Query Analysis tools

Query performance is the single biggest factor in PostgreSQL application responsiveness. A query that runs in 50 milliseconds during development can take 30 seconds in production when table sizes grow from thousands to millions of rows. The difference between a sequential scan and an index scan can mean orders-of-magnitude improvement — but identifying the right optimization requires understanding execution plans.

PostgreSQL's EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) output contains everything you need: actual row counts versus estimates, buffer hits versus disk reads, sort methods, join strategies, and per-node timing. However, reading raw plan output is a skill that takes years to develop. Misreading an estimate ratio or overlooking a nested loop on an unindexed column are common mistakes that lead to ineffective optimizations.

These tools help you analyze query execution plans, identify missing indexes, and understand why PostgreSQL chose a particular strategy. Whether you're debugging a slow dashboard query or planning indexes for a new feature, AI-assisted analysis catches issues that are easy to miss — like implicit casts preventing index usage, correlations between columns affecting row estimates, or join order problems in multi-table queries.