Understanding SQL Aggregate Functions
James Reed
Infrastructure Engineer · Leapcell

Key Takeaways
- SQL aggregate functions summarize large data sets using simple commands.
GROUP BY
andHAVING
enhance aggregate analysis.- Aggregate functions usually ignore NULL values.
Introduction
Aggregate functions in SQL process a set of values and return a single summarized result. These are essential tools for analyzing and summarizing data in relational databases .
Common Aggregate Functions
1. COUNT()
-
Definition: Returns the number of rows.
-
Variants:
COUNT(*)
: All rows, including those with NULLs.COUNT(column)
: Only non-NULL values.COUNT(DISTINCT column)
: Number of unique non-NULL entries.
-
Example:
SELECT COUNT(*) AS TotalOrders FROM Orders;
2. SUM()
-
Definition: Calculates the total of numeric values.
-
Treatment of NULLs: Ignores them.
-
Example:
SELECT SUM(amount) AS TotalRevenue FROM Sales;
3. AVG()
-
Definition: Calculates the average (mean) of numeric values.
-
Calculation:
SUM / COUNT
of non-NULLs. -
Example:
SELECT AVG(salary) AS AvgSalary FROM Employees;
4. MIN()
and MAX()
-
Definition:
MIN()
: Smallest value in a column.MAX()
: Largest value in a column.
-
Examples:
SELECT MIN(salary) AS LowestSalary, MAX(salary) AS HighestSalary FROM Employees;
Working with GROUP BY
Aggregate functions are most powerful when combined with GROUP BY
, which groups rows based on one or more columns before applying the function .
Example: Sum of units by product and location
SELECT product, location, SUM(units) AS total_units FROM Sales GROUP BY product, location;
Filtering with HAVING
The HAVING
clause filters groups based on aggregate results (unlike WHERE
, which filters rows before aggregation) .
Example: Departments with average salary over 600
SELECT department, AVG(salary) AS avg_salary FROM Employees GROUP BY department HAVING AVG(salary) > 600;
NULL Handling
Except for COUNT(*)
, aggregate functions ignore NULL values by default . This ensures summaries aren’t skewed by missing data.
Extended Aggregate Functions
Many RDBMS support more advanced options, such as:
VARIANCE()
,STDDEV()
– statistical measuresGROUPING_ID()
,LISTAGG()
,STRING_AGG()
– grouping metadata or concatenating strings- All existing aggregates support the
OVER()
clause for window functions
Example Use Case
Assume an Orders
table:
SELECT customer, COUNT(*) AS order_count, SUM(total_amount) AS total_spent, AVG(total_amount) AS avg_order_value, MIN(total_amount) AS min_order, MAX(total_amount) AS max_order FROM Orders GROUP BY customer HAVING SUM(total_amount) > 1000;
This query summarizes key metrics per customer, then filters to show only those with total spending above 1,000.
Conclusion
Aggregate functions (COUNT
, SUM
, AVG
, MIN
, MAX
, etc.) are foundational in SQL for summarizing large datasets. Combined with GROUP BY
and filtered using HAVING
, they answer essential analytical questions like totals, averages, extremes, and counts.
FAQs
They are functions that return summarized data from multiple rows.
It groups rows so aggregates are calculated for each group.
Except COUNT(*), most aggregate functions ignore NULL values.
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