Create Anomaly Detector

prometheusservice_create_anomaly_detector

R Documentation

Creates an anomaly detector within a workspace using the Random Cut Forest algorithm for time-series analysis

Description

Creates an anomaly detector within a workspace using the Random Cut Forest algorithm for time-series analysis. The anomaly detector analyzes Amazon Managed Service for Prometheus metrics to identify unusual patterns and behaviors.

Usage

prometheusservice_create_anomaly_detector(workspaceId, alias,
  evaluationIntervalInSeconds, missingDataAction, configuration, labels,
  clientToken, tags)

Arguments

workspaceId	[required] The identifier of the workspace where the anomaly detector will be created.
alias	[required] A user-friendly name for the anomaly detector.
evaluationIntervalInSeconds	The frequency, in seconds, at which the anomaly detector evaluates metrics. The default value is 60 seconds.
missingDataAction	Specifies the action to take when data is missing during evaluation.
configuration	[required] The algorithm configuration for the anomaly detector.
labels	The Amazon Managed Service for Prometheus metric labels to associate with the anomaly detector.
clientToken	A unique, case-sensitive identifier that you provide to ensure the idempotency of the request.
tags	The metadata to apply to the anomaly detector to assist with categorization and organization.

Value

A list with the following syntax:

list(
  anomalyDetectorId = "string",
  arn = "string",
  status = list(
    statusCode = "CREATING"|"ACTIVE"|"UPDATING"|"DELETING"|"CREATION_FAILED"|"UPDATE_FAILED"|"DELETION_FAILED",
    statusReason = "string"
  ),
  tags = list(
    "string"
  )
)

Request syntax

svc$create_anomaly_detector(
  workspaceId = "string",
  alias = "string",
  evaluationIntervalInSeconds = 123,
  missingDataAction = list(
    markAsAnomaly = TRUE|FALSE,
    skip = TRUE|FALSE
  ),
  configuration = list(
    randomCutForest = list(
      query = "string",
      shingleSize = 123,
      sampleSize = 123,
      ignoreNearExpectedFromAbove = list(
        amount = 123.0,
        ratio = 123.0
      ),
      ignoreNearExpectedFromBelow = list(
        amount = 123.0,
        ratio = 123.0
      )
    )
  ),
  labels = list(
    "string"
  ),
  clientToken = "string",
  tags = list(
    "string"
  )
)