Graph Databases: When to Use Graphs

Graph databases store data as nodes and edges, with properties on both. This structure makes it easy to follow connections from one item to another, even through many steps. They shine when relationships are central to the problem, and when the speed of traversing those connections matters more than raw counts of records.

Common use cases include social networks, fraud detection, recommendation systems, knowledge graphs, and supply chains. For example, in a social app you might want to find friends of friends who share a hobby, or uncover clusters of users who influence purchases. In a knowledge graph, you link entities like people, places, and events to answer questions quickly.

Graphs are not always the best choice. If your data is mostly simple lookups or wide, flat tables with heavy analytics, relational databases or columnar stores can be easier to manage. Loading data that rarely connects can be heavier in a graph than in a document or key-value store. Also, graph engines add a learning curve for modeling and for new query languages such as Cypher or Gremlin.

Modeling tips: start with the main entities as nodes and the important relationships as edges. Add properties on nodes and edges to keep facts like dates, weights, or statuses. Keep the graph small enough to traverse quickly and use indexes on commonly walked paths. For bigger graphs, plan scales and consider whether your tool supports efficient sharding or clustering. Measure performance with realistic queries that match your real needs.

Practical steps to try: sketch the domain as a small graph, prototype with a few types of nodes and a handful of relationships, then run your typical traversals. Compare the results and the time to answer with a relational approach. If you see fast hops through many steps, a graph can save time and simplify queries. If not, stay with a different database.

Key Takeaways

  • Graph databases excel at connected data and fast traversals.
  • Use cases include social graphs, recommendations, knowledge graphs, and fraud detection.
  • Start small, model carefully, and compare with relational options to choose the right tool.