snowflake-query-analyzer

Snowflake query performance analysis examines execution time, bytes scanned, partition pruning, and spilling to pinpoint bottlenecks. The Skill connects to INFORMATION_SCHEMA, computes execution metrics, and outputs structured JSON highlighting which query stages consume the most resources and credits.

Cost reduction strategies include clustering key design, incremental processing, and caching to minimize bytes scanned and warehouse compute time. The Skill analyzes query profiles to recommend which optimization applies to your workload and estimates potential cost savings.

The Skill analyzes individual queries by query ID and can fetch related queries using model parameters like `--model fct_orders`, enabling batch analysis across workloads. Results output as structured JSON for downstream cost analysis and reporting across multiple warehouses.

Access to Snowflake INFORMATION_SCHEMA and query IDs or table names are prerequisites. The Skill requires snowflake-connector-python to authenticate and retrieve query execution history, metrics, and metadata needed for performance and cost analysis.

Query analysis outputs structured JSON containing execution metrics, cost drivers, and optimization recommendations. This JSON format integrates with downstream reporting tools, dashboards, and cost visualization platforms for further analysis.

Yes, the Skill detects partition pruning, spilling, and credit consumption across warehouse queries. It satisfies multi-warehouse performance troubleshooting by analyzing INFORMATION_SCHEMA data and providing cost-driver reports applicable to any warehouse configuration.