The new generation of data persistence with graph

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1) The document discusses the evolution of data from goods to information and machines, and the transition from SQL to NoSQL databases like key-value, column, document and graph structures. 2) It provides an example of modeling data as a graph database using TinkerPop and Gremlin, showing how to add vertices and edges to represent relationships. 3) Graph databases allow complex queries over relationships, like finding engineers who know other people or might fall in love, which would be difficult in SQL.

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

The new generation of data persistence with graph

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

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

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

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

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

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

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

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

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

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

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");

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

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

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

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

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

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

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
... ...

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();

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

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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

The new generation of data persistence with graph

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