Firebolt is designed to run SQL queries efficiently, using advanced optimizations at both the query and workload levels. This section provides an overview of how Firebolt processes queries, the optimizations applied during query handling, and the tools available for monitoring and improving performance. For guidance on how to run queries and interact with data in Firebolt, see Query data.

​

Optimizations on a per-query basis

Firebolt applies specific optimizations at the individual query level to ensure efficient performance, even for complex queries. These optimizations take effect immediately when you create an account and start your first engine.

• Spilling intermediate query state to the local SSD cache. This allows processing queries whose working set exceeds main memory.
• Firebolt has advanced support for correlated subqueries. These are automatically decorrelated by our query planner to maximize performance.

​

Workload-level optimizations

Homogeneous workloads with repeated query structures can benefit significantly from workload-level optimizations. Examples for such workloads are customer-facing, high-concurrency data apps, or internal BI workloads. For these workloads, Firebolt leverages multiple different optimizations.

• Reusing query sub-results and result caching to reduce redundant calculations across queries.
• History-based query optimization, which leverages past query patterns to improve query plans for new queries.

​

Query telemetry and monitoring

Firebolt provides the following tools to monitor and analyze query performance:

• EXPLAIN command: View the query plan to understand how Firebolt executes your query.
• Telemetry data: Analyze metrics such as runtime, memory usage, and data processed.
• Query history: Use views like information_schema.engine_query_history to monitor query performance details over time.