{"appId":"sql-app","version":1551,"selectedAngularVersion":20,"item":{"id":"lesson-deleting-data","conceptKey":"deleting-data","subjectId":"subject-deleting-data","title":"Deleting Data","summary":"Deleting Data is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.","baseContent":"<h2>Deleting Data</h2><p>Deleting Data is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.</p><h3>Example</h3><pre><code>DELETE FROM sessions WHERE expires_at &lt; CURRENT_TIMESTAMP;</code></pre><h3>Key point</h3><p>Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.</p><h3>Real-life example</h3><p>An inventory service receives a confirmed business action and must change only the intended rows while preserving an audit-friendly history.</p><h3>Advanced example</h3><pre><code>BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Deleting Data operation, verify affected rows, then commit.\nCOMMIT;</code></pre><h3>Expected result</h3><p>The schema or operation satisfies the stated rule, rejects invalid data, and can be verified with a repeatable query.</p><h3>Production check</h3><ul><li>Test with empty, duplicate, null, and boundary values.</li><li>Use a transaction for related writes.</li><li>Inspect the execution plan before adding an index.</li><li>Use parameterized queries for application input.</li></ul><h3>Continue with the PicoStore database</h3><p>This lesson reuses <strong>picostore</strong>. Relevant tables: <code>customers, products, orders, order_items</code>. Keep the starter rows from the Introduction lesson so results remain comparable.</p><h3>Another practical example</h3><pre><code>DELETE FROM orders\nWHERE status = 'cancelled'\n  AND ordered_at &lt; '2025-01-01';\n\nSELECT COUNT(*) FROM orders WHERE status = 'cancelled';</code></pre><h3>Check the result</h3><p>Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.</p><section data-nonversioned-curriculum=\"1\"><h3>Easy example</h3><p>Start with a small customer table and retrieve active customers in a predictable order.</p><pre><code>SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;</code></pre><h3>How to verify the easy example</h3><ul><li>Run it with representative input.</li><li>Confirm the expected output.</li><li>Try one missing, invalid, or boundary value.</li></ul><h3>Advanced example</h3><p>Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.</p><pre><code>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;</code></pre><h3>Advanced review</h3><ul><li>Explain the tradeoffs and assumptions.</li><li>Test failure, scale, security, and recovery behavior.</li><li>Capture evidence from tests, execution plans, logs, or review output.</li></ul><h3>Additional practical guidance</h3><div><h3>Deleting Data: MySQL and PostgreSQL</h3><p>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.</p><h3>Required verification</h3><ul><li>Run the simple case.</li><li>Test a NULL, duplicate, empty, or boundary case where relevant.</li><li>Confirm the affected rows or query result.</li><li>Use EXPLAIN for performance-sensitive queries.</li></ul></div></section>","detailId":"lesson-deleting-data-a656808d","versions":[],"isActive":true,"detailIsActive":true,"lessonVersions":[],"selectedVersion":20,"content":"","updatedAt":"2026-08-01T09:43:04.474Z","details":[{"id":"lesson-deleting-data","conceptKey":"deleting-data","subjectId":"subject-deleting-data","title":"Deleting Data","summary":"Deleting Data is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.","baseContent":"<h2>Deleting Data</h2><p>Deleting Data is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.</p><h3>Example</h3><pre><code>DELETE FROM sessions WHERE expires_at &lt; CURRENT_TIMESTAMP;</code></pre><h3>Key point</h3><p>Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.</p><h3>Real-life example</h3><p>An inventory service receives a confirmed business action and must change only the intended rows while preserving an audit-friendly history.</p><h3>Advanced example</h3><pre><code>BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Deleting Data operation, verify affected rows, then commit.\nCOMMIT;</code></pre><h3>Expected result</h3><p>The schema or operation satisfies the stated rule, rejects invalid data, and can be verified with a repeatable query.</p><h3>Production check</h3><ul><li>Test with empty, duplicate, null, and boundary values.</li><li>Use a transaction for related writes.</li><li>Inspect the execution plan before adding an index.</li><li>Use parameterized queries for application input.</li></ul><h3>Continue with the PicoStore database</h3><p>This lesson reuses <strong>picostore</strong>. Relevant tables: <code>customers, products, orders, order_items</code>. Keep the starter rows from the Introduction lesson so results remain comparable.</p><h3>Another practical example</h3><pre><code>DELETE FROM orders\nWHERE status = 'cancelled'\n  AND ordered_at &lt; '2025-01-01';\n\nSELECT COUNT(*) FROM orders WHERE status = 'cancelled';</code></pre><h3>Check the result</h3><p>Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.</p><section data-nonversioned-curriculum=\"1\"><h3>Easy example</h3><p>Start with a small customer table and retrieve active customers in a predictable order.</p><pre><code>SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;</code></pre><h3>How to verify the easy example</h3><ul><li>Run it with representative input.</li><li>Confirm the expected output.</li><li>Try one missing, invalid, or boundary value.</li></ul><h3>Advanced example</h3><p>Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.</p><pre><code>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;</code></pre><h3>Advanced review</h3><ul><li>Explain the tradeoffs and assumptions.</li><li>Test failure, scale, security, and recovery behavior.</li><li>Capture evidence from tests, execution plans, logs, or review output.</li></ul><h3>Additional practical guidance</h3><div><h3>Deleting Data: MySQL and PostgreSQL</h3><p>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.</p><h3>Required verification</h3><ul><li>Run the simple case.</li><li>Test a NULL, duplicate, empty, or boundary case where relevant.</li><li>Confirm the affected rows or query result.</li><li>Use EXPLAIN for performance-sensitive queries.</li></ul></div></section>","detailId":"lesson-deleting-data-a656808d","versions":[],"isActive":true,"detailIsActive":true,"lessonVersions":[],"selectedVersion":20,"content":"","updatedAt":"2026-08-01T09:43:04.474Z"}]}}