It’s a fact: today NoSQL databases are very popular in several areas of the software industry. They have many different uses cases, including graphs. The graph database has a structure that’s pretty different from relational technology and has a lot of successful use cases such as recommendation systems on Facebook and LinkedIn. This session covers what a graph database is and how to use it with Java. With a useful and practical live demo of career recommendation with Neo4J, you will learn how easy it could be to build your next successful application. Today!

9. Stark
Rogers
Romanoff
Banners
Berlin
São Paulo
Moscow
San
Francisco
Medellin

10. Stark
Rogers
Romanoff
Banners
Berlin
São Paulo
Moscow
San
Francisco
MedellinBusiness
Fun

11. 1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

12. name Tony Stark
nationality American
hero Iron man
power rich
when 2011
why Save the world
who flying
type Fun
name San Francisco
country USA
State California
Founded 1776

13. NoSQL
● Database
● Doesn't use
●
● BASE
● Five different types

14. SQL Key-value Column Document Graph
Table Bucket Column
Family
Collection
Row Key/value
pair
Column Document Vertex
Column Key/value
pair
Key/value pair Vertex and Edge
property
Relationship Link Edge

15. S
c
a
l
a
b
i
l
i
t
y Complexity
key-value
Column
Document
Graph

17. Graph
● Neo4j
● InfoGrid
● Sones
● HyperGraphDB
Apollo Ares
Kratoswas killed by was killed by
Is brother
killed killed

18. Grace Hopper
label Person
id ada
name Ada Lovelace
occupation scientist
Ada Lovelace
label Person
id grace
name Grace Hopper
occupation scientist
Knows
Vertex Vertex
Edge

19. Radioactive
label Person
id marie_curie
name Marie Curie
occupation scientist
Marie Curie
label nature
id radioactive
name Radioactive
discovers
when 1867
where Europe

22. Grace Hopper
label Person
id grace
name Grace Hopper
occupation scientist
Vertex
grace = graph.addVertex(
T.label, "person",
"id", "grace",
"name", "Grace Hopper",
"occupation", "scientist");

23. Grace
Hopper
Ada
Lovelace
Knows
grace.addEdge("knows", ada);

24. Out ->
In <-
Both

25. ●
●
●

26. Id name Occupation Salary age gender
... ... ... ... ... ...
SELECT * FROM Person WHERE
occupation = "Engineer" AND
salary > 3000 and age BETWEEN 20
AND 25

27. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25));

28. Id name Occupation Salary age gender
... ... ... ... ... ...
SELECT name FROM Person WHERE
occupation = "Engineer" AND
salary > 3000 and age BETWEEN 20
AND 25

29. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25))
.values("name");

30. Id name Occupation Salary age gender
... ... ... ... ... ...
SELECT count(*) FROM Person WHERE
occupation = "Engineer" AND
salary > 3000 and age BETWEEN 20
AND 25

31. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25))
.count();

32. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25))
.groupCount()
.by(“city”);

34. Id name Occupation Salary age gender
... ... ... ... ... ...
SELECT * FROM Person WHERE
occupation = "Engineer" AND
salary > 3000 and age BETWEEN 20
AND 25//...joins
Id know
... ...

35. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25))
.out("knows");

37. Id name Occupation Salary age gender
... ... ... ... ... ...
SELECT * FROM Person WHERE
occupation = "Engineer" AND
salary > 3000 and age BETWEEN 20
AND 25//...joins
Id know
... ...
Id love
... ...

38. Grace
Hopper
Ada
Lovelace
Knows
g.V().has("occupation","Engineer")
.has("salary", gt(3000))
.has("age", between(20, 25))
.outE("knows")
.has("feel", "love")
.bothV();

39. g.V().repeat(out("eats"))
.times(3);
g.V().repeat(out("eats"))
.until(has("name", "grass"));
//path().by("name");

40. ● Mapping API
● Communication API
● No lock-in
● Divide and conquer
DAO
Mapping
Communication
Document
Key
Column
Graph
DIANA
ARTEMIS
Data Tier

41. ● CDI Based
● Diana Based
● Annotation Based
● Events to insert, delete, update
● Supports to Bean Validation
● Configurable and Extensible
● Query Method

43. Poliana
label Book
age 2007
name The Shack
The Shack
label Person
name Poliana
age 25
reads
where Brazil

44. Person poliana = graphTemplate.insert(Person.builder()
.withName("Poliana").withAge(25).build());
Book shack =
graphTemplate.insert(Book.builder().withAge(2007)
.withName("The Shack").build());
EdgeEntity reads = graphTemplate.edge(poliana, "reads",
shack);
reads.add("where", "Brazil");

45. ●
●
●

46. List<Person> developers = graph.getTraversalVertex()
.has("salary", gte(3_000D))
.has("age", between(20, 25))
.has("occupation", "Developer")
.<Person>stream().collect(toList());

48. List<Person> result = graph.getTraversalVertex()
.has("salary", gte(3_000D))
.has("age", between(20, 25))
.has("occupation", "Developer")
.out("knows")

50. List<Person> love = graph.getTraversalVertex()
.has("salary", gte(3_000D))
.has("age", between(20, 25))
.has("occupation", "Developer")
.outE("knows")
.has("feel", "love")
.bothV()
.<Person>stream()
.distinct()
.collect(toList());