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elasticstack 0.13.1 published on Thursday, Dec 11, 2025 by elastic
Schema (JSON)
elastic/terraform-provider-elasticstack

elasticstack.ElasticsearchMlAnomalyDetectionJob

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elasticstack 0.13.1 published on Thursday, Dec 11, 2025 by elastic
Schema (JSON)
elastic/terraform-provider-elasticstack

    Creates and manages Machine Learning anomaly detection jobs. See the ML Job API documentation for more details.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
import * as elasticstack from "@pulumi/elasticstack";

// Basic anomaly detection job
const example = new elasticstack.ElasticsearchMlAnomalyDetectionJob("example", {
    jobId: "example-anomaly-detector",
    description: "Example anomaly detection job for monitoring web traffic",
    groups: [
        "web",
        "monitoring",
    ],
    analysisConfig: {
        bucketSpan: "15m",
        detectors: [
            {
                "function": "count",
                detectorDescription: "Count anomalies in web traffic",
            },
            {
                "function": "mean",
                fieldName: "response_time",
                detectorDescription: "Mean response time anomalies",
            },
        ],
        influencers: [
            "client_ip",
            "status_code",
        ],
    },
    dataDescription: {
        timeField: "@timestamp",
        timeFormat: "epoch_ms",
    },
    analysisLimits: {
        modelMemoryLimit: "100mb",
    },
    modelPlotConfig: {
        enabled: true,
    },
    modelSnapshotRetentionDays: 30,
    resultsRetentionDays: 90,
});

    import pulumi
import pulumi_elasticstack as elasticstack

# Basic anomaly detection job
example = elasticstack.ElasticsearchMlAnomalyDetectionJob("example",
    job_id="example-anomaly-detector",
    description="Example anomaly detection job for monitoring web traffic",
    groups=[
        "web",
        "monitoring",
    ],
    analysis_config={
        "bucket_span": "15m",
        "detectors": [
            {
                "function": "count",
                "detector_description": "Count anomalies in web traffic",
            },
            {
                "function": "mean",
                "field_name": "response_time",
                "detector_description": "Mean response time anomalies",
            },
        ],
        "influencers": [
            "client_ip",
            "status_code",
        ],
    },
    data_description={
        "time_field": "@timestamp",
        "time_format": "epoch_ms",
    },
    analysis_limits={
        "model_memory_limit": "100mb",
    },
    model_plot_config={
        "enabled": True,
    },
    model_snapshot_retention_days=30,
    results_retention_days=90)

    package main

import (
	"github.com/pulumi/pulumi-terraform-provider/sdks/go/elasticstack/elasticstack"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		// Basic anomaly detection job
		_, err := elasticstack.NewElasticsearchMlAnomalyDetectionJob(ctx, "example", &elasticstack.ElasticsearchMlAnomalyDetectionJobArgs{
			JobId:       pulumi.String("example-anomaly-detector"),
			Description: pulumi.String("Example anomaly detection job for monitoring web traffic"),
			Groups: pulumi.StringArray{
				pulumi.String("web"),
				pulumi.String("monitoring"),
			},
			AnalysisConfig: &elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs{
				BucketSpan: pulumi.String("15m"),
				Detectors: elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArray{
					&elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs{
						Function:            pulumi.String("count"),
						DetectorDescription: pulumi.String("Count anomalies in web traffic"),
					},
					&elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs{
						Function:            pulumi.String("mean"),
						FieldName:           pulumi.String("response_time"),
						DetectorDescription: pulumi.String("Mean response time anomalies"),
					},
				},
				Influencers: pulumi.StringArray{
					pulumi.String("client_ip"),
					pulumi.String("status_code"),
				},
			},
			DataDescription: &elasticstack.ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs{
				TimeField:  pulumi.String("@timestamp"),
				TimeFormat: pulumi.String("epoch_ms"),
			},
			AnalysisLimits: &elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs{
				ModelMemoryLimit: pulumi.String("100mb"),
			},
			ModelPlotConfig: &elasticstack.ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs{
				Enabled: pulumi.Bool(true),
			},
			ModelSnapshotRetentionDays: pulumi.Float64(30),
			ResultsRetentionDays:       pulumi.Float64(90),
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Elasticstack = Pulumi.Elasticstack;

return await Deployment.RunAsync(() => 
{
    // Basic anomaly detection job
    var example = new Elasticstack.ElasticsearchMlAnomalyDetectionJob("example", new()
    {
        JobId = "example-anomaly-detector",
        Description = "Example anomaly detection job for monitoring web traffic",
        Groups = new[]
        {
            "web",
            "monitoring",
        },
        AnalysisConfig = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
        {
            BucketSpan = "15m",
            Detectors = new[]
            {
                new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs
                {
                    Function = "count",
                    DetectorDescription = "Count anomalies in web traffic",
                },
                new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs
                {
                    Function = "mean",
                    FieldName = "response_time",
                    DetectorDescription = "Mean response time anomalies",
                },
            },
            Influencers = new[]
            {
                "client_ip",
                "status_code",
            },
        },
        DataDescription = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
        {
            TimeField = "@timestamp",
            TimeFormat = "epoch_ms",
        },
        AnalysisLimits = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
        {
            ModelMemoryLimit = "100mb",
        },
        ModelPlotConfig = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
        {
            Enabled = true,
        },
        ModelSnapshotRetentionDays = 30,
        ResultsRetentionDays = 90,
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.elasticstack.ElasticsearchMlAnomalyDetectionJob;
import com.pulumi.elasticstack.ElasticsearchMlAnomalyDetectionJobArgs;
import com.pulumi.elasticstack.inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs;
import com.pulumi.elasticstack.inputs.ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs;
import com.pulumi.elasticstack.inputs.ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs;
import com.pulumi.elasticstack.inputs.ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        // Basic anomaly detection job
        var example = new ElasticsearchMlAnomalyDetectionJob("example", ElasticsearchMlAnomalyDetectionJobArgs.builder()
            .jobId("example-anomaly-detector")
            .description("Example anomaly detection job for monitoring web traffic")
            .groups(            
                "web",
                "monitoring")
            .analysisConfig(ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs.builder()
                .bucketSpan("15m")
                .detectors(                
                    ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs.builder()
                        .function("count")
                        .detectorDescription("Count anomalies in web traffic")
                        .build(),
                    ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs.builder()
                        .function("mean")
                        .fieldName("response_time")
                        .detectorDescription("Mean response time anomalies")
                        .build())
                .influencers(                
                    "client_ip",
                    "status_code")
                .build())
            .dataDescription(ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs.builder()
                .timeField("@timestamp")
                .timeFormat("epoch_ms")
                .build())
            .analysisLimits(ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs.builder()
                .modelMemoryLimit("100mb")
                .build())
            .modelPlotConfig(ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs.builder()
                .enabled(true)
                .build())
            .modelSnapshotRetentionDays(30.0)
            .resultsRetentionDays(90.0)
            .build());

