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.
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
- 8. Next?
- 11. JCP
- 12. Ajax
- 13. Search
- 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");
- 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
- 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();