Graph Databases (Neo4j, Dgraph, ArangoDB)

Graph Database Fundamentals

Graph databases store data as nodes (entities) and edges (relationships). They excel at traversing connected data.

Neo4j

The most popular graph database with Cypher query language:

CREATE (alice:Person {name: 'Alice', age: 30})

CREATE (bob:Person {name: 'Bob', age: 25})

CREATE (alice)-[:FOLLOWS]->(bob)

MATCH (alice:Person {name: 'Alice'})-[:FOLLOWS*2]->(friend)

RETURN friend.name

ArangoDB

Multi-model database supporting graph, document, and key-value:

db._query(`

FOR v IN 1..3 OUTBOUND 'users/alice' GRAPH 'social'

RETURN v.name

`);

Property Graph vs RDF

| Aspect | Property Graph | RDF (SPARQL) | |--------|---------------|--------------| | Model | Labeled nodes/edges | Triple stores | | Schema | Schema-optional | Formal ontology | | Query | Cypher, Gremlin | SPARQL | | Best for | Applications | Linked data, semantics |

Use Cases

Graph databases excel in social networks, recommendation engines, fraud detection, knowledge graphs, and identity resolution. Avoid them for simple CRUD or aggregation-heavy analytics.

Conclusion

Choose Neo4j for mature graph capabilities and Cypher. Choose ArangoDB for multi-model flexibility. Use property graphs for applications and RDF for semantic web workloads.