    }
}

    resources:
  # Basic anomaly detection job
  example:
    type: elasticstack:ElasticsearchMlAnomalyDetectionJob
    properties:
      jobId: example-anomaly-detector
      description: Example anomaly detection job for monitoring web traffic
      groups:
        - web
        - monitoring
      analysisConfig:
        bucketSpan: 15m
        detectors:
          - function: count
            detectorDescription: Count anomalies in web traffic
          - function: mean
            fieldName: response_time
            detectorDescription: Mean response time anomalies
        influencers:
          - client_ip
          - status_code
      dataDescription:
        timeField: '@timestamp'
        timeFormat: epoch_ms
      analysisLimits:
        modelMemoryLimit: 100mb
      modelPlotConfig:
        enabled: true
      modelSnapshotRetentionDays: 30
      resultsRetentionDays: 90

    Create ElasticsearchMlAnomalyDetectionJob Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new ElasticsearchMlAnomalyDetectionJob(name: string, args: ElasticsearchMlAnomalyDetectionJobArgs, opts?: CustomResourceOptions);
    @overload
def ElasticsearchMlAnomalyDetectionJob(resource_name: str,
                                       args: ElasticsearchMlAnomalyDetectionJobArgs,
                                       opts: Optional[ResourceOptions] = None)

@overload
def ElasticsearchMlAnomalyDetectionJob(resource_name: str,
                                       opts: Optional[ResourceOptions] = None,
                                       data_description: Optional[ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs] = None,
                                       analysis_config: Optional[ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs] = None,
                                       job_id: Optional[str] = None,
                                       elasticsearch_connections: Optional[Sequence[ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs]] = None,
                                       custom_settings: Optional[str] = None,
                                       daily_model_snapshot_retention_after_days: Optional[float] = None,
                                       background_persist_interval: Optional[str] = None,
                                       description: Optional[str] = None,
                                       allow_lazy_open: Optional[bool] = None,
                                       groups: Optional[Sequence[str]] = None,
                                       analysis_limits: Optional[ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs] = None,
                                       model_plot_config: Optional[ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs] = None,
                                       model_snapshot_retention_days: Optional[float] = None,
                                       renormalization_window_days: Optional[float] = None,
                                       results_index_name: Optional[str] = None,
                                       results_retention_days: Optional[float] = None)
    func NewElasticsearchMlAnomalyDetectionJob(ctx *Context, name string, args ElasticsearchMlAnomalyDetectionJobArgs, opts ...ResourceOption) (*ElasticsearchMlAnomalyDetectionJob, error)
    public ElasticsearchMlAnomalyDetectionJob(string name, ElasticsearchMlAnomalyDetectionJobArgs args, CustomResourceOptions? opts = null)
    public ElasticsearchMlAnomalyDetectionJob(String name, ElasticsearchMlAnomalyDetectionJobArgs args)
public ElasticsearchMlAnomalyDetectionJob(String name, ElasticsearchMlAnomalyDetectionJobArgs args, CustomResourceOptions options)

    type: elasticstack:ElasticsearchMlAnomalyDetectionJob
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args ElasticsearchMlAnomalyDetectionJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args ElasticsearchMlAnomalyDetectionJobArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args ElasticsearchMlAnomalyDetectionJobArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ElasticsearchMlAnomalyDetectionJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ElasticsearchMlAnomalyDetectionJobArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var elasticsearchMlAnomalyDetectionJobResource = new Elasticstack.ElasticsearchMlAnomalyDetectionJob("elasticsearchMlAnomalyDetectionJobResource", new()
{
    DataDescription = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
    {
        TimeField = "string",
        TimeFormat = "string",
    },
    AnalysisConfig = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
    {
        Detectors = new[]
        {
            new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs
            {
                Function = "string",
                ByFieldName = "string",
                CustomRules = new[]
                {
                    new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleArgs
                    {
                        Actions = new[]
                        {
                            "string",
                        },
                        Conditions = new[]
                        {
                            new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleConditionArgs
                            {
                                AppliesTo = "string",
                                Operator = "string",
                                Value = 0,
                            },
                        },
                    },
                },
                DetectorDescription = "string",
                ExcludeFrequent = "string",
                FieldName = "string",
                OverFieldName = "string",
                PartitionFieldName = "string",
                UseNull = false,
            },
        },
        BucketSpan = "string",
        CategorizationFieldName = "string",
        CategorizationFilters = new[]
        {
            "string",
        },
        Influencers = new[]
        {
            "string",
        },
        Latency = "string",
        ModelPruneWindow = "string",
        MultivariateByFields = false,
        PerPartitionCategorization = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorizationArgs
        {
            Enabled = false,
            StopOnWarn = false,
        },
        SummaryCountFieldName = "string",
    },
    JobId = "string",
    CustomSettings = "string",
    DailyModelSnapshotRetentionAfterDays = 0,
    BackgroundPersistInterval = "string",
    Description = "string",
    AllowLazyOpen = false,
    Groups = new[]
    {
        "string",
    },
    AnalysisLimits = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
    {
        CategorizationExamplesLimit = 0,
        ModelMemoryLimit = "string",
    },
    ModelPlotConfig = new Elasticstack.Inputs.ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
    {
        AnnotationsEnabled = false,
        Enabled = false,
        Terms = "string",
    },
    ModelSnapshotRetentionDays = 0,
    RenormalizationWindowDays = 0,
    ResultsIndexName = "string",
    ResultsRetentionDays = 0,
});

