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title description menu weight flux/v0/tags introduced
reduce() function `reduce()` aggregates rows in each input table using a reducer function (`fn`).
flux_v0_ref
name parent identifier
reduce universe universe/reduce
101
transformations
aggregates
0.23.0

reduce() aggregates rows in each input table using a reducer function (fn).

The output for each table is the group key of the table with columns corresponding to each field in the reducer record. If the reducer record contains a column with the same name as a group key column, the group key column’s value is overwritten, and the outgoing group key is changed. However, if two reduced tables write to the same destination group key, the function returns an error.

Dropped columns

reduce() drops any columns that:

  • Are not part of the input table’s group key.
  • Are not explicitly mapped in the identity record or the reducer function (fn).
Function type signature
(<-tables: stream[B], fn: (accumulator: A, r: B) => A, identity: A) => stream[C] where A: Record, B: Record, C: Record

{{% caption %}} For more information, see Function type signatures. {{% /caption %}}

Parameters

fn

({{< req >}}) Reducer function to apply to each row record (r).

The reducer function accepts two parameters:

  • r: Record representing the current row.
  • accumulator: Record returned from the reducer function's operation on the previous row.

identity

({{< req >}}) Record that defines the reducer record and provides initial values for the reducer operation on the first row.

May be used more than once in asynchronous processing use cases. The data type of values in the identity record determine the data type of output values.

tables

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

Examples

Compute the sum of the value column

import "sampledata"

sampledata.int()
    |> reduce(fn: (r, accumulator) => ({sum: r._value + accumulator.sum}), identity: {sum: 0})

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time _value *tag
2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:20Z 7 t1
2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:50Z 4 t1
_time _value *tag
2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:20Z -3 t2
2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:50Z 1 t2

Output data

*tag sum
t1 51
*tag sum
t2 53

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

Compute the sum and count in a single reducer

import "sampledata"

sampledata.int()
    |> reduce(
        fn: (r, accumulator) => ({sum: r._value + accumulator.sum, count: accumulator.count + 1}),
        identity: {sum: 0, count: 0},
    )

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time _value *tag
2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:20Z 7 t1
2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:50Z 4 t1
_time _value *tag
2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:20Z -3 t2
2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:50Z 1 t2

Output data

*tag count sum
t1 6 51
*tag count sum
t2 6 53

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

Compute the product of all values

import "sampledata"

sampledata.int()
    |> reduce(fn: (r, accumulator) => ({prod: r._value * accumulator.prod}), identity: {prod: 1})

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time _value *tag
2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:20Z 7 t1
2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:50Z 4 t1
_time _value *tag
2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:20Z -3 t2
2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:50Z 1 t2

Output data

*tag prod
t1 -142800
*tag prod
t2 -56316

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

Calculate the average of all values

import "sampledata"

sampledata.int()
    |> reduce(
        fn: (r, accumulator) =>
            ({
                count: accumulator.count + 1,
                total: accumulator.total + r._value,
                avg: float(v: accumulator.total + r._value) / float(v: accumulator.count + 1),
            }),
        identity: {count: 0, total: 0, avg: 0.0},
    )

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time _value *tag
2021-01-01T00:00:00Z -2 t1
2021-01-01T00:00:10Z 10 t1
2021-01-01T00:00:20Z 7 t1
2021-01-01T00:00:30Z 17 t1
2021-01-01T00:00:40Z 15 t1
2021-01-01T00:00:50Z 4 t1
_time _value *tag
2021-01-01T00:00:00Z 19 t2
2021-01-01T00:00:10Z 4 t2
2021-01-01T00:00:20Z -3 t2
2021-01-01T00:00:30Z 19 t2
2021-01-01T00:00:40Z 13 t2
2021-01-01T00:00:50Z 1 t2

Output data

*tag avg count total
t1 8.5 6 51
*tag avg count total
t2 8.833333333333334 6 53

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