This document discusses using Neo4j graph databases for projects instead of traditional SQL databases. It notes that graph databases better model complex relationships, scale to large amounts of data more efficiently, and allow for faster querying of connected data through graph traversal compared to SQL databases which require complex joins. Examples are provided showing how queries involving friends of friends or finding connection paths between characters can be done more simply and quickly using the Cypher query language in Neo4j compared to equivalent SQL queries. A demo is also promised to illustrate these benefits hands-on.

1. Use Neo4j In Your
Next Java Project
Love relationships again
Tobias Coetzee
@tobiascode

2. All Roads Lead To SQL
Do we even have
other choices?
Every project we work on will have
some form of database, 99% of
those in the enterprise will be a
relational database

3. Impedance Mismatch
The database model does not
match the domain model
Not Scaling
More data means less speed
Complex Queries
Lots of joins and some self
joining nightmares as a bonus
Relationship Problems

4. Facebook For Superheroes
MEMBER_OF
TOOK_PART_IN
WAS_IN
Characters
Events
Teams
Stories
CONSISTS_OF
FRIEND_OF

5. Queries with lots of joins to get to the final answer
Similar Data Models
Configuration
Security
Navigation

6. Graph Databases
Just Mathematics
Based on graph theory, created
by Leonhard Euler
All About Relationships
In graph databases relationships
are first class citizens
Neo4j
Most well known and widely
used graph database

7. Graph Databases
Nodes
Represent the entities or
records in the database
Relationships
Link nodes together
Properties
Nodes and relationships can
also have properties

8. MEMBER_OF
MEMBER_OF
FRIEND_OF
WAS_IN
TOOK_PART_IN • Eyes: Blue
• Hair: None
• Citizenship: Canada• Since: 14 Feb 2001
Graph Databases

9. The database model does not
match the domain model
Impedance Mismatch

10. Impedance Mismatch
Character_Team
Character_Event
Team_Event
Character_Story
Team_Story
Event_Story
Friends
MarvelCharacter
Team
Event
Story

11. Traditional Solutions
ORM Frameworks, e.g.
Hibernate or Spring Data
Boilerplate code generation
tools
Impedance Mismatch

12. Impedance Mismatch
MEMBER_OF
TOOK_PART_IN
WAS_IN
Characters
Events
Teams
Stories
CONSISTS_OF
FRIEND_OF

13. MATCH
(c:Character)-[:MEMBER_OF]->(t:Team)
WHERE t.name = 'Avengers'
RETURN c.name
Cypher Intro

14. Demo Time

15. Lots of joins and some self
joining nightmares as a bonus
Complex Queries

16. Denormalise the database
Hide complexity behind
views and stored procedures
Traditional Solutions
Complex Queries

17. Friends of Friends
Name Number Friends
in Common
Vision 51
Wolfsbane 38
Punisher 33
Rage 38
Shard 25
Wind Dancer 25
Possible friend recommendations for Deadpool

18. SELECT FriendOfFriend.Name, COUNT(*)
FROM MarvelCharacter deadpool
INNER JOIN Friends DeadpoolFriends
ON deadpool.Id = DeadpoolFriends.CharacterId1
INNER JOIN Friends FriendsFriends
ON DeadpoolFriends.CharacterId2 = FriendsFriends.CharacterId1
INNER JOIN MarvelCharacter FriendOfFriend
ON FriendsFriends.CharacterId2 = FriendOfFriend.Id
WHERE deadpool.Name = 'Deadpool'
AND FriendsFriends.CharacterId2 NOT IN( SELECT CharacterId2
FROM MarvelCharacter
INNER JOIN Friends
ON MarvelCharacter.Id = CharacterId1
WHERE Name = 'Deadpool')
GROUP BY FriendOfFriend.Name
ORDER BY COUNT(*) DESC
Sooo Many Joins

19. MATCH (deadpool:Character)-[:FRIEND_OF*2]->(FriendOfFriend)
WHERE deadpool.name = 'Deadpool'
AND NOT (deadpool)-[:FRIEND_OF]->(FriendOfFriend)
AND NOT deadpool = FriendOfFriend
RETURN FriendOfFriend.name, COUNT(*)
ORDER BY COUNT(*) DESC, FriendOfFriend.name
Sooo Many Joins

20. MATCH (deadpool:Character)-[:FRIEND_OF*2]->(FriendOfFriend)
Sooo Many Joins
FROM MarvelCharacter deadpool
INNER JOIN Friends DeadpoolFriends
ON deadpool.Id = DeadpoolFriends.CharacterId1
INNER JOIN Friends FriendsFriends
ON DeadpoolFriends.CharacterId2 = FriendsFriends.CharacterId1
INNER JOIN MarvelCharacter FriendOfFriend
ON FriendsFriends.CharacterId2 = FriendOfFriend.Id

21. Quick Demo

22. How Do You Know Him?
MEMBER_OF
MEMBER_OF
FRIEND_OF
How can Deadpool connect to Ironman?

23. MATCH (deadpool:Character { name:"Deadpool" }),
(other:Character { name:"Ultron" }),
path = shortestPath((deadpool)-[*]-(other))
RETURN path
Shortest Path

24. MATCH (deadpool:Character { name:"Deadpool" }),
(other:Character { name:"Ultron" }),
path = shortestPath((deadpool)-[*]-(other))
RETURN path
Shortest Path

25. MATCH (deadpool:Character { name:"Deadpool" }),
(other:Character { name:"Ultron" }),
path = shortestPath((deadpool)-[*]-(other))
RETURN path
Shortest Path

26. MATCH (deadpool:Character { name:"Deadpool" }),
(other:Character { name:"Ultron" }),
path = shortestPath((deadpool)-[*]-(other))
RETURN path
Shortest Path

27. Quick Demo

28. Not Scaling
More data means less speed

29. Traditional Solutions
Offline databases
Batch runs to
process data
More indexes
Not Scaling

30. Speed
Embedded Server
Processing requires reading the
same data you wrote
Fixed Size Records
All records of the same type
have the same size on disk
Index-Free Adjacency
Pointer to the next node

31. Demo Time

32. Wrong Usage
This is not a hammer

33. Wrong Usage
Set Orientated
Lists of things with few or no
joins
Global Operations
Made for local graph
operations
Aggregate Queries
Processing requires reading the
same data you wrote

34. Thank You
For Staying Till The End!
@tobiascode