    example, err := elasticstack.NewElasticsearchMlAnomalyDetectionJob(ctx, "elasticsearchMlAnomalyDetectionJobResource", &elasticstack.ElasticsearchMlAnomalyDetectionJobArgs{
	DataDescription: &elasticstack.ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs{
		TimeField:  pulumi.String("string"),
		TimeFormat: pulumi.String("string"),
	},
	AnalysisConfig: &elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs{
		Detectors: elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArray{
			&elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs{
				Function:    pulumi.String("string"),
				ByFieldName: pulumi.String("string"),
				CustomRules: elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleArray{
					&elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleArgs{
						Actions: pulumi.StringArray{
							pulumi.String("string"),
						},
						Conditions: elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleConditionArray{
							&elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleConditionArgs{
								AppliesTo: pulumi.String("string"),
								Operator:  pulumi.String("string"),
								Value:     pulumi.Float64(0),
							},
						},
					},
				},
				DetectorDescription: pulumi.String("string"),
				ExcludeFrequent:     pulumi.String("string"),
				FieldName:           pulumi.String("string"),
				OverFieldName:       pulumi.String("string"),
				PartitionFieldName:  pulumi.String("string"),
				UseNull:             pulumi.Bool(false),
			},
		},
		BucketSpan:              pulumi.String("string"),
		CategorizationFieldName: pulumi.String("string"),
		CategorizationFilters: pulumi.StringArray{
			pulumi.String("string"),
		},
		Influencers: pulumi.StringArray{
			pulumi.String("string"),
		},
		Latency:              pulumi.String("string"),
		ModelPruneWindow:     pulumi.String("string"),
		MultivariateByFields: pulumi.Bool(false),
		PerPartitionCategorization: &elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorizationArgs{
			Enabled:    pulumi.Bool(false),
			StopOnWarn: pulumi.Bool(false),
		},
		SummaryCountFieldName: pulumi.String("string"),
	},
	JobId:                                pulumi.String("string"),
	CustomSettings:                       pulumi.String("string"),
	DailyModelSnapshotRetentionAfterDays: pulumi.Float64(0),
	BackgroundPersistInterval:            pulumi.String("string"),
	Description:                          pulumi.String("string"),
	AllowLazyOpen:                        pulumi.Bool(false),
	Groups: pulumi.StringArray{
		pulumi.String("string"),
	},
	AnalysisLimits: &elasticstack.ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs{
		CategorizationExamplesLimit: pulumi.Float64(0),
		ModelMemoryLimit:            pulumi.String("string"),
	},
	ModelPlotConfig: &elasticstack.ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs{
		AnnotationsEnabled: pulumi.Bool(false),
		Enabled:            pulumi.Bool(false),
		Terms:              pulumi.String("string"),
	},
	ModelSnapshotRetentionDays: pulumi.Float64(0),
	RenormalizationWindowDays:  pulumi.Float64(0),
	ResultsIndexName:           pulumi.String("string"),
	ResultsRetentionDays:       pulumi.Float64(0),
})

    var elasticsearchMlAnomalyDetectionJobResource = new ElasticsearchMlAnomalyDetectionJob("elasticsearchMlAnomalyDetectionJobResource", ElasticsearchMlAnomalyDetectionJobArgs.builder()
    .dataDescription(ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs.builder()
        .timeField("string")
        .timeFormat("string")
        .build())
    .analysisConfig(ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs.builder()
        .detectors(ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs.builder()
            .function("string")
            .byFieldName("string")
            .customRules(ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleArgs.builder()
                .actions("string")
                .conditions(ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleConditionArgs.builder()
                    .appliesTo("string")
                    .operator("string")
                    .value(0.0)
                    .build())
                .build())
            .detectorDescription("string")
            .excludeFrequent("string")
            .fieldName("string")
            .overFieldName("string")
            .partitionFieldName("string")
            .useNull(false)
            .build())
        .bucketSpan("string")
        .categorizationFieldName("string")
        .categorizationFilters("string")
        .influencers("string")
        .latency("string")
        .modelPruneWindow("string")
        .multivariateByFields(false)
        .perPartitionCategorization(ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorizationArgs.builder()
            .enabled(false)
            .stopOnWarn(false)
            .build())
        .summaryCountFieldName("string")
        .build())
    .jobId("string")
    .customSettings("string")
    .dailyModelSnapshotRetentionAfterDays(0.0)
    .backgroundPersistInterval("string")
    .description("string")
    .allowLazyOpen(false)
    .groups("string")
    .analysisLimits(ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs.builder()
        .categorizationExamplesLimit(0.0)
        .modelMemoryLimit("string")
        .build())
    .modelPlotConfig(ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs.builder()
        .annotationsEnabled(false)
        .enabled(false)
        .terms("string")
        .build())
    .modelSnapshotRetentionDays(0.0)
    .renormalizationWindowDays(0.0)
    .resultsIndexName("string")
    .resultsRetentionDays(0.0)
    .build());

    elasticsearch_ml_anomaly_detection_job_resource = elasticstack.ElasticsearchMlAnomalyDetectionJob("elasticsearchMlAnomalyDetectionJobResource",
    data_description={
        "time_field": "string",
        "time_format": "string",
    },
    analysis_config={
        "detectors": [{
            "function": "string",
            "by_field_name": "string",
            "custom_rules": [{
                "actions": ["string"],
                "conditions": [{
                    "applies_to": "string",
                    "operator": "string",
                    "value": 0,
                }],
            }],
            "detector_description": "string",
            "exclude_frequent": "string",
            "field_name": "string",
            "over_field_name": "string",
            "partition_field_name": "string",
            "use_null": False,
        }],
        "bucket_span": "string",
        "categorization_field_name": "string",
        "categorization_filters": ["string"],
        "influencers": ["string"],
        "latency": "string",
        "model_prune_window": "string",
        "multivariate_by_fields": False,
        "per_partition_categorization": {
            "enabled": False,
            "stop_on_warn": False,
        },
        "summary_count_field_name": "string",
    },
    job_id="string",
    custom_settings="string",
    daily_model_snapshot_retention_after_days=0,
    background_persist_interval="string",
    description="string",
    allow_lazy_open=False,
    groups=["string"],
    analysis_limits={
        "categorization_examples_limit": 0,
        "model_memory_limit": "string",
    },
    model_plot_config={
        "annotations_enabled": False,
        "enabled": False,
        "terms": "string",
    },
    model_snapshot_retention_days=0,
    renormalization_window_days=0,
    results_index_name="string",
    results_retention_days=0)

    const elasticsearchMlAnomalyDetectionJobResource = new elasticstack.ElasticsearchMlAnomalyDetectionJob("elasticsearchMlAnomalyDetectionJobResource", {
    dataDescription: {
        timeField: "string",
        timeFormat: "string",
    },
    analysisConfig: {
        detectors: [{
            "function": "string",
            byFieldName: "string",
            customRules: [{
                actions: ["string"],
                conditions: [{
                    appliesTo: "string",
                    operator: "string",
                    value: 0,
                }],
            }],
            detectorDescription: "string",
            excludeFrequent: "string",
            fieldName: "string",
            overFieldName: "string",
            partitionFieldName: "string",
            useNull: false,
        }],
        bucketSpan: "string",
        categorizationFieldName: "string",
        categorizationFilters: ["string"],
        influencers: ["string"],
        latency: "string",
        modelPruneWindow: "string",
        multivariateByFields: false,
        perPartitionCategorization: {
            enabled: false,
            stopOnWarn: false,
        },
        summaryCountFieldName: "string",
    },
    jobId: "string",
    customSettings: "string",
    dailyModelSnapshotRetentionAfterDays: 0,
    backgroundPersistInterval: "string",
    description: "string",
    allowLazyOpen: false,
    groups: ["string"],
    analysisLimits: {
        categorizationExamplesLimit: 0,
        modelMemoryLimit: "string",
    },
    modelPlotConfig: {
        annotationsEnabled: false,
        enabled: false,
        terms: "string",
    },
    modelSnapshotRetentionDays: 0,
    renormalizationWindowDays: 0,
    resultsIndexName: "string",
    resultsRetentionDays: 0,
});

