Documentation

map() function

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]

For more information, see Function type signatures.

Parameters

fn

(Required) 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}))

View example input and output

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}),
    )

View example input and output

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)}))

View example input and output


Was this page helpful?

Thank you for your feedback!


New in InfluxDB 3.5

Key enhancements in InfluxDB 3.5 and the InfluxDB 3 Explorer 1.3.

See the Blog Post

InfluxDB 3.5 is now available for both Core and Enterprise, introducing custom plugin repository support, enhanced operational visibility with queryable CLI parameters and manual node management, stronger security controls, and general performance improvements.

InfluxDB 3 Explorer 1.3 brings powerful new capabilities including Dashboards (beta) for saving and organizing your favorite queries, and cache querying for instant access to Last Value and Distinct Value caches—making Explorer a more comprehensive workspace for time series monitoring and analysis.

For more information, check out:

InfluxDB Docker latest tag changing to InfluxDB 3 Core

On November 3, 2025, the latest tag for InfluxDB Docker images will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments.

If using Docker to install and run InfluxDB, the latest tag will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments. For example, if using Docker to run InfluxDB v2, replace the latest version tag with a specific version tag in your Docker pull command–for example:

docker pull influxdb:2