Column Function - Aggregate Operation

Enhanced Definition

An aggregate operation, often referred to as an aggregate function or column function in SQL, performs a calculation on a set of values in a column and returns a single summary value. These functions are fundamental for data summarization and reporting within relational database management systems like DB2 for z/OS. They operate on groups of rows, rather than individual rows, to produce consolidated results. A column function, also known as an aggregate function, is an SQL function in DB2 for z/OS that operates on a set of values (typically from a column across multiple rows) and returns a single scalar value. It summarizes data from a group of rows into a single result, providing statistical or summary information.

Key Characteristics

- Set-Oriented: Operates on a collection of values (a column or an expression over a column) across multiple rows, not on a single row at a time.
- Single Scalar Result: Always returns a single value for the entire set or for each defined group of rows.
- Common Functions: Includes standard SQL functions such as SUM (total), AVG (average), COUNT (number of items), MIN (minimum value), and MAX (maximum value).
- NULL Handling: Most aggregate functions (e.g., SUM, AVG, MIN, MAX) ignore NULL values in their calculations; COUNT(*) includes rows with NULLs, while COUNT(column_name) counts non-NULL values.
- DISTINCT Keyword Support: Can be used with the DISTINCT keyword (e.g., COUNT(DISTINCT column_name)) to operate only on unique values within the specified column.

Use Cases

- Financial Reporting: Calculating the SUM of all transactions for a specific account or the AVG balance across a portfolio of accounts.
- Inventory Management: Determining the COUNT of unique product IDs in stock or the MAX quantity of a particular item ever received.
- Performance Analysis: Finding the MIN and MAX response times for a CICS transaction over a period, or the AVG CPU utilization for a batch job class.
- Sales Analysis: Summarizing total sales SUM by region or product category, often using a GROUP BY clause to segment the data.
- Auditing and Compliance: Counting the number of records processed or updated within a specific timeframe to ensure data integrity and track activity.

Related Concepts

Aggregate functions are intrinsically linked to SQL and DB2 for z/OS, forming a core part of data manipulation language (DML) for analytical queries. They are frequently used in conjunction with the GROUP BY clause to partition data into logical sets before aggregation, and with the HAVING clause to filter these grouped results based on aggregate conditions (unlike WHERE, which filters individual rows). Application programs written in COBOL, PL/I, or Java on z/OS use embedded SQL to execute queries containing aggregate functions, processing the single-row result sets returned. They are also heavily utilized by reporting tools like QMF (Query Management Facility) to generate summary reports from DB2 tables.

Best Practices:

Specify COUNT(*) vs. COUNT(column): Use COUNT(*) to count all rows in a group (including those with NULLs in other columns), and COUNT(column_name) to count only non-NULL values in a specific column.
Understand NULL Impact: Be aware that NULL values are typically ignored by SUM, AVG, MIN, and MAX. If NULLs should be treated as zeros or other values, use COALESCE or VALUE functions within the aggregate (e.g., SUM(COALESCE(column, 0))).
Optimize GROUP BY Queries: For large tables, ensure that columns used in the GROUP BY clause are indexed appropriately to improve query performance, as DB2 may need to sort the data before aggregation.
Use HAVING for Group Filtering: Do not use aggregate functions directly in the WHERE clause. Instead, use the HAVING clause to filter the results *after* the GROUP BY and aggregation have occurred.
Consider Materialized Query Tables (MQTs): For frequently run aggregate queries on large datasets, consider creating MQTs in DB2. These are pre-computed tables that store the results of a query, significantly speeding up subsequent requests for the same aggregated data.