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Graph Databases, a little connected tour (Codemotion Rome)
- 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 origenG = (V, E)
- 8. What is a Graph DB?
- 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
- 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
- 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
- 34. Geospatial problems Fraud detection Authorization Network management
- 35. Cypher Declarative language ASCII oriented Pattern matching
- 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
- 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
- 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?
- 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 GenreGenre Belongs_toBelongs_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;
- 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