docs-v2/content/influxdb/cloud-dedicated/reference/sql/group-by.md

title	description	menu	weight
GROUP BY clause	Use the `GROUP BY` clause to group query data by column values.	influxdb_cloud_dedicated name parent GROUP BY clause SQL reference	203

Use the GROUP BY clause to group data by values.

GROUP BY is an optional clause used to group rows that have the same values for all columns and expressions in the list. To output an aggregation for each group, include an aggregate or selector function in the SELECT statement. When GROUP BY appears in a query, the SELECT list can only use columns that appear in the GROUP BY list or in aggregate expressions.

GROUP BY can use column aliases that are defined in the SELECT clause. GROUP BY can't use an alias named time. In a GROUP BY list, time always refers to the measurement time column.

• Syntax
• Examples

Syntax

SELECT
  AGGREGATE_FN(field1),
  tag1
FROM measurement
GROUP BY tag1

Examples

Group data by tag values

SELECT
  AVG("water_level") AS "avg_water_level",
  "location"
FROM "h2o_feet" 
GROUP BY "location"

{{< expand-wrapper >}}} {{% expand "View example results" %}}

avg_water_level	location
5.359142420303919	coyote_creek
3.530712094245885	santa_monica

{{% /expand %}} {{< /expand-wrapper >}}

Group results in 15 minute time intervals by tag:

SELECT
  "location",
  DATE_BIN(INTERVAL '15 minutes', time, TIMESTAMP '2022-01-01 00:00:00Z') AS _time,
  COUNT("water_level") AS count
FROM "h2o_feet"
WHERE 
  time >= timestamp '2019-09-17T00:00:00Z'
  AND time <= timestamp '2019-09-17T01:00:00Z'
GROUP BY
  _time,
  location
ORDER BY
  location,
  _time

{{< expand-wrapper >}}} {{% expand "View example results" %}}

The query uses the COUNT() function to count the number of water_level points per 15 minute interval. Results are then ordered by location and time.

location	_time	count
coyote_creek	2019-09-16T23:45:00Z	1
coyote_creek	2019-09-17T00:00:00Z	2
coyote_creek	2019-09-17T00:15:00Z	3
coyote_creek	2019-09-17T00:30:00Z	2
coyote_creek	2019-09-17T00:45:00Z	3
santa_monica	2019-09-16T23:45:00Z	1
santa_monica	2019-09-17T00:00:00Z	2
santa_monica	2019-09-17T00:15:00Z	3
santa_monica	2019-09-17T00:30:00Z	2
santa_monica	2019-09-17T00:45:00Z	3

{{% /expand %}} {{< /expand-wrapper >}}