docs-v2/content/shared/sql-reference/group-by.md

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.

[!Note]

Group by aliases

• GROUP BY can use column aliases that are defined in the SELECT clause.
• GROUP BY won't use an aliased value if the alias is the same as the original column name. GROUP BY uses the original value of the column, not the transformed, aliased value. We recommended using column ordinals in in the GROUP BY clause to group by transformed values and maintain the alias identifier.

• 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 data into 15 minute time intervals by tag

SELECT
  location,
  DATE_BIN(INTERVAL '15 minutes', time) 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 1, location
ORDER BY location, 1

{{< 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 >}}