Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
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Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
⚠️ Always test changes in non-production first. Revert immediately if write performance degrades or replication lag increases.
Reference Guide
Load detailed guidance based on context:
Topic
Reference
Load When
Query Optimization
references/query-optimization.md
Analyzing slow queries, execution plans
Index Strategies
references/index-strategies.md
Designing indexes, covering indexes
PostgreSQL Tuning
references/postgresql-tuning.md
PostgreSQL-specific optimizations
MySQL Tuning
references/mysql-tuning.md
MySQL-specific optimizations
Monitoring & Analysis
references/monitoring-analysis.md
Performance metrics, diagnostics
Common Operations & Examples
Identify Top Slow Queries (PostgreSQL)
-- Requires pg_stat_statements extensionSELECT query,
calls,
round(total_exec_time::numeric, 2) AS total_ms,
round(mean_exec_time::numeric, 2) AS mean_ms,
round(stddev_exec_time::numeric, 2) AS stddev_ms,
rowsFROM pg_stat_statements
ORDERBY mean_exec_time DESC
LIMIT 20;
Capture an Execution Plan
-- Use BUFFERS to expose cache hit vs. disk read ratio
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT o.id, c.name
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE o.status ='pending'AND o.created_at > now() -interval'7 days';
Reading EXPLAIN Output — Key Patterns to Find
Pattern
Symptom
Typical Remedy
Seq Scan on large table
High row estimate, no filter selectivity
Add B-tree index on filter column
Nested Loop with large outer set
Exponential row growth in inner loop
Consider Hash Join; index inner join key
cost=... rows=1 but actual rows=50000
Stale statistics
Run ANALYZE <table>;
Buffers: hit=10 read=90000
Low buffer cache hit rate
Increase shared_buffers; add covering index
Sort Method: external merge
Sort spilling to disk
Increase work_mem for the session
Create a Covering Index
-- Covers the filter AND the projected columns, eliminating a heap fetchCREATE INDEX CONCURRENTLY idx_orders_status_created_covering
ON orders (status, created_at)
INCLUDE (customer_id, total_amount);
Validate Improvement
-- Before optimization: save plan & timing
EXPLAIN (ANALYZE, BUFFERS) <query>; -- note "Execution Time: X ms"-- After optimization: compare
EXPLAIN (ANALYZE, BUFFERS) <query>; -- target meaningful reduction in cost & time-- Confirm index is actually usedSELECT indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes
WHERE relname ='orders';
MySQL: Find Slow Queries
-- Inspect slow query log candidatesSELECT*FROM performance_schema.events_statements_summary_by_digest
ORDERBY SUM_TIMER_WAIT DESC
LIMIT 20;
-- Execution plan
EXPLAIN FORMAT=JSON
SELECT*FROM orders WHERE status ='pending'AND created_at > NOW() -INTERVAL7DAY;
Constraints
MUST DO
Capture EXPLAIN (ANALYZE, BUFFERS) output before optimizing — this is the baseline
Measure performance before and after every change
Create indexes with CONCURRENTLY (PostgreSQL) to avoid table locks
Test in non-production; roll back if write performance or replication lag worsens
Document all optimization decisions with before/after metrics
Run ANALYZE after bulk data changes to refresh statistics
MUST NOT DO
Apply optimizations without a measured baseline
Create redundant or unused indexes
Make multiple changes simultaneously (impossible to attribute impact)
Ignore write amplification caused by new indexes
Neglect VACUUM / statistics maintenance
Output Templates
When optimizing database performance, provide:
Performance analysis with baseline metrics (query time, cost, buffer hit ratio)
Identified bottlenecks and root causes (with EXPLAIN evidence)
