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name: sql-query-optimization description: Analyze and optimize SQL queries for performance. Use when improving slow queries, reducing execution time, or analyzing query performance in PostgreSQL and MySQL.

SQL Query Optimization

Overview

Analyze SQL queries to identify performance bottlenecks and implement optimization techniques. Includes query analysis, indexing strategies, and rewriting patterns for improved performance.

When to Use

• Slow query analysis and tuning
• Query rewriting and refactoring
• Index utilization verification
• Join optimization
• Subquery optimization
• Query plan analysis (EXPLAIN)
• Performance baseline establishment

Query Analysis Framework

1. Analyze Current Performance

PostgreSQL:

-- Analyze query plan with execution time
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT u.id, u.email, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '1 year'
GROUP BY u.id, u.email;

-- Check table statistics
SELECT * FROM pg_stats
WHERE tablename = 'users' AND attname = 'created_at';

MySQL:

-- Analyze query plan
EXPLAIN FORMAT=JSON
SELECT u.id, u.email, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at > DATE_SUB(NOW(), INTERVAL 1 YEAR)
GROUP BY u.id, u.email;

-- Check table size
SELECT table_name, ROUND(((data_length + index_length) / 1024 / 1024), 2) AS 'Size_MB'
FROM information_schema.tables WHERE table_schema = 'database_name';

2. Common Optimization Patterns

PostgreSQL - Index Optimization:

-- Create indexes for frequently filtered columns
CREATE INDEX idx_orders_user_created
ON orders(user_id, created_at DESC)
WHERE status != 'cancelled';

-- Partial indexes for filtered queries
CREATE INDEX idx_active_products
ON products(category_id)
WHERE active = true;

-- Multi-column covering indexes
CREATE INDEX idx_users_email_verified_covering
ON users(email, verified)
INCLUDE (id, name, created_at);

MySQL - Index Optimization:

-- Create composite index for multi-column filtering
CREATE INDEX idx_orders_user_created
ON orders(user_id, created_at DESC);

-- Use FULLTEXT index for text search
CREATE FULLTEXT INDEX idx_products_search
ON products(name, description);

-- Prefix indexes for large VARCHAR
CREATE INDEX idx_large_text
ON large_table(text_column(100));

3. Query Rewriting Techniques

PostgreSQL - Window Functions:

-- Inefficient: multiple passes
SELECT p.id, p.name,
  (SELECT COUNT(*) FROM orders o WHERE o.product_id = p.id) as order_count,
  (SELECT SUM(quantity) FROM order_items oi WHERE oi.product_id = p.id) as total_sold
FROM products p;

-- Optimized: single pass with window functions
SELECT DISTINCT p.id, p.name,
  COUNT(*) OVER (PARTITION BY p.id) as order_count,
  SUM(oi.quantity) OVER (PARTITION BY p.id) as total_sold
FROM products p
LEFT JOIN order_items oi ON p.id = oi.product_id;

MySQL - JOIN Optimization:

-- Inefficient: JOIN after aggregation
SELECT user_id, name, total_orders
FROM (
  SELECT u.id as user_id, u.name, COUNT(o.id) as total_orders
  FROM users u
  LEFT JOIN orders o ON u.id = o.user_id
  GROUP BY u.id, u.name
) subquery
WHERE total_orders > 5;

-- Optimized: aggregate with HAVING clause
SELECT u.id, u.name, COUNT(o.id) as total_orders
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY u.id, u.name
HAVING COUNT(o.id) > 5;

4. Batch Operations

PostgreSQL - Bulk Insert:

-- Inefficient: multiple round trips
INSERT INTO users (email, name) VALUES ('user1@example.com', 'User One');
INSERT INTO users (email, name) VALUES ('user2@example.com', 'User Two');

-- Optimized: single batch
INSERT INTO users (email, name) VALUES
  ('user1@example.com', 'User One'),
  ('user2@example.com', 'User Two'),
  ('user3@example.com', 'User Three')
ON CONFLICT (email) DO UPDATE SET updated_at = NOW();

MySQL - Bulk Update:

-- Optimized: bulk update with VALUES clause
UPDATE products p
JOIN (
  SELECT id, price FROM product_updates
) AS updates ON p.id = updates.id
SET p.price = updates.price;

Performance Monitoring

PostgreSQL - Long Running Queries:

-- Find slow queries
SELECT query, calls, mean_exec_time, total_exec_time
FROM pg_stat_statements
WHERE mean_exec_time > 1000
ORDER BY mean_exec_time DESC
LIMIT 10;

-- Reset statistics
SELECT pg_stat_statements_reset();

MySQL - Slow Query Log:

-- Enable slow query log
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 2;

-- View slow queries
SELECT * FROM mysql.slow_log
ORDER BY start_time DESC LIMIT 10;

Key Optimization Checklist

• Use EXPLAIN/EXPLAIN ANALYZE before and after optimization
• Add indexes to columns in WHERE, JOIN, and ORDER BY clauses
• Use LIMIT when exploring large result sets
• Avoid SELECT * when only specific columns needed
• Use database functions instead of application-level processing
• Batch operations to reduce network round trips
• Partition large tables for improved query performance
• Update statistics regularly with ANALYZE

Common Pitfalls

❌ Don't create indexes without testing impact ❌ Don't use LIKE with leading wildcard without full-text search ❌ Don't JOIN unnecessary tables ❌ Don't ignore ORDER BY performance impact ❌ Don't skip EXPLAIN analysis

✅ DO test query changes in development first ✅ DO monitor query performance after deployment ✅ DO update table statistics regularly ✅ DO use appropriate data types for columns ✅ DO consider materialized views for complex aggregations

Resources

• PostgreSQL EXPLAIN Documentation
• MySQL EXPLAIN Documentation
• pgBadger - PostgreSQL log analyzer
• MySQL Workbench Query Analyzer