TRUNCATE vs DELETE vs DROP

Lesson content

TRUNCATE vs DELETE vs DROP TRUNCATE vs DELETE vs DROP is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly. Example DELETE FROM logs WHERE created_at < '2025-01-01'; TRUNCATE TABLE staging_logs; DROP TABLE obsolete_logs; Key point Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use. Real-life example An inventory service receives a confirmed business action and must change only the intended rows while preserving an audit-friendly history. Advanced example BEGIN; -- Preview the exact target set first. SELECT id FROM orders WHERE status = 'pending'; -- Apply the TRUNCATE vs DELETE vs DROP operation, verify affected rows, then commit. COMMIT; Expected result The schema or operation satisfies the stated rule, rejects invalid data, and can be verified with a repeatable query. Production check Test with empty, duplicate, null, and boundary values. Use a transaction for related writes. Inspect the execution plan before adding an index. Use parameterized queries for application input. Continue with the PicoStore database This lesson reuses picostore . Relevant tables: customers, products, orders, order_items . Keep the starter rows from the Introduction lesson so results remain comparable. Another practical example DELETE FROM orders WHERE status = 'cancelled' AND ordered_at < '2025-01-01'; SELECT COUNT(*) FROM orders WHERE status = 'cancelled'; Check the result Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct. Easy example Start with a small customer table and retrieve active customers in a predictable order. SELECT customer_id, name, email FROM customers WHERE status = 'active' ORDER BY name; How to verify the easy example Run it with representative input. Confirm the expected output. Try one missing, invalid, or boundary value. Advanced example Use a CTE and a window function to rank customer revenue while keeping the query readable and testable. WITH customer_revenue AS ( SELECT customer_id, SUM(total_amount) AS revenue FROM orders WHERE order_status = 'completed' GROUP BY customer_id ) SELECT customer_id, revenue, DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank FROM customer_revenue ORDER BY revenue_rank, customer_id; Advanced review Explain the tradeoffs and assumptions. Test failure, scale, security, and recovery behavior. Capture evidence from tests, execution plans, logs, or review output. Additional practical guidance TRUNCATE vs DELETE vs DROP: MySQL and PostgreSQL Preview the target rows and use a transaction for important changes. MySQL and PostgreSQL share the core statement, while RETURNING support, safe-update settings, and DDL transaction behavior may differ. Required verification Run the simple case. Test a NULL, duplicate, empty, or boundary case where relevant. Confirm the affected rows or query result. Use EXPLAIN for performance-sensitive queries.