    type: elasticstack:ElasticsearchMlAnomalyDetectionJob
properties:
    allowLazyOpen: false
    analysisConfig:
        bucketSpan: string
        categorizationFieldName: string
        categorizationFilters:
            - string
        detectors:
            - byFieldName: string
              customRules:
                - actions:
                    - string
                  conditions:
                    - appliesTo: string
                      operator: string
                      value: 0
              detectorDescription: string
              excludeFrequent: string
              fieldName: string
              function: string
              overFieldName: string
              partitionFieldName: string
              useNull: false
        influencers:
            - string
        latency: string
        modelPruneWindow: string
        multivariateByFields: false
        perPartitionCategorization:
            enabled: false
            stopOnWarn: false
        summaryCountFieldName: string
    analysisLimits:
        categorizationExamplesLimit: 0
        modelMemoryLimit: string
    backgroundPersistInterval: string
    customSettings: string
    dailyModelSnapshotRetentionAfterDays: 0
    dataDescription:
        timeField: string
        timeFormat: string
    description: string
    groups:
        - string
    jobId: string
    modelPlotConfig:
        annotationsEnabled: false
        enabled: false
        terms: string
    modelSnapshotRetentionDays: 0
    renormalizationWindowDays: 0
    resultsIndexName: string
    resultsRetentionDays: 0

    ElasticsearchMlAnomalyDetectionJob Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The ElasticsearchMlAnomalyDetectionJob resource accepts the following input properties:

    AnalysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    DataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    JobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    AllowLazyOpen bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    AnalysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    BackgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    CustomSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    DailyModelSnapshotRetentionAfterDays double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    Description string
    A description of the job.
    ElasticsearchConnections List<ElasticsearchMlAnomalyDetectionJobElasticsearchConnection>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    Groups List<string>
    A set of job groups. A job can belong to no groups or many.
    ModelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    ModelSnapshotRetentionDays double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    RenormalizationWindowDays double
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    ResultsIndexName string
    A text string that affects the name of the machine learning results index.
    ResultsRetentionDays double
    Advanced configuration option. The period of time (in days) that results are retained.
    AnalysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    DataDescription ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
    Defines the format of the input data when you send data to the job by using the post data API.
    JobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    AllowLazyOpen bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    AnalysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
    Limits can be applied for the resources required to hold the mathematical models in memory.
    BackgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    CustomSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    DailyModelSnapshotRetentionAfterDays float64
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    Description string
    A description of the job.
    ElasticsearchConnections []ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    Groups []string
    A set of job groups. A job can belong to no groups or many.
    ModelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    ModelSnapshotRetentionDays float64
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    RenormalizationWindowDays float64
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    ResultsIndexName string
    A text string that affects the name of the machine learning results index.
    ResultsRetentionDays float64
    Advanced configuration option. The period of time (in days) that results are retained.
    analysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    dataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    jobId String
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    allowLazyOpen Boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval String
    Advanced configuration option. The time between each periodic persistence of the model.
    customSettings String
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays Double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    description String
    A description of the job.
    elasticsearchConnections List<ElasticsearchMlAnomalyDetectionJobElasticsearchConnection>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups List<String>
    A set of job groups. A job can belong to no groups or many.
    modelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotRetentionDays Double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays Double
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName String
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays Double
    Advanced configuration option. The period of time (in days) that results are retained.
    analysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    dataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    jobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    allowLazyOpen boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    customSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    description string
    A description of the job.
    elasticsearchConnections ElasticsearchMlAnomalyDetectionJobElasticsearchConnection[]
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups string[]
    A set of job groups. A job can belong to no groups or many.
    modelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotRetentionDays number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays number
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName string
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays number
    Advanced configuration option. The period of time (in days) that results are retained.
    analysis_config ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    data_description ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
    Defines the format of the input data when you send data to the job by using the post data API.
    job_id str
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    allow_lazy_open bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysis_limits ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
    Limits can be applied for the resources required to hold the mathematical models in memory.
    background_persist_interval str
    Advanced configuration option. The time between each periodic persistence of the model.
    custom_settings str
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    daily_model_snapshot_retention_after_days float
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    description str
    A description of the job.
    elasticsearch_connections Sequence[ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs]
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups Sequence[str]
    A set of job groups. A job can belong to no groups or many.
    model_plot_config ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    model_snapshot_retention_days float
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalization_window_days float
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    results_index_name str
    A text string that affects the name of the machine learning results index.
    results_retention_days float
    Advanced configuration option. The period of time (in days) that results are retained.
    analysisConfig Property Map
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    dataDescription Property Map
    Defines the format of the input data when you send data to the job by using the post data API.
    jobId String
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    allowLazyOpen Boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisLimits Property Map
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval String
    Advanced configuration option. The time between each periodic persistence of the model.
    customSettings String
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays Number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    description String
    A description of the job.
    elasticsearchConnections List<Property Map>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups List<String>
    A set of job groups. A job can belong to no groups or many.
    modelPlotConfig Property Map
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotRetentionDays Number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays Number
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName String
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays Number
    Advanced configuration option. The period of time (in days) that results are retained.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the ElasticsearchMlAnomalyDetectionJob resource produces the following output properties:

    CreateTime string
    The time the job was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    JobType string
    Reserved for future use, currently set to anomaly_detector.
    JobVersion string
    The version of Elasticsearch when the job was created.
    ModelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    CreateTime string
    The time the job was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    JobType string
    Reserved for future use, currently set to anomaly_detector.
    JobVersion string
    The version of Elasticsearch when the job was created.
    ModelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    createTime String
    The time the job was created.
    id String
    The provider-assigned unique ID for this managed resource.
    jobType String
    Reserved for future use, currently set to anomaly_detector.
    jobVersion String
    The version of Elasticsearch when the job was created.
    modelSnapshotId String
    A numerical character string that uniquely identifies the model snapshot.
    createTime string
    The time the job was created.
    id string
    The provider-assigned unique ID for this managed resource.
    jobType string
    Reserved for future use, currently set to anomaly_detector.
    jobVersion string
    The version of Elasticsearch when the job was created.
    modelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    create_time str
    The time the job was created.
    id str
    The provider-assigned unique ID for this managed resource.
    job_type str
    Reserved for future use, currently set to anomaly_detector.
    job_version str
    The version of Elasticsearch when the job was created.
    model_snapshot_id str
    A numerical character string that uniquely identifies the model snapshot.
    createTime String
    The time the job was created.
    id String
    The provider-assigned unique ID for this managed resource.
    jobType String
    Reserved for future use, currently set to anomaly_detector.
    jobVersion String
    The version of Elasticsearch when the job was created.
    modelSnapshotId String
    A numerical character string that uniquely identifies the model snapshot.

