Drupal and Neo4J
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This document discusses using graph databases like Neo4j to solve problems that are difficult for relational databases. It provides examples of how graph databases are used in applications like social networks, fraud detection, and recommendations. It also describes how Neo4j works, including its data model of nodes and relationships, and query language Cypher. Finally, it mentions the neo4j_connector module that allows Drupal sites to integrate with Neo4j for more complex queries and relationships between content.
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Drupal and Neo4J
- 3. • The “problem” • Graph databases • Neo4J • Examples • Drupal & graph • Future
- 4. The problem
- 7. tricks only graphs can solve
- 8. 1M nodes, ~50 rel/node Depth RDBMS Neo4J Returned records 2 0.016 0.01 ~2’500 3 30.267 0.168 ~110’000 4 1543.505 1.359 ~600’000 5 unfinished 2.132 ~800’000
- 9. Such speed So wow Very graph
- 10. Examples
- 12. • Business Intelligence • Content Management • Fraud Detection • Geo • Graph Search • Identity & Access Management • Impact Analysis • Logistics • Master Data Management • Network and Data Center Management • Product Catalog • Recommendation Engine • Resource Authorization & Access Control • Social Network
- 14. Graph databases
- 17. Jim:PERSON Lisa:PERSON :COOK_FOR city: London gender: male city: San Francisco gender: female meal: Jalfrezi score: 9.8 user_index::uid::3 user_index::uid::8
- 18. Jim:PERSON Lisa:PERSON :COOK_FOR city: London gender: male city: San Francisco gender: female meal: Jalfrezi score: 9.8 :LOVE user_index::uid::3 user_index::uid::8
- 20. Start
- 31. Neo4J
- 32. Server: embedded - server mode low latency network low level API REST API better control better scaling JVM only languages
- 33. Backend
- 37. Disks Record files File system cache Object cache Traversal API Cypher Transaction log Transaction management Core API
- 38. Core API
- 39. Traversal API
- 40. Cypher
- 42. • MATCH (n)-[r]->(m) • MATCH (n:USER)-[:EAT]->(m:FOOD) • MATCH p = (n)--(m) • MATCH (n{name:’Jim’})-[r]->(m) • MATCH (o)-->(n)<-[*1..3]-(m) • MATCH [:TRAVEL{by:”bus”}]->(n)
- 43. START n=node(*)
- 44. • START n=node(12) • START n=node({12, 3, 7}) • START n=node:user_index(uid=“12”) • START n=node(3), m=node(12)
- 45. • WHERE n.name = ‘Jim’ • WHERE n.has(“name”) • WITH count(n) AS all WHERE all > 10
- 46. • RETURN n • RETURN collect(n), o.name • RETURN length(p)
- 47. I want a good party this Friday. Show me my foaf who has at least 20 close friends - so I can avoid awkward parties. And they don’t know my ex.
- 48. START n=node(12) MATCH p = (n)-[:FRIEND*]->(m), (m)<-[:FRIEND*1..2]-(o) WHERE any(p IN collect(o) WHERE p.name != ‘Liz’) WITH count(o) AS total WHERE total >= 20 RETURN m.name, p, total, collect(o)
- 49. CREATE (jim:USER{hair:’green’}), (erica:USER{hair:’brown’}), jim-[:REPORTS_TO{type:’TPS’}]->erica; ! MATCH (n) SET n.name = ‘Walter’; ! MATCH (n) DELETE n;
- 50. Drupal <3 Neo4J
- 53. • Pluggable indexes • Dynamic labels, properties and relationships • “smart” indexing
- 54. DEMO
- 55. Future? • Cypher builder • Views integration • More field support • Embedded graph display