GROUP BY

The GROUP BY clause specifies which grouping columns should be used to perform any aggregations in the SELECT clause. If the GROUP BY clause is specified, the query is always an aggregate query, even if no aggregations are present in the SELECT clause.

When a GROUP BY clause is specified, all tuples that have matching data in the grouping columns (i.e., all tuples that belong to the same group) will be combined. The values of the grouping columns themselves are unchanged, and any other columns can be combined using an aggregate function (such as count, sum, avg, etc).

GROUP BY ALL

Use GROUP BY ALL to GROUP BY all columns in the SELECT statement that are not wrapped in aggregate functions. This simplifies the syntax by allowing the columns list to be maintained in a single location, and prevents bugs by keeping the SELECT granularity aligned to the GROUP BY granularity (e.g., it prevents duplication). See examples below and the SQL Extensions page.

Multiple Dimensions

Normally, the GROUP BY clause groups along a single dimension. Using the GROUPING SETS, CUBE or ROLLUP clauses it is possible to group along multiple dimensions. See the GROUPING SETS page for more information.

Examples

Count the number of entries in the addresses table that belong to each different city:

Query

SELECT city, count(*)FROM addressesGROUP BY city;

Result

 city     | count----------+------- Lakeside |     2 Linden   |     2 Mallard  |     2

Compute the average income per city per street_name:

Query

SELECT city, street_name, avg(income)FROM addressesGROUP BY city, street_name;

Result

 city     | street_name | avg----------+-------------+------- Lakeside | Birch Ln    | 75000 Linden   | Birch Ln    | 60000 Linden   | Spruce Blvd | 50000 Mallard  | Lake View   | 40000 Mallard  | Pond Road   | 90000

GROUP BY ALL Examples

Group by city and street_name to remove any duplicate values:

Query

SELECT city, street_nameFROM addressesGROUP BY ALL;

Result

 city     | street_name----------+------------- Lakeside | Birch Ln Linden   | Birch Ln Linden   | Spruce Blvd Mallard  | Lake View Mallard  | Pond Road

Compute the average income per city per street_name. Since income is wrapped in an aggregate function, do not include it in the GROUP BY:

Query

SELECT city, street_name, avg(income)FROM addressesGROUP BY ALL;

Result

 city     | street_name | avg----------+-------------+------- Lakeside | Birch Ln    | 75000 Linden   | Birch Ln    | 60000 Linden   | Spruce Blvd | 50000 Mallard  | Lake View   | 40000 Mallard  | Pond Road   | 90000

Syntax