Graph database vs distributed SQL for identity resolution

This post refines the identity resolution capability by directly comparing distributed SQL engines with native graph databases for this specific workload. It argues that for identity resolution, which typically requires only one or two hops and prioritizes millisecond ingestion of new edges and concurrent joins without global recomputation, a distributed SQL engine with data sharding is favored over traversal-optimized graph databases. Key technical differentiators highlighted include the ability to process transactional writes and analytical reads concurrently (HTAP), high concurrency for read-heavy workloads, and multi-modality for handling structured data, JSON signals, and vectors. The post emphasizes that the choice depends on the deepest traversal required by the production workload, with one or two hops indicating a partitioning and serving problem better suited for distributed SQL.