There are many kinds of NoSQL databases like, document databases, key-value, column databases and graph databases. In some scenarios is more convenient to store our data as a graph, because we want to extract and study information relative to these connections. In this scenario, graph databases are the ideal, they are designed and implemented to deal with connected information in a efficient way. https://ep2014.europython.eu/en/schedule/sessions/70/

1. Graph Databases
A little connected tour
Francisco Fernández Castaño
!
@fcofdezc

2. Beginning

4. 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?

5. El origenG = (V, E)

7. What is a Graph DB?

8. Graph
Nodes Relationships
Properties
Store
Store
Connect
Have
Have

9. Written in Java
ACID
Rest interface
Cypher

10. Why Graph DB?

11. The traditional way in the context
of connected data is artificial

12. !Scalability

13. 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

14. Person
Id Person
1 Frank
2 John
.. …
99 Alice
PersonFriend
PersonID FriendID
1 2
2 1
.. …
99 2

15. O(log n)

16. O(1)

17. O(m log n)

18. O(m)

19. We can transform our domain model in a
natural way

21. Use cases

22. Social Networks
Follow
Follow
John Jeff
Douglas

23. Geospatial problems
Fraud detection
Authorization
Network management

24. Recommendation
System

25. Cypher
Declarative language
ASCII oriented
Pattern matching

26. Cypher
Cypher
Traverser API
Core API
Kernel

27. Cypher
a b
(a)-->(b)

28. Cypher
clapton cream
(clapton)-[:play_in]->(cream)
play_in

29. Follow
FollowJohn Jeff
Douglas
Cypher
(john:User)-[:FOLLOW]->(jeff:User)
!
(douglas:User)-[:FOLLOW]->(john:User)

30. Cypher
clapton
{name: Eric
Clapton}
cream
(clapton)-[:play_in]->(cream)<-[:labeled]-(blues)
play_in
{date: 1968}
Blues
labeled

31. Cypher
MATCH (m)-[:PLAY_IN]—>(b)
RETURN m,b;

32. Cypher
MATCH (m)-[:PLAY_IN]—>(b),
(b)<-[:LABELED]-(s)
RETURN m.name,
t.date,
s.name;

33. Cypher
MATCH (c {name: ‘clapton’})-[t:PLAY_IN]—>(b),
(b)<-[:LABELED]-(s)
RETURN c.name,
b.name,
s.name;

34. Cypher
MATCH (c {name: ‘clapton’})-[t:PLAY_IN]—>(b),
(b)<-[:LABELED]-(e {name: ‘blues’})
RETURN c.name,
b.name,
e.name
ORDER BY t.date

35. Cypher
MATCH (c {name: ‘clapton’})-[r:PLAY_IN | PRODUCE]—>(b),
(b)<-[:LABELED]-(e {name: ‘blues’})
RETURN c.name,
b.name,
e.name
WHERE r.date > 1968
ORDER BY r.date

36. Cypher
MATCH (a)-[:KNOW*5]—>(b)

37. 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

38. Neo4J extensions
Managed
Unmanaged

39. Neo4J extensions
Managed
Unmanaged

40. Neo4J extensions
Managed
Unmanaged

41. Drivers/Clients

42. 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

43. References

44. Neo4J as a service
http://www.graphenedb.com

46. Danke