Documentation

Operate on columns

This page documents an earlier version of InfluxDB. InfluxDB v2.3 is the latest stable version. View this page in the v2.3 documentation.

Use the following common queries to operate on columns:

These examples use NOAA water sample data.

Find and count unique values in a column

Find and count the number of unique values in a specified column. The following examples find and count unique locations where data was collected.

Find unique values

This query:

  • Uses group() to ungroup data and return results in a single table.
  • Uses keep() and unique() to return unique values in the specified column.
from(bucket: "noaa")
    |> range(start: 2019-08-17)
    |> keep(columns: ["location"])
    |> group()
    |> unique(column: "location")

Example results

location
coyote_creek
santa_monica

Count unique values

This query:

  • Uses group() to ungroup data and return results in a single table.
  • Uses keep(), unique(), and then count() to count the number of unique values.
from(bucket: "noaa")
    |> range(start: 2019-08-17)
    |> keep(columns: ["location"])
    |> group()
    |> unique(column: "location")
    |> count(column: "location")

Example results

location
2

Recalculate the _values column

To recalculate the _value column, use the with operator in map() to overwrite the existing _value column.

The following query:

  • Uses filter() to filter the average_temperature measurement.
  • Uses map() to convert Fahrenheit temperature values into Celsius.

from(bucket: "noaa")
    |> range(start: 2019-08-17)
    |> filter(fn: (r) => r._measurement == "average_temperature")
    |> map(fn: (r) => ({r with _value: (float(v: r._value) - 32.0) * 5.0 / 9.0} ))
_field _measurement _start _stop _time location _value
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:00:00Z coyote_creek 27.77777777777778
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:06:00Z coyote_creek 22.77777777777778
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:12:00Z coyote_creek 30
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:18:00Z coyote_creek 31.666666666666668
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:24:00Z coyote_creek 25
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:30:00Z coyote_creek 21.11111111111111
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:36:00Z coyote_creek 28.88888888888889
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:42:00Z coyote_creek 24.444444444444443
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:48:00Z coyote_creek 29.444444444444443
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T00:54:00Z coyote_creek 26.666666666666668
degrees average_temperature 1920-03-05T22:10:01Z 2020-03-05T22:10:01Z 2019-08-17T01:00:00Z coyote_creek 21.11111111111111
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Calculate a new column

To use values in a row to calculate and add a new column, use map(). This example below converts temperature from Fahrenheit to Celsius and maps the Celsius value to a new celsius column.

The following query:

  • Uses filter() to filter the average_temperature measurement.
  • Uses map() to create a new column calculated from existing values in each row.
from(bucket: "noaa")
    |> range(start: 2019-08-17)
    |> filter(fn: (r) => r._measurement == "average_temperature")
    |> map(fn: (r) => ({r with celsius: (r._value - 32.0) * 5.0 / 9.0}))

Example results

_start _stop _field _measurement location _time _value celsius
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:00:00Z 82 27.78
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:06:00Z 73 22.78
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:12:00Z 86 30.00
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:18:00Z 89 31.67
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:24:00Z 77 25.00
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:30:00Z 70 21.11
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:36:00Z 84 28.89
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:42:00Z 76 24.44
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:48:00Z 85 29.44
1920-03-05T22:10:01Z 2020-03-05T22:10:01Z degrees average_temperature coyote_creek 2019-08-17T00:54:00Z 80 26.67
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