidad No está destinada para ciertos escenarios Interoperability
21. Adaptation to changes
22. !Scalability
23. The traditional way in the context of connected data is artificial
24. Depth MySQL time (s) Neo4j time (s) Results 2 0.016 0.01 ~2500 3 30.267 0.168 ~110,000 4 1543.505 1.359 ~600,000 5 No Acaba 2.132 ~800,000 MySQL vs Neo4j * Neo4J in Action
25. Person Id Person 1 Frank 2 John .. … 99 Alice PersonFriend PersonID FriendID 1 2 2 1 .. … 99 2
26. O(log n)
27. O(1)
28. O(m log n)
29. O(m)
30. We can transform our domain model in a natural way
32. Use cases
33. Social Networks Follow Follow John Jeff Douglas
34. Geospatial problems Fraud detection Authorization Network management
35. Cypher Declarative language ASCII oriented Pattern matching
36. Cypher Cypher Traverser API Core API Kernel
37. Cypher a b (a)-->(b)
38. Cypher clapton cream (clapton)-[:play_in]->(cream) play_in
39. Follow Follow John Jeff Douglas Cypher (john:User)-[:FOLLOW]->(jeff:User) ! (douglas:User)-[:FOLLOW]->(john:User)
40. Cypher clapton {name: Eric Clapton} cream (clapton)-[:play_in]->(cream)<-[:labeled]-(blues) play_in {date: 1968} Blues labeled
41. Cypher MATCH (a)-—>(b) RETURN a,b;
42. Cypher MATCH (a)-[:PLAY_IN]—>(b) RETURN a,b;
43. Cypher MATCH (a)-[:PLAY_IN]—>(g), (g)<-[:LABELED]-(e) RETURN a.name, t.date, e.name;
44. Cypher MATCH (c {name: ‘clapton’})-[t:PLAY_IN]—>(g), (g)<-[:LABELED]-(e) RETURN c.name, t.date, e.name;
45. Cypher MATCH (c {name: ‘clapton’})-[t:PLAY_IN]—>(g), (g)<-[:LABELED]-(e {name: ‘blues’}) RETURN c.name, e.name ORDER BY t.date
46. Cypher MATCH (c {name: ‘clapton’})-[r:PLAY_IN | PRODUCE]—>(g), (g)<-[:LABELED]-(e {name: ‘blues’}) RETURN c.name, e.name WHERE r.date > 1968 ORDER BY r.date
47. Cypher MATCH (carlo)-[:KNOW*5]—>(john)
48. MATCH p = (startNode:Station {name: ‘Sol’}) -[rels:CONNECTED_TO*]-> (endNode:Station {name: ‘Retiro’}) RETURN p AS shortestPath, reduce(weight=0, r in rels: weight + r.weight) as tWeight ORDER BY tWeight ASC LIMIT 1
49. Recommendation System
50. Social network
51. Movies social network Users rate movies People act in movies People direct movies Users follow other users
52. Movies social network How do we model it?
53. Movies social network Follow Rate {stars} User Film User Actor Director Act in Direct
54. Movies social network MATCH (fran:User {name: ‘Fran’}) -[or:Rate]-> (pf:Film {title: ‘Pulp Fiction’}), ! (pf)<-[:Rate]-(other_users)-[:Rate]->(other_films) ! RETURN distinct other_films.title;
55. Movies social network Rate {stars} Rate {stars} User 1 Film PF Fran User 2 Rate {stars} Film Film Rate {stars} Rate {stars}
56. Movies social network MATCH (fran:User {name: ‘Fran’}) -[or:Rate]-> (pf:Film {title: ‘Pulp Fiction’}), ! (pf)<-[:Rate]-(other_users)-[r:Rate]->(other_films) ! WHERE or.stars = r.stars ! RETURN distinct other_films.title;
57. Movies social network MATCH (fran:User {name: ‘Fran’}) -[or:Rate]-> (pf:Film {title: ‘Pulp Fiction’}), ! (pf)<-[:Rate]-(other_users)-[r:Rate]->(other_films), ! (other_users)-[:FOLLOW]-(fran) ! WHERE or.stars = r.stars ! RETURN distinct other_films.title;
58. Movies social network Rate {star} User 1 Film PF Fran Rate {stars} Film Follow Rate {star}
59. Movies social network MATCH (tarantino:User {name: ‘Quentin Tarantino’}), (tarantino)-[:DIRECT]->(movie)<-[:ACT_IN]-(tarantino) RETURN movie.title
60. Movies social network Film Actor Director Act_in Direct
61. Movies social network Now you should be able to categorize the movies
62. Movies social network Film SubGenre Belongs_to SubGenre Belongs_to Genre Genre Belongs_to Belongs_to
63. Movies social network MATCH (fran:User {name: ‘Fran’}) -[or:Rate]-> (pf:Film {title: ‘Pulp Fiction’}), ! (pf)<-[:Rate]-(other_users)-[r:Rate]->(other_films), (film)->[:BELONGS_TO*3]->(genre)<-[:BELONGS_TO]-(other_films), ! (other_users)-[:FOLLOW]-(fran) ! WHERE or.stars = r.stars ! RETURN distinct other_films.title;
64. Neo4J extensions Managed Unmanaged
65. Neo4J extensions Managed Unmanaged
66. Neo4J extensions Managed Unmanaged
67. Drivers/Clients
68. Instead of just picking a relational database because everyone does, we need to understand the nature of the data we’re storing and how we want to manipulate it. Martin Fowler
69. References
70. Neo4J as a service http://www.graphenedb.com
72. Grazie