SQL Performance Monitoring is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
EXPLAIN SELECT * FROM orders WHERE customer_id = 42 ORDER BY ordered_at DESC;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
An orders query that was fast with a thousand rows becomes slow at ten million rows. The team must measure the plan and improve it without changing the result.
CREATE INDEX idx_orders_customer_date\n ON orders (customer_id, ordered_at DESC);\n\nEXPLAIN\nSELECT id, total, ordered_at\nFROM orders\nWHERE customer_id = 42\nORDER BY ordered_at DESC\nLIMIT 20;The execution plan shows a measured reduction in unnecessary scanning or sorting without changing query results.
This lesson reuses picostore. Relevant tables: products, orders. Keep the starter rows from the Introduction lesson so results remain comparable.
CREATE INDEX idx_orders_customer_date\n ON orders (customer_id, ordered_at DESC);\n\nEXPLAIN SELECT order_id, total\nFROM orders\nWHERE customer_id = 1\nORDER BY ordered_at DESC\nLIMIT 20;Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.
Start with a small customer table and retrieve active customers in a predictable order.
SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.
WITH customer_revenue AS (\n SELECT customer_id, SUM(total_amount) AS revenue\n FROM orders\n WHERE order_status = 'completed'\n GROUP BY customer_id\n)\nSELECT customer_id, revenue,\n DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank\nFROM customer_revenue\nORDER BY revenue_rank, customer_id;Index and optimizer behavior is vendor-specific. Compare EXPLAIN output on the actual MySQL or PostgreSQL version and measure with production-shaped data before keeping an index.
SQL Performance Monitoring is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
EXPLAIN SELECT * FROM orders WHERE customer_id = 42 ORDER BY ordered_at DESC;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
An orders query that was fast with a thousand rows becomes slow at ten million rows. The team must measure the plan and improve it without changing the result.
CREATE INDEX idx_orders_customer_date\n ON orders (customer_id, ordered_at DESC);\n\nEXPLAIN\nSELECT id, total, ordered_at\nFROM orders\nWHERE customer_id = 42\nORDER BY ordered_at DESC\nLIMIT 20;The execution plan shows a measured reduction in unnecessary scanning or sorting without changing query results.
This lesson reuses picostore. Relevant tables: products, orders. Keep the starter rows from the Introduction lesson so results remain comparable.
CREATE INDEX idx_orders_customer_date\n ON orders (customer_id, ordered_at DESC);\n\nEXPLAIN SELECT order_id, total\nFROM orders\nWHERE customer_id = 1\nORDER BY ordered_at DESC\nLIMIT 20;Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.
Start with a small customer table and retrieve active customers in a predictable order.
SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.
WITH customer_revenue AS (\n SELECT customer_id, SUM(total_amount) AS revenue\n FROM orders\n WHERE order_status = 'completed'\n GROUP BY customer_id\n)\nSELECT customer_id, revenue,\n DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank\nFROM customer_revenue\nORDER BY revenue_rank, customer_id;Index and optimizer behavior is vendor-specific. Compare EXPLAIN output on the actual MySQL or PostgreSQL version and measure with production-shaped data before keeping an index.