    Look up Existing ElasticsearchMlAnomalyDetectionJob Resource

    Get an existing ElasticsearchMlAnomalyDetectionJob resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: ElasticsearchMlAnomalyDetectionJobState, opts?: CustomResourceOptions): ElasticsearchMlAnomalyDetectionJob
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        allow_lazy_open: Optional[bool] = None,
        analysis_config: Optional[ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs] = None,
        analysis_limits: Optional[ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs] = None,
        background_persist_interval: Optional[str] = None,
        create_time: Optional[str] = None,
        custom_settings: Optional[str] = None,
        daily_model_snapshot_retention_after_days: Optional[float] = None,
        data_description: Optional[ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs] = None,
        description: Optional[str] = None,
        elasticsearch_connections: Optional[Sequence[ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs]] = None,
        groups: Optional[Sequence[str]] = None,
        job_id: Optional[str] = None,
        job_type: Optional[str] = None,
        job_version: Optional[str] = None,
        model_plot_config: Optional[ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs] = None,
        model_snapshot_id: Optional[str] = None,
        model_snapshot_retention_days: Optional[float] = None,
        renormalization_window_days: Optional[float] = None,
        results_index_name: Optional[str] = None,
        results_retention_days: Optional[float] = None) -> ElasticsearchMlAnomalyDetectionJob
    func GetElasticsearchMlAnomalyDetectionJob(ctx *Context, name string, id IDInput, state *ElasticsearchMlAnomalyDetectionJobState, opts ...ResourceOption) (*ElasticsearchMlAnomalyDetectionJob, error)
    public static ElasticsearchMlAnomalyDetectionJob Get(string name, Input<string> id, ElasticsearchMlAnomalyDetectionJobState? state, CustomResourceOptions? opts = null)
    public static ElasticsearchMlAnomalyDetectionJob get(String name, Output<String> id, ElasticsearchMlAnomalyDetectionJobState state, CustomResourceOptions options)
    resources:  _:    type: elasticstack:ElasticsearchMlAnomalyDetectionJob    get:      id: ${id}
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    AllowLazyOpen bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    AnalysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    AnalysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    BackgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    CreateTime string
    The time the job was created.
    CustomSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    DailyModelSnapshotRetentionAfterDays double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    DataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    Description string
    A description of the job.
    ElasticsearchConnections List<ElasticsearchMlAnomalyDetectionJobElasticsearchConnection>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    Groups List<string>
    A set of job groups. A job can belong to no groups or many.
    JobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    JobType string
    Reserved for future use, currently set to anomaly_detector.
    JobVersion string
    The version of Elasticsearch when the job was created.
    ModelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    ModelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    ModelSnapshotRetentionDays double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    RenormalizationWindowDays double
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    ResultsIndexName string
    A text string that affects the name of the machine learning results index.
    ResultsRetentionDays double
    Advanced configuration option. The period of time (in days) that results are retained.
    AllowLazyOpen bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    AnalysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    AnalysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
    Limits can be applied for the resources required to hold the mathematical models in memory.
    BackgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    CreateTime string
    The time the job was created.
    CustomSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    DailyModelSnapshotRetentionAfterDays float64
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    DataDescription ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
    Defines the format of the input data when you send data to the job by using the post data API.
    Description string
    A description of the job.
    ElasticsearchConnections []ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    Groups []string
    A set of job groups. A job can belong to no groups or many.
    JobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    JobType string
    Reserved for future use, currently set to anomaly_detector.
    JobVersion string
    The version of Elasticsearch when the job was created.
    ModelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    ModelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    ModelSnapshotRetentionDays float64
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    RenormalizationWindowDays float64
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    ResultsIndexName string
    A text string that affects the name of the machine learning results index.
    ResultsRetentionDays float64
    Advanced configuration option. The period of time (in days) that results are retained.
    allowLazyOpen Boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    analysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval String
    Advanced configuration option. The time between each periodic persistence of the model.
    createTime String
    The time the job was created.
    customSettings String
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays Double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    dataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    description String
    A description of the job.
    elasticsearchConnections List<ElasticsearchMlAnomalyDetectionJobElasticsearchConnection>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups List<String>
    A set of job groups. A job can belong to no groups or many.
    jobId String
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    jobType String
    Reserved for future use, currently set to anomaly_detector.
    jobVersion String
    The version of Elasticsearch when the job was created.
    modelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotId String
    A numerical character string that uniquely identifies the model snapshot.
    modelSnapshotRetentionDays Double
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays Double
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName String
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays Double
    Advanced configuration option. The period of time (in days) that results are retained.
    allowLazyOpen boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisConfig ElasticsearchMlAnomalyDetectionJobAnalysisConfig
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    analysisLimits ElasticsearchMlAnomalyDetectionJobAnalysisLimits
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval string
    Advanced configuration option. The time between each periodic persistence of the model.
    createTime string
    The time the job was created.
    customSettings string
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    dataDescription ElasticsearchMlAnomalyDetectionJobDataDescription
    Defines the format of the input data when you send data to the job by using the post data API.
    description string
    A description of the job.
    elasticsearchConnections ElasticsearchMlAnomalyDetectionJobElasticsearchConnection[]
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups string[]
    A set of job groups. A job can belong to no groups or many.
    jobId string
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    jobType string
    Reserved for future use, currently set to anomaly_detector.
    jobVersion string
    The version of Elasticsearch when the job was created.
    modelPlotConfig ElasticsearchMlAnomalyDetectionJobModelPlotConfig
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotId string
    A numerical character string that uniquely identifies the model snapshot.
    modelSnapshotRetentionDays number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays number
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName string
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays number
    Advanced configuration option. The period of time (in days) that results are retained.
    allow_lazy_open bool
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysis_config ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    analysis_limits ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs
    Limits can be applied for the resources required to hold the mathematical models in memory.
    background_persist_interval str
    Advanced configuration option. The time between each periodic persistence of the model.
    create_time str
    The time the job was created.
    custom_settings str
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    daily_model_snapshot_retention_after_days float
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    data_description ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs
    Defines the format of the input data when you send data to the job by using the post data API.
    description str
    A description of the job.
    elasticsearch_connections Sequence[ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs]
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups Sequence[str]
    A set of job groups. A job can belong to no groups or many.
    job_id str
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    job_type str
    Reserved for future use, currently set to anomaly_detector.
    job_version str
    The version of Elasticsearch when the job was created.
    model_plot_config ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    model_snapshot_id str
    A numerical character string that uniquely identifies the model snapshot.
    model_snapshot_retention_days float
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalization_window_days float
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    results_index_name str
    A text string that affects the name of the machine learning results index.
    results_retention_days float
    Advanced configuration option. The period of time (in days) that results are retained.
    allowLazyOpen Boolean
    Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
    analysisConfig Property Map
    Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
    analysisLimits Property Map
    Limits can be applied for the resources required to hold the mathematical models in memory.
    backgroundPersistInterval String
    Advanced configuration option. The time between each periodic persistence of the model.
    createTime String
    The time the job was created.
    customSettings String
    Advanced configuration option. Contains custom meta data about the job. For example, it can contain custom URL information.
    dailyModelSnapshotRetentionAfterDays Number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    dataDescription Property Map
    Defines the format of the input data when you send data to the job by using the post data API.
    description String
    A description of the job.
    elasticsearchConnections List<Property Map>
    Elasticsearch connection configuration block.

