filter() function

The filter() function filters data based on conditions defined in a predicate function (fn). The output tables have the same schema as the corresponding input tables.

filter(
  fn: (r) => r._measurement == "cpu",
  onEmpty: "drop"
)

Parameters

fn

A single argument predicate function that evaluates true or false. Records are passed to the function. Those that evaluate to true are included in the output tables. Records that evaluate to null or false are not included in the output tables.

onEmpty

Defines the behavior for empty tables. Potential values are keep and drop. Defaults to drop.

drop

Tables without rows are dropped.

keep

Tables without rows are output to the next transformation.

tables

Input data. Default is piped-forward data (<-).

Examples

• Filter based on InfluxDB measurement, field, and tag
• Keep empty tables when filtering
• Filter out null values
• Filter values based on thresholds

Filter based on InfluxDB measurement, field, and tag

from(bucket:"example-bucket")
  |> range(start:-1h)
  |> filter(fn: (r) =>
    r._measurement == "cpu" and
    r._field == "usage_system" and
    r.cpu == "cpu-total"
  )

Keep empty tables when filtering

The following example uses data provided by the sampledata package.

import "sampledata"
import "experimental/table"

sampledata.int()
  |> filter(fn: (r) => r._value > 18, onEmpty: "keep")

Filter out null values

The following example uses data provided by the sampledata package.

import "sampledata"

sampledata.int(includeNull: true)
  |> filter(fn: (r) => exists r._value )

Filter values based on thresholds

The following example uses data provided by the sampledata package.

import "sampledata"

sampledata.int()
  |> filter(fn: (r) => r._value > 0 and r._value < 10 )