Aggregate Functions

Lesson content

Aggregate Functions Aggregate Functions is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly. Example SELECT UPPER(name), ROUND(price, 2), CURRENT_DATE FROM products; Key point Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use. Real-life example A sales manager needs a monthly report showing revenue, order counts, rankings, and changes over time without exporting raw data to a spreadsheet. Advanced example BEGIN; -- Preview the exact target set first. SELECT id FROM orders WHERE status = 'pending'; -- Apply the Aggregate Functions operation, verify affected rows, then commit. COMMIT; Expected result The query returns only the intended rows and columns, with deterministic ordering where order matters. 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 SELECT category, COUNT(*) AS products, ROUND(AVG(price), 2) AS average_price, SUM(stock) AS units_available FROM products GROUP BY category HAVING SUM(stock) > 0; 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. SQL reference catalog: Aggregate Functions This catalog covers the practical public SQL surface. Check the exact server-version documentation before using a vendor-specific item. Portable/common aggregates COUNT — count rows or non-null values SUM — total numeric values AVG — average numeric value MIN — smallest value MAX — largest value EVERY — true when every value is true; standard/PostgreSQL ANY / SOME — boolean aggregate in standard SQL; vendor syntax varies Statistical aggregates STDDEV_POP / STDDEV_SAMP — population or sample standard deviation VAR_POP / VAR_SAMP — population or sample variance CORR — correlation; PostgreSQL COVAR_POP / COVAR_SAMP — covariance; PostgreSQL REGR_* — linear-regression aggregates; PostgreSQL Collection aggregates STRING_AGG — join text; PostgreSQL GROUP_CONCAT — join text; MySQL ARRAY_AGG — collect values into array; PostgreSQL JSON_ARRAYAGG / JSON_AGG — collect values into JSON array JSON_OBJECTAGG / JSON_OBJECT_AGG — collect key/value pairs into JSON object BIT_AND / BIT_OR / BIT_XOR — bitwise aggregates; support varies BOOL_AND / BOOL_OR — boolean aggregates; PostgreSQL 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 Aggregate Functions: MySQL and PostgreSQL Prefer standard SQL functions when portability matters. MySQL and PostgreSQL differ most in date formatting, type conversion, string aggregation, and error handling for invalid values. 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.