    Deprecated: Deprecated

    groups List<String>
    A set of job groups. A job can belong to no groups or many.
    jobId String
    The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
    jobType String
    Reserved for future use, currently set to anomaly_detector.
    jobVersion String
    The version of Elasticsearch when the job was created.
    modelPlotConfig Property Map
    This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection.
    modelSnapshotId String
    A numerical character string that uniquely identifies the model snapshot.
    modelSnapshotRetentionDays Number
    Advanced configuration option, which affects the automatic removal of old model snapshots for this job.
    renormalizationWindowDays Number
    Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.
    resultsIndexName String
    A text string that affects the name of the machine learning results index.
    resultsRetentionDays Number
    Advanced configuration option. The period of time (in days) that results are retained.

    Supporting Types

    ElasticsearchMlAnomalyDetectionJobAnalysisConfig, ElasticsearchMlAnomalyDetectionJobAnalysisConfigArgs

    Detectors List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector>
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    BucketSpan string
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    CategorizationFieldName string
    For categorization jobs only. The name of the field to categorize.
    CategorizationFilters List<string>
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    Influencers List<string>
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    Latency string
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    ModelPruneWindow string
    Advanced configuration option. The time interval (in days) between pruning the model.
    MultivariateByFields bool
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    PerPartitionCategorization ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization
    Settings related to how categorization interacts with partition fields.
    SummaryCountFieldName string
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.
    Detectors []ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    BucketSpan string
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    CategorizationFieldName string
    For categorization jobs only. The name of the field to categorize.
    CategorizationFilters []string
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    Influencers []string
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    Latency string
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    ModelPruneWindow string
    Advanced configuration option. The time interval (in days) between pruning the model.
    MultivariateByFields bool
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    PerPartitionCategorization ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization
    Settings related to how categorization interacts with partition fields.
    SummaryCountFieldName string
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.
    detectors List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector>
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    bucketSpan String
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    categorizationFieldName String
    For categorization jobs only. The name of the field to categorize.
    categorizationFilters List<String>
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    influencers List<String>
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    latency String
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    modelPruneWindow String
    Advanced configuration option. The time interval (in days) between pruning the model.
    multivariateByFields Boolean
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    perPartitionCategorization ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization
    Settings related to how categorization interacts with partition fields.
    summaryCountFieldName String
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.
    detectors ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector[]
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    bucketSpan string
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    categorizationFieldName string
    For categorization jobs only. The name of the field to categorize.
    categorizationFilters string[]
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    influencers string[]
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    latency string
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    modelPruneWindow string
    Advanced configuration option. The time interval (in days) between pruning the model.
    multivariateByFields boolean
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    perPartitionCategorization ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization
    Settings related to how categorization interacts with partition fields.
    summaryCountFieldName string
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.
    detectors Sequence[ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector]
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    bucket_span str
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    categorization_field_name str
    For categorization jobs only. The name of the field to categorize.
    categorization_filters Sequence[str]
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    influencers Sequence[str]
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    latency str
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    model_prune_window str
    Advanced configuration option. The time interval (in days) between pruning the model.
    multivariate_by_fields bool
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    per_partition_categorization ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization
    Settings related to how categorization interacts with partition fields.
    summary_count_field_name str
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.
    detectors List<Property Map>
    Detector configuration objects. Detectors identify the anomaly detection functions and the fields on which they operate.
    bucketSpan String
    The size of the interval that the analysis is aggregated into, typically between 15m and 1h. If the anomaly detector is expecting to see data at near real-time frequency, then the bucketspan should be set to a value around 10 times the time between ingested documents. For example, if data comes every second, bucketspan should be 10s; if data comes every 5 minutes, bucketspan should be 50m. For sparse or batch data, use larger bucketspan values.
    categorizationFieldName String
    For categorization jobs only. The name of the field to categorize.
    categorizationFilters List<String>
    For categorization jobs only. An array of regular expressions. A categorization message is matched against each regex in the order they are listed in the array.
    influencers List<String>
    A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration.
    latency String
    The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second.
    modelPruneWindow String
    Advanced configuration option. The time interval (in days) between pruning the model.
    multivariateByFields Boolean
    This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features.
    perPartitionCategorization Property Map
    Settings related to how categorization interacts with partition fields.
    summaryCountFieldName String
    If this property is specified, the data that is fed to the job is expected to be pre-summarized.

    ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetector, ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorArgs

    Function string
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    ByFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    CustomRules List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule>
    Custom rules enable you to customize the way detectors operate.
    DetectorDescription string
    A description of the detector.
    ExcludeFrequent string
    Contains one of the following values: all, none, by, or over.
    FieldName string
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    OverFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    PartitionFieldName string
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    UseNull bool
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.
    Function string
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    ByFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    CustomRules []ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule
    Custom rules enable you to customize the way detectors operate.
    DetectorDescription string
    A description of the detector.
    ExcludeFrequent string
    Contains one of the following values: all, none, by, or over.
    FieldName string
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    OverFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    PartitionFieldName string
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    UseNull bool
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.
    function String
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    byFieldName String
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    customRules List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule>
    Custom rules enable you to customize the way detectors operate.
    detectorDescription String
    A description of the detector.
    excludeFrequent String
    Contains one of the following values: all, none, by, or over.
    fieldName String
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    overFieldName String
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    partitionFieldName String
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    useNull Boolean
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.
    function string
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    byFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    customRules ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule[]
    Custom rules enable you to customize the way detectors operate.
    detectorDescription string
    A description of the detector.
    excludeFrequent string
    Contains one of the following values: all, none, by, or over.
    fieldName string
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    overFieldName string
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    partitionFieldName string
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    useNull boolean
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.
    function str
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    by_field_name str
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    custom_rules Sequence[ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule]
    Custom rules enable you to customize the way detectors operate.
    detector_description str
    A description of the detector.
    exclude_frequent str
    Contains one of the following values: all, none, by, or over.
    field_name str
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    over_field_name str
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    partition_field_name str
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    use_null bool
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.
    function String
    The analysis function that is used. For example, count, rare, mean, min, max, sum.
    byFieldName String
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
    customRules List<Property Map>
    Custom rules enable you to customize the way detectors operate.
    detectorDescription String
    A description of the detector.
    excludeFrequent String
    Contains one of the following values: all, none, by, or over.
    fieldName String
    The field that the detector function analyzes. Some functions require a field. Functions that don't require a field are count, rare, and freq_rare.
    overFieldName String
    The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
    partitionFieldName String
    The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
    useNull Boolean
    Defines whether a new series is used as the null series when there is no value for the by or partition fields.

    ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRule, ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleArgs

    Actions List<string>
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    Conditions List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition>
    An array of numeric conditions when the rule applies.
    Actions []string
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    Conditions []ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition
    An array of numeric conditions when the rule applies.
    actions List<String>
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    conditions List<ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition>
    An array of numeric conditions when the rule applies.
    actions string[]
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    conditions ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition[]
    An array of numeric conditions when the rule applies.
    actions Sequence[str]
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    conditions Sequence[ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition]
    An array of numeric conditions when the rule applies.
    actions List<String>
    The set of actions to be triggered when the rule applies. If more than one action is specified the effects of all actions are combined.
    conditions List<Property Map>
    An array of numeric conditions when the rule applies.

    ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleCondition, ElasticsearchMlAnomalyDetectionJobAnalysisConfigDetectorCustomRuleConditionArgs

    AppliesTo string
    Specifies the result property to which the condition applies.
    Operator string
    Specifies the condition operator.
    Value double
    The value that is compared against the applies_to field using the operator.
    AppliesTo string
    Specifies the result property to which the condition applies.
    Operator string
    Specifies the condition operator.
    Value float64
    The value that is compared against the applies_to field using the operator.
    appliesTo String
    Specifies the result property to which the condition applies.
    operator String
    Specifies the condition operator.
    value Double
    The value that is compared against the applies_to field using the operator.
    appliesTo string
    Specifies the result property to which the condition applies.
    operator string
    Specifies the condition operator.
    value number
    The value that is compared against the applies_to field using the operator.
    applies_to str
    Specifies the result property to which the condition applies.
    operator str
    Specifies the condition operator.
    value float
    The value that is compared against the applies_to field using the operator.
    appliesTo String
    Specifies the result property to which the condition applies.
    operator String
    Specifies the condition operator.
    value Number
    The value that is compared against the applies_to field using the operator.

    ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorization, ElasticsearchMlAnomalyDetectionJobAnalysisConfigPerPartitionCategorizationArgs

    Enabled bool
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    StopOnWarn bool
    This setting can be set to true only if per-partition categorization is enabled.
    Enabled bool
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    StopOnWarn bool
    This setting can be set to true only if per-partition categorization is enabled.
    enabled Boolean
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    stopOnWarn Boolean
    This setting can be set to true only if per-partition categorization is enabled.
    enabled boolean
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    stopOnWarn boolean
    This setting can be set to true only if per-partition categorization is enabled.
    enabled bool
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    stop_on_warn bool
    This setting can be set to true only if per-partition categorization is enabled.
    enabled Boolean
    To enable this setting, you must also set the partitionfieldname property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.
    stopOnWarn Boolean
    This setting can be set to true only if per-partition categorization is enabled.

    ElasticsearchMlAnomalyDetectionJobAnalysisLimits, ElasticsearchMlAnomalyDetectionJobAnalysisLimitsArgs

    CategorizationExamplesLimit double
    The maximum number of examples stored per category in memory and in the results data store.
    ModelMemoryLimit string
    The approximate maximum amount of memory resources that are required for analytical processing.
    CategorizationExamplesLimit float64
    The maximum number of examples stored per category in memory and in the results data store.
    ModelMemoryLimit string
    The approximate maximum amount of memory resources that are required for analytical processing.
    categorizationExamplesLimit Double
    The maximum number of examples stored per category in memory and in the results data store.
    modelMemoryLimit String
    The approximate maximum amount of memory resources that are required for analytical processing.
    categorizationExamplesLimit number
    The maximum number of examples stored per category in memory and in the results data store.
    modelMemoryLimit string
    The approximate maximum amount of memory resources that are required for analytical processing.
    categorization_examples_limit float
    The maximum number of examples stored per category in memory and in the results data store.
    model_memory_limit str
    The approximate maximum amount of memory resources that are required for analytical processing.
    categorizationExamplesLimit Number
    The maximum number of examples stored per category in memory and in the results data store.
    modelMemoryLimit String
    The approximate maximum amount of memory resources that are required for analytical processing.

    ElasticsearchMlAnomalyDetectionJobDataDescription, ElasticsearchMlAnomalyDetectionJobDataDescriptionArgs

    TimeField string
    The name of the field that contains the timestamp.
    TimeFormat string
    The time format, which can be epoch, epoch_ms, or a custom pattern.
    TimeField string
    The name of the field that contains the timestamp.
    TimeFormat string
    The time format, which can be epoch, epoch_ms, or a custom pattern.
    timeField String
    The name of the field that contains the timestamp.
    timeFormat String
    The time format, which can be epoch, epoch_ms, or a custom pattern.
    timeField string
    The name of the field that contains the timestamp.
    timeFormat string
    The time format, which can be epoch, epoch_ms, or a custom pattern.
    time_field str
    The name of the field that contains the timestamp.
    time_format str
    The time format, which can be epoch, epoch_ms, or a custom pattern.
    timeField String
    The name of the field that contains the timestamp.
    timeFormat String
    The time format, which can be epoch, epoch_ms, or a custom pattern.

