Building a recommendation engine with tinker pop
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The document discusses using the TinkerPop graph traversal framework to build a recommendation engine. It provides examples of traversing a graph database to model relationships between entities and executing queries equivalent to SQL queries on the graph. Sample queries demonstrate filtering graph vertices based on properties, retrieving property values, counting results, and traversing edges to find related vertices. The document promotes TinkerPop for complex queries and scaling to large datasets compared to SQL databases.
Building a recommendation engine with tinker pop
- 1. Building a recommendation engine with TinkerPop Otávio Santana @otaviojava osantana@tomitribe.com otaviojava@apache.org
- 2. NoSQL ● Database ● Doesn't use structure ● Not Transaction ● BASE ● Types
- 3. Graph ● Neo4j ● InfoGrid ● Sones ● HyperGraphDB Apollo Ares Kratoswas killed by was killed by Is brother killed killed
- 4. SQL Key-value Document Column Graph Table Bucket Collection Column Family Vertex and Edge Row Key/value pair Document Column Vertex Column Key/value pair Key/value pair Vertex and Edge property Relationship Link Edge
- 7. 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
- 8. 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
- 10. TinkerPop
- 11. 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");
- 14. Marketing Campaign ● Engineer ● Salary 3000 ● Age between 20 and 25 years ● Man
- 15. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT * FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25
- 17. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT name FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25
- 18. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .values("name");
- 19. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT count(*) FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25
- 20. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .count();
- 21. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .groupCount() .by(“city”);
- 22. Their Friends
- 23. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT * FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25//...joins Id know ... ...
- 24. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .out("knows");
- 25. Falling in love
- 26. 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 ... ...
- 27. 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();
- 30. TinkerPop
- 31. Our database is too slow and not big enough! We need NoSql!