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Group By

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

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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

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

Examples

Group data by tag values

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

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

avg_water_levellocation
5.359142420303919coyote_creek
3.530712094245885santa_monica

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

Group data into 15 minute time intervals by tag

sql
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.

locationtimecount
coyote_creek2019-09-16T23:45:00Z1
coyote_creek2019-09-17T00:00:00Z2
coyote_creek2019-09-17T00:15:00Z3
coyote_creek2019-09-17T00:30:00Z2
coyote_creek2019-09-17T00:45:00Z3
santa_monica2019-09-16T23:45:00Z1
santa_monica2019-09-17T00:00:00Z2
santa_monica2019-09-17T00:15:00Z3
santa_monica2019-09-17T00:30:00Z2
santa_monica2019-09-17T00:45:00Z3

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