    ElasticsearchMlAnomalyDetectionJobElasticsearchConnection, ElasticsearchMlAnomalyDetectionJobElasticsearchConnectionArgs

    ApiKey string
    API Key to use for authentication to Elasticsearch
    BearerToken string
    Bearer Token to use for authentication to Elasticsearch
    CaData string
    PEM-encoded custom Certificate Authority certificate
    CaFile string
    Path to a custom Certificate Authority certificate
    CertData string
    PEM encoded certificate for client auth
    CertFile string
    Path to a file containing the PEM encoded certificate for client auth
    Endpoints List<string>
    EsClientAuthentication string
    ES Client Authentication field to be used with the JWT token
    Headers Dictionary<string, string>
    A list of headers to be sent with each request to Elasticsearch.
    Insecure bool
    Disable TLS certificate validation
    KeyData string
    PEM encoded private key for client auth
    KeyFile string
    Path to a file containing the PEM encoded private key for client auth
    Password string
    Password to use for API authentication to Elasticsearch.
    Username string
    Username to use for API authentication to Elasticsearch.
    ApiKey string
    API Key to use for authentication to Elasticsearch
    BearerToken string
    Bearer Token to use for authentication to Elasticsearch
    CaData string
    PEM-encoded custom Certificate Authority certificate
    CaFile string
    Path to a custom Certificate Authority certificate
    CertData string
    PEM encoded certificate for client auth
    CertFile string
    Path to a file containing the PEM encoded certificate for client auth
    Endpoints []string
    EsClientAuthentication string
    ES Client Authentication field to be used with the JWT token
    Headers map[string]string
    A list of headers to be sent with each request to Elasticsearch.
    Insecure bool
    Disable TLS certificate validation
    KeyData string
    PEM encoded private key for client auth
    KeyFile string
    Path to a file containing the PEM encoded private key for client auth
    Password string
    Password to use for API authentication to Elasticsearch.
    Username string
    Username to use for API authentication to Elasticsearch.
    apiKey String
    API Key to use for authentication to Elasticsearch
    bearerToken String
    Bearer Token to use for authentication to Elasticsearch
    caData String
    PEM-encoded custom Certificate Authority certificate
    caFile String
    Path to a custom Certificate Authority certificate
    certData String
    PEM encoded certificate for client auth
    certFile String
    Path to a file containing the PEM encoded certificate for client auth
    endpoints List<String>
    esClientAuthentication String
    ES Client Authentication field to be used with the JWT token
    headers Map<String,String>
    A list of headers to be sent with each request to Elasticsearch.
    insecure Boolean
    Disable TLS certificate validation
    keyData String
    PEM encoded private key for client auth
    keyFile String
    Path to a file containing the PEM encoded private key for client auth
    password String
    Password to use for API authentication to Elasticsearch.
    username String
    Username to use for API authentication to Elasticsearch.
    apiKey string
    API Key to use for authentication to Elasticsearch
    bearerToken string
    Bearer Token to use for authentication to Elasticsearch
    caData string
    PEM-encoded custom Certificate Authority certificate
    caFile string
    Path to a custom Certificate Authority certificate
    certData string
    PEM encoded certificate for client auth
    certFile string
    Path to a file containing the PEM encoded certificate for client auth
    endpoints string[]
    esClientAuthentication string
    ES Client Authentication field to be used with the JWT token
    headers {[key: string]: string}
    A list of headers to be sent with each request to Elasticsearch.
    insecure boolean
    Disable TLS certificate validation
    keyData string
    PEM encoded private key for client auth
    keyFile string
    Path to a file containing the PEM encoded private key for client auth
    password string
    Password to use for API authentication to Elasticsearch.
    username string
    Username to use for API authentication to Elasticsearch.
    api_key str
    API Key to use for authentication to Elasticsearch
    bearer_token str
    Bearer Token to use for authentication to Elasticsearch
    ca_data str
    PEM-encoded custom Certificate Authority certificate
    ca_file str
    Path to a custom Certificate Authority certificate
    cert_data str
    PEM encoded certificate for client auth
    cert_file str
    Path to a file containing the PEM encoded certificate for client auth
    endpoints Sequence[str]
    es_client_authentication str
    ES Client Authentication field to be used with the JWT token
    headers Mapping[str, str]
    A list of headers to be sent with each request to Elasticsearch.
    insecure bool
    Disable TLS certificate validation
    key_data str
    PEM encoded private key for client auth
    key_file str
    Path to a file containing the PEM encoded private key for client auth
    password str
    Password to use for API authentication to Elasticsearch.
    username str
    Username to use for API authentication to Elasticsearch.
    apiKey String
    API Key to use for authentication to Elasticsearch
    bearerToken String
    Bearer Token to use for authentication to Elasticsearch
    caData String
    PEM-encoded custom Certificate Authority certificate
    caFile String
    Path to a custom Certificate Authority certificate
    certData String
    PEM encoded certificate for client auth
    certFile String
    Path to a file containing the PEM encoded certificate for client auth
    endpoints List<String>
    esClientAuthentication String
    ES Client Authentication field to be used with the JWT token
    headers Map<String>
    A list of headers to be sent with each request to Elasticsearch.
    insecure Boolean
    Disable TLS certificate validation
    keyData String
    PEM encoded private key for client auth
    keyFile String
    Path to a file containing the PEM encoded private key for client auth
    password String
    Password to use for API authentication to Elasticsearch.
    username String
    Username to use for API authentication to Elasticsearch.

    ElasticsearchMlAnomalyDetectionJobModelPlotConfig, ElasticsearchMlAnomalyDetectionJobModelPlotConfigArgs

    AnnotationsEnabled bool
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    Enabled bool
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    Terms string
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.
    AnnotationsEnabled bool
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    Enabled bool
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    Terms string
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.
    annotationsEnabled Boolean
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    enabled Boolean
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    terms String
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.
    annotationsEnabled boolean
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    enabled boolean
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    terms string
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.
    annotations_enabled bool
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    enabled bool
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    terms str
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.
    annotationsEnabled Boolean
    If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
    enabled Boolean
    If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
    terms String
    Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied.

    Package Details

    Repository
    elasticstack elastic/terraform-provider-elasticstack
    License
    Notes
    This Pulumi package is based on the elasticstack Terraform Provider.
    elasticstack logo
    elasticstack 0.13.1 published on Thursday, Dec 11, 2025 by elastic
    Schema (JSON)
    elastic/terraform-provider-elasticstack
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