Test Result Storage and Analysis Questions

Design systems that ingest, store, index, and analyze large volumes of automated test results and related metadata to support fast queries, pattern detection, and stakeholder reporting. Discuss ingestion strategies such as streaming and batched pipelines, data models for test runs and artifacts, indexing and partitioning to support common query patterns, and tiered storage between fast hot stores and long term archives. Explain trade offs between storage technologies such as time series databases, columnar analytics stores, search engines, object storage, and relational databases with respect to query latency, cost, and retention. Cover aggregation and rollup strategies, anomaly detection and failure pattern identification, linking results to code commits and builds, application programming interfaces for access and export, multi tenant access control, retention and backup policies, and operational concerns such as compaction, cost optimization, and scaling to millions of daily test results. Describe how dashboards and automated alerts surface trends and how the system supports root cause analysis and stakeholder reporting at scale.