What is Query Caching?

Query caching stores previously computed query results so later identical or compatible requests can avoid repeated scans and calculations, improving response times and reducing compute costs for dashboards, exploratory analytics, and AI or machine-learning pipelines. The approach can yield substantial gains for high-concurrency workloads and large data lakes, but using transient query-result caches as inputs to production ELT pipelines creates risks because caches may expire quickly, be user- or cluster-specific, have small size limits, and be invalidated by data changes or non-deterministic queries. Cache-based ingestion can also complicate governance, security, lineage, debugging, and cost forecasting because cached artifacts are ephemeral and may fall outside conventional data-management controls. The Starburst Team recommends purpose-built alternatives such as durable materialized views, transparent cached views and table-scan redirection, and data-level caching with indexes, alongside monitoring, refresh SLAs, and fallback mechanisms that recompute from source data when cached data is unavailable.

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