Betting the Company on a Graph Database - Aseem Kishore @ GraphConnect Boston 2013
1 like1,316 views
This document discusses graph databases and lessons learned from building an application using Neo4j. It defines what a graph database is, describes how Neo4j works and common usage patterns. It then outlines several key lessons learned, such as using unique relationship types, caching statistics, representing history as event nodes, modeling objects as nodes not relationships, and connecting data through relationships rather than properties.
![START user=node(1), other=node(2)
MATCH (user) -[r1:has|wants]-> (thing) <-[r2:has|wants]- (other)
WHERE TYPE(r1) <> TYPE(r2)
RETURN TYPE(r1), TYPE(r2), thing](https://image.slidesharecdn.com/bettingthecompanyliterallyonagraphdatabasetalksaseemkishore-1-130628105246-phpapp02/85/Betting-the-Company-on-a-Graph-Database-Aseem-Kishore-GraphConnect-Boston-2013-9-320.jpg)
![# adjacency list:
nodes = List<Node>
neighbors = Map<Node, List<Node>>
neighbors[node1].add(node2)
# adjacency matrix:
nodes = List<Node>
connections = Map<Node, Map<Node, bool>>
connections[node1][node2] = true](https://image.slidesharecdn.com/bettingthecompanyliterallyonagraphdatabasetalksaseemkishore-1-130628105246-phpapp02/85/Betting-the-Company-on-a-Graph-Database-Aseem-Kishore-GraphConnect-Boston-2013-14-320.jpg)
Betting the Company on a Graph Database - Aseem Kishore @ GraphConnect Boston 2013
- 1. BETTING THE COMPANYBETTING THE COMPANY (LITERALLY) ON A GRAPH DATABASEGRAPH DATABASE TIPS, TRICKS, AND LESSONS LEARNED Aseem Kishore Jan 2013
- 9. START user=node(1), other=node(2) MATCH (user) -[r1:has|wants]-> (thing) <-[r2:has|wants]- (other) WHERE TYPE(r1) <> TYPE(r2) RETURN TYPE(r1), TYPE(r2), thing
- 12. SO… JUST WHAT IS A GRAPH DATABASEGRAPH DATABASE?
- 14. # adjacency list: nodes = List<Node> neighbors = Map<Node, List<Node>> neighbors[node1].add(node2) # adjacency matrix: nodes = List<Node> connections = Map<Node, Map<Node, bool>> connections[node1][node2] = true
- 18. “ By definition, a graph database is any storage system that provides index-free adjacency. ” “ This means that every element contains a direct pointer to its adjacent element and no index lookups are necessary. ”
- 19. QUERYING 1. Start somewhere 2. Traverse elsewhere
- 20. QUERYING IN NEO4J 1. Start somewhere Root node ID directly (file offset) Lucene index 2. Traverse elsewhere Traversal APIs Cypher patterns Built-in graph algos (Djikstra, A*, etc.)
- 21. NEO4J USAGE Embedded mode (Java API) Server mode (REST API) Cypher query language (both)
- 24. NEO4J EDITIONS Community edition Single instance Offline backup Advanced edition Meh Enterprise edition Multi-instance cluster! Online backup!
- 25. NEO4J SCALING Master-slave replication Cache-based sharding Feature-based polyglot'ing 64B limit on nodes, rels, props But can be easily upped; just flipping some bits 100 props/node (high) ⇒ 640M nodes
- 26. OKAY... LET'S TALK ABOUT WHAT WE LEARNEDWHAT WE LEARNED
- 28. WHAT WE LEARNED Unique, expressive relationship types
- 30. WHAT WE LEARNED Unique, expressive relationship types Cache stats where possible
- 32. WHAT WE LEARNED Unique, expressive relationship types Cache stats where possible Capture history through event nodes
- 34. WHAT WE LEARNED Unique, expressive relationship types Cache stats where possible Capture history through event nodes First-class objects ⇒ nodes, not rels
- 36. WHAT WE LEARNED Unique, expressive relationship types Cache stats where possible First-class objects ⇒ nodes, not rels Capture history through event nodes Connected data ⇒ nodes, not props
- 38. WHAT WE LEARNED Unique, expressive relationship types Cache stats where possible First-class objects ⇒ nodes, not rels Capture history through event nodes Connected data ⇒ nodes, not props Maintain linked lists for O(1) queries
- 39. NEO4J ROADMAP Overhaul of indexing API Relationship type grouping Socket and/or binary protocol Automatic sharding?
- 41. THANKS! TWITTER: @ASEEMK GITHUB: @ASEEMK EMAIL: ASEEM.KISHORE@GMAIL.COM Questions?