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Identity Resolution with Distributed SQL

1 post · Aug 2026

What this trail covers

SingleStore's distributed SQL engine is presented as a superior alternative to traditional graph databases and lakehouse batch processing for identity resolution workloads. It enables continuous, real-time merging of device signals, logins, and household identifiers, overcoming the limitations of nightly batch updates. This leads to more accurate attribution, frequency capping, and consent management, while also addressing the growing engineering bottleneck of recomputing large identity graphs. This post details how incoming signals resolve incrementally against a live graph, ensuring new edges are ready for attribution, targeting, and frequency capping within seconds, and how opt-out requests are processed in seconds, providing a defensible audit trail.

All SingleStore trails
  1. Why ad models need fresh context at decision timeThis post highlights the critical need for fresh data context at decision time for AI models in advertising, particularly for real-time bidding and autonomous agents. It explains how data staleness in offline metrics and auction-time decisions leads to financial losses and trust issues. The post argues against adding separate feature stores and vector databases, advocating for a unified data engine that supports similarity searches alongside SQL filters on real-time data. It emphasizes that a simpler model with live signals outperforms a complex one with stale data, and that AI agents amplify the risk of architectural gaps. The final installment will focus on identity resolution as a foundational challenge.
  2. Graph database vs distributed SQL for identity resolutionThis 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.
  3. Identity resolution without the nightly batchThis post details how incoming signals resolve incrementally against a live graph, ensuring new edges are ready for attribution, targeting, and frequency capping within seconds. It also explains how opt-out requests are processed in seconds, providing a defensible audit trail. The obsolescence of nightly batch processing for identity graphs is highlighted, emphasizing the architectural debt incurred by stale data and the benefits of continuous resolution for improved accuracy in attribution, frequency capping, and consent management.