Graph Databases

Purpose

This skill guides selection and implementation of graph databases for applications where relationships between entities are first-class citizens. Unlike relational databases that model relationships through foreign keys and joins, graph databases natively represent connections as properties, enabling efficient traversal-heavy queries.

When to Use This Skill

Use graph databases when:

• Deep relationship traversals (4+ hops): "Friends of friends of friends"
• Variable/evolving relationships: Schema changes don't break existing queries
• Path finding: Shortest route, network analysis, dependency chains
• Pattern matching: Fraud detection, recommendation engines, access control

Do NOT use graph databases when:

• Fixed schema with shallow joins (2-3 tables) → Use PostgreSQL
• Primarily aggregations/analytics → Use columnar databases
• Key-value lookups only → Use Redis/DynamoDB

Quick Decision Framework

DATA CHARACTERISTICS?
├── Fixed schema, shallow joins (≤3 hops)
│   └─ PostgreSQL (relational)
│
├── Already on PostgreSQL + simple graphs
│   └─ Apache AGE (PostgreSQL extension)
│
├── Deep traversals (4+ hops) + general purpose
│   └─ Neo4j (battle-tested, largest ecosystem)
│
├── Multi-model (documents + graph)
│   └─ ArangoDB
│
├── AWS-native, serverless
│   └─ Amazon Neptune
│
└── Real-time streaming, in-memory
    └─ Memgraph

Core Concepts

Property Graph Model

Graph databases store data as:

• Nodes (vertices): Entities with labels and properties
• Relationships (edges): Typed connections with properties
• Properties: Key-value pairs on nodes and relationships

(Person {name: "Alice", age: 28})-[:FRIEND {since: "2020-01-15"}]->(Person {name: "Bob"})

Query Languages