Stored Functions is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
SELECT UPPER(name), ROUND(price, 2), CURRENT_DATE FROM products;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A sales manager needs a monthly report showing revenue, order counts, rankings, and changes over time without exporting raw data to a spreadsheet.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Stored Functions operation, verify affected rows, then commit.\nCOMMIT;The query returns only the intended rows and columns, with deterministic ordering where order matters.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
SELECT category,\n COUNT(*) AS products,\n ROUND(AVG(price), 2) AS average_price,\n SUM(stock) AS units_available\nFROM products\nGROUP BY category\nHAVING SUM(stock) > 0;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;Stored Functions is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
SELECT UPPER(name), ROUND(price, 2), CURRENT_DATE FROM products;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A sales manager needs a monthly report showing revenue, order counts, rankings, and changes over time without exporting raw data to a spreadsheet.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Stored Functions operation, verify affected rows, then commit.\nCOMMIT;The query returns only the intended rows and columns, with deterministic ordering where order matters.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
SELECT category,\n COUNT(*) AS products,\n ROUND(AVG(price), 2) AS average_price,\n SUM(stock) AS units_available\nFROM products\nGROUP BY category\nHAVING SUM(stock) > 0;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;