using-graph-databases_skill

Overview

This skill provides a practical guide to selecting and implementing graph databases for relationship-heavy applications. It focuses on Neo4j as the primary recommendation and also covers ArangoDB, Amazon Neptune, Apache AGE, query patterns (Cypher), and graph data modeling. The goal is to help engineers design performant graph schemas, write common queries, and integrate graphs with AI pipelines.

How this skill works

The skill explains the property graph model (nodes, relationships, properties) and maps common application needs to appropriate database choices via a decision framework. It presents core Cypher patterns (matching, variable-length paths, shortest path, recommendations, fraud detection), indexing and caching strategies, and language-specific examples in Python, TypeScript, Go. It also includes modeling best practices, performance tips, and validation scripts for common anti-patterns.

When to use it

• Deep relationship traversals (4+ hops) or traversal-heavy queries
• Variable or evolving relationship schemas where joins become brittle
• Path finding, network analysis, dependency chains
• Pattern matching tasks like fraud detection or complex recommendations
• When you need multi-model capabilities (documents + graph) or AWS-native managed graph services

Best practices

• Model relationships explicitly as first-class edges; avoid storing relationship lists as node properties
• Store metadata on relationship properties (timestamps, strength) for richer queries
• Bound traversals to a max depth and use indexes to prevent unbounded scans
• Avoid supernodes by sharding or using intermediate aggregation/time-partition nodes
• Materialize expensive aggregations (friend counts, scores) in node properties or cache results

Example use cases

• Social network: friend recommendations, mutual connections, news feed and influence metrics
• Recommendation engine: collaborative filtering, content-based and session-based strategies
• Knowledge graph for AI/RAG: hybrid vector + graph search, entity expansion for LLM context
• Fraud detection: detect circular money flows, shared devices, and anomalous transaction chains
• AWS deployments: use Amazon Neptune for managed Gremlin/SPARQL workloads; pick ArangoDB for multi-model needs