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title description menu weight flux/v0/tags introduced
map() function `map()` iterates over and applies a function to input rows.
flux_v0_ref
name parent identifier
map universe universe/map
101
transformations
0.7.0

map() iterates over and applies a function to input rows.

Each input row is passed to the fn as a record, r. Each r property represents a column key-value pair. Output values must be of the following supported column types:

  • float
  • integer
  • unsigned integer
  • string
  • boolean
  • time

Output data

Output tables are the result of applying the map function (fn) to each record of the input tables. Output records are assigned to new tables based on the group key of the input stream. If the output record contains a different value for a group key column, the record is regrouped into the appropriate table. If the output record drops a group key column, that column is removed from the group key.

Preserve columns

map() drops any columns that are not mapped explicitly by column label or implicitly using the with operator in the fn function. The with operator updates a record property if it already exists, creates a new record property if it doesn’t exist, and includes all existing properties in the output record.

data
    |> map(fn: (r) => ({ r with newColumn: r._value * 2 }))
Function type signature
(<-tables: stream[A], fn: (r: A) => B, ?mergeKey: bool) => stream[B]

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

Parameters

fn

({{< req >}}) Single argument function to apply to each record. The return value must be a record.

mergeKey

(Deprecated) Merge group keys of mapped records. Default is false.

tables

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

Examples

Square the value in each row

import "sampledata"

sampledata.int()
    |> map(fn: (r) => ({r with _value: r._value * r._value}))

{{< 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

_time _value *tag
2021-01-01T00:00:00Z 4 t1
2021-01-01T00:00:10Z 100 t1
2021-01-01T00:00:20Z 49 t1
2021-01-01T00:00:30Z 289 t1
2021-01-01T00:00:40Z 225 t1
2021-01-01T00:00:50Z 16 t1
_time _value *tag
2021-01-01T00:00:00Z 361 t2
2021-01-01T00:00:10Z 16 t2
2021-01-01T00:00:20Z 9 t2
2021-01-01T00:00:30Z 361 t2
2021-01-01T00:00:40Z 169 t2
2021-01-01T00:00:50Z 1 t2

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

Create a new table with new columns

import "sampledata"

sampledata.int()
    |> map(
        fn: (r) => ({time: r._time, source: r.tag, alert: if r._value > 10 then true else false}),
    )

{{< 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

time source alert
2021-01-01T00:00:00Z t1 false
2021-01-01T00:00:10Z t1 false
2021-01-01T00:00:20Z t1 false
2021-01-01T00:00:30Z t1 true
2021-01-01T00:00:40Z t1 true
2021-01-01T00:00:50Z t1 false
2021-01-01T00:00:00Z t2 true
2021-01-01T00:00:10Z t2 false
2021-01-01T00:00:20Z t2 false
2021-01-01T00:00:30Z t2 true
2021-01-01T00:00:40Z t2 true
2021-01-01T00:00:50Z t2 false

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

Add new columns and preserve existing columns

Use the with operator on the r record to preserve columns not directly operated on by the map operation.

import "sampledata"

sampledata.int()
    |> map(fn: (r) => ({r with server: "server-${r.tag}", valueFloat: float(v: r._value)}))

{{< 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

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

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