Query Optimization

This skill enables an AI agent to diagnose and fix slow database queries. The agent uses EXPLAIN/EXPLAIN ANALYZE to interpret query execution plans, identifies missing indexes and inefficient scan patterns, rewrites queries to eliminate performance bottlenecks, detects and resolves N+1 query problems in ORMs, and recommends monitoring tools to track query performance over time. The focus is on practical, measurable improvements with before-and-after evidence.

Workflow

1. Identify the slow query: Collect the problematic query from slow query logs, application performance monitoring (APM) tools, or user reports. Note the current execution time, the table sizes involved, and how frequently the query runs. High-frequency slow queries should be prioritized over rare ones.

2. Analyze the execution plan: Run EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN FORMAT=JSON (MySQL) on the query to obtain the actual execution plan. Look for sequential scans on large tables, nested loop joins with high row estimates, sort operations on unindexed columns, and large gaps between estimated and actual row counts.

3. Identify optimization opportunities: Based on the plan, identify concrete fixes: add indexes for columns in WHERE, JOIN, and ORDER BY clauses; rewrite subqueries as JOINs; replace SELECT * with specific columns; add LIMIT clauses where appropriate; use covering indexes to avoid table lookups; eliminate redundant or duplicate conditions.

4. Apply optimizations: Create the necessary indexes, rewrite the query, or adjust ORM usage. For N+1 problems, switch from lazy loading to eager loading (e.g., select_related/prefetch_related in Django, include in Prisma, joinedload in SQLAlchemy). Apply one change at a time to measure each improvement independently.

5. Measure and validate: Re-run EXPLAIN ANALYZE on the optimized query and compare execution time, rows scanned, and plan structure against the original. Verify that the query returns identical results. Check that new indexes do not degrade write performance beyond acceptable thresholds.

6. Set up ongoing monitoring: Configure slow query logging with appropriate thresholds (e.g., 100ms for PostgreSQL via log_min_duration_statement). Integrate with monitoring tools like pg_stat_statements, Datadog, or Grafana to track query performance trends and catch regressions early.

Supported Technologies