Neo4j Graph-Based Recommendation Engine Scales to 25M Daily Active Users

A DEV Community article examines using Neo4j's graph database to power high-throughput matching in dating apps. It argues the main challenge is computing relationship-based scores on a constantly changing, densely connected graph under tight latency targets. The author contrasts relational stacks, where multi-join queries slow sharply at peak load, with graph traversals that handle multi-hop neighborhoods more efficiently. The piece also weighs trade-offs including write latency under Raft consensus, operational complexity, developer productivity, and cost as edge counts grow. It closes with practical guidance on cluster sizing, write buffering, traversal depth limits, caching of top matches in Redis, and observability.