Auto-Increment Columns is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
CREATE TABLE users (\n id INTEGER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,\n name VARCHAR(100)\n);Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A development team applies Auto-Increment Columns to a small commerce database, verifies the result, and then decides whether the same approach is safe for production data.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Auto-Increment Columns operation, verify affected rows, then commit.\nCOMMIT;The schema or operation satisfies the stated rule, rejects invalid data, and can be verified with a repeatable query.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
ALTER TABLE products\n ADD COLUMN active BOOLEAN NOT NULL DEFAULT TRUE;\n\nSELECT product_id, name, active FROM products;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;Auto-Increment Columns is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
CREATE TABLE users (\n id INTEGER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,\n name VARCHAR(100)\n);Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A development team applies Auto-Increment Columns to a small commerce database, verifies the result, and then decides whether the same approach is safe for production data.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Auto-Increment Columns operation, verify affected rows, then commit.\nCOMMIT;The schema or operation satisfies the stated rule, rejects invalid data, and can be verified with a repeatable query.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
ALTER TABLE products\n ADD COLUMN active BOOLEAN NOT NULL DEFAULT TRUE;\n\nSELECT product_id, name, active FROM products;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;