The new generation of data persistence with graph

1. Otávio Santana @otaviojava
The new
generation of
data persistence
with Graph

2. Evolution

3. Goods
● Land
● Gold
● Oil
● Information

4. Machines

5. The information is
not enough

6. Big Data

7. Web 3.0
Web Semantic

8. Next?

9. Recommendation

11. JCP

12. Ajax

13. Search

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

17. Stark
Rogers
Romanoff
Banners
New York
São Paulo
Moscow
San
Francisco
Medellin
Travel
Business
Fun

18. Reasons why
travel
1. Fun
2. Conferences
3. Music
4. Geek Conf
5. Family
6. History
7. Language
8. Honeymoon
9. Work
10. Meetings
11. University
12. Shopping
13. Relax

19. More Information
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
Travels

20. NoSQL
● Database
● Doesn't use structure
● Not Transaction
● BASE
● Five different types

21. 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
SQL vs NoSQL

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

23. CAP

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

25. Graph
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

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

27. Graph Database

28. TinkerPop

29. TinkerPop
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");

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

31. Edges
Out ->
In <-
Both

32. Marketing
Campaign
● Engineer
● Salary 3000
● Age between 20 and 25 years

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

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

35. Their Friends

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

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

38. Falling in love

39. SQLId 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
... ...

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

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

42. Otávio Santana @otaviojava