Documentation

polyline.rdp() function

polyline.rdp() is experimental and subject to change at any time.

polyline.rdp() applies the Ramer Douglas Peucker (RDP) algorithm to input data to downsample curves composed of line segments into visually indistinguishable curves with fewer points.

Function type signature
(
    <-tables: stream[A],
    ?epsilon: float,
    ?retention: float,
    ?timeColumn: string,
    ?valColumn: string,
) => stream[B] where A: Record, B: Record

For more information, see Function type signatures.

Parameters

valColumn

Column with Y axis values of the given curve. Default is _value.

timeColumn

Column with X axis values of the given curve. Default is _time.

epsilon

Maximum tolerance value that determines the amount of compression.

Epsilon should be greater than 0.0.

retention

Percentage of points to retain after downsampling.

Retention rate should be between 0.0 and 100.0.

tables

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

Examples

Downsample data using the RDP algorithm

When using polyline.rdp(), leave both epsilon and retention unspecified to automatically calculate the maximum tolerance for producing a visually indistinguishable curve.

import "experimental/polyline"

data
    |> polyline.rdp()

View example input and output

Downsample data using the RDP algorithm with an epsilon of 1.5

import "experimental/polyline"

data
    |> polyline.rdp(epsilon: 1.5)

View example input and output

Downsample data using the RDP algorithm with a retention rate of 90%

import "experimental/polyline"

data
    |> polyline.rdp(retention: 90.0)

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