This document provides an introduction to graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for certain types of connected data. It uses social network and movie recommendation examples to demonstrate how to model and query data in a graph database using the Cypher query language.

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?

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 OrigenImpedance 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
FollowJohn 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
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;

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