Get behavioral analytics collections Technical preview; Added in 8.8.0

GET /_application/analytics/{name}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_application/analytics

GET /_application/analytics/{name}

Path parameters

  • name array[string] Required

    A list of analytics collections to limit the returned information

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • * object Additional properties
      Hide * attribute Show * attribute object
      • event_data_stream object Required

        Data stream for the collection.

        Hide event_data_stream attribute Show event_data_stream attribute object
        • name string Required
GET /_application/analytics/{name} 
GET _application/analytics/my*

resp = client.search_application.get_behavioral_analytics(
    name="my*",
)
const response = await client.searchApplication.getBehavioralAnalytics({
  name: "my*",
});
response = client.search_application.get_behavioral_analytics(
  name: "my*"
)
$resp = $client->searchApplication()->getBehavioralAnalytics([
    "name" => "my*",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_application/analytics/my*"
client.searchApplication().getBehavioralAnalytics(g -> g
    .name("my*")
);
Response examples (200)
A successful response from `GET _application/analytics/my*` 
{
  "my_analytics_collection": {
      "event_data_stream": {
          "name": "behavioral_analytics-events-my_analytics_collection"
      }
  },
  "my_analytics_collection2": {
      "event_data_stream": {
          "name": "behavioral_analytics-events-my_analytics_collection2"
      }
  }
}

Get trained models Generally available; Added in 7.7.0

GET /_cat/ml/trained_models/{model_id}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_cat/ml/trained_models

GET /_cat/ml/trained_models/{model_id}

Get configuration and usage information about inference trained models.

IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get trained models statistics API.

Required authorization

  • Cluster privileges: monitor_ml

Path parameters

  • model_id string Required

    A unique identifier for the trained model.

Query parameters

  • allow_no_match boolean

    Specifies what to do when the request: contains wildcard expressions and there are no models that match; contains the _all string or no identifiers and there are no matches; contains wildcard expressions and there are only partial matches. If true, the API returns an empty array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

  • bytes string

    The unit used to display byte values.

    Values are b, kb, mb, gb, tb, or pb.

  • h string | array[string]

    A comma-separated list of column names to display.

    Supported values include:

    • create_time (or ct): The time when the trained model was created.
    • created_by (or c, createdBy): Information on the creator of the trained model.
    • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
    • description (or d): The description of the trained model.
    • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
    • id: Identifier for the trained model.
    • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
    • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
    • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
    • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
    • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
    • license (or l): The license level of the trained model.
    • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
    • version (or v): The Elasticsearch version number in which the trained model was created.
  • s string | array[string]

    A comma-separated list of column names or aliases used to sort the response.

    Supported values include:

    • create_time (or ct): The time when the trained model was created.
    • created_by (or c, createdBy): Information on the creator of the trained model.
    • data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
    • description (or d): The description of the trained model.
    • heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
    • id: Identifier for the trained model.
    • ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
    • ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
    • ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
    • ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
    • ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
    • license (or l): The license level of the trained model.
    • operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
    • version (or v): The Elasticsearch version number in which the trained model was created.
  • from number

    Skips the specified number of transforms.

  • size number

    The maximum number of transforms to display.

  • time string

    Unit used to display time values.

    Values are nanos, micros, ms, s, m, h, or d.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string

      The model identifier.

    • created_by string

      Information about the creator of the model.

    • heap_size number | string

      The estimated heap size to keep the model in memory.

      One of:
      number-1 number string-2 string

      The estimated heap size to keep the model in memory.

    • operations string

      The estimated number of operations to use the model. This number helps to measure the computational complexity of the model.

    • license string

      The license level of the model.

    • create_time string | number

      The time the model was created.

      One of:
      string-1 string UnitMillis number

      The time the model was created.

    • version string

      The version of Elasticsearch when the model was created.

    • description string

      A description of the model.

    • ingest.pipelines string

      The number of pipelines that are referencing the model.

    • ingest.count string

      The total number of documents that are processed by the model.

    • ingest.time string

      The total time spent processing documents with thie model.

    • ingest.current string

      The total number of documents that are currently being handled by the model.

    • ingest.failed string

      The total number of failed ingest attempts with the model.

    • data_frame.id string

      The identifier for the data frame analytics job that created the model. Only displayed if the job is still available.

    • data_frame.create_time string

      The time the data frame analytics job was created.

    • data_frame.source_index string

      The source index used to train in the data frame analysis.

    • data_frame.analysis string

      The analysis used by the data frame to build the model.

    • type string Generally available; Added in 8.0.0
GET /_cat/ml/trained_models/{model_id} 
GET _cat/ml/trained_models?v=true&format=json

resp = client.cat.ml_trained_models(
    v=True,
    format="json",
)
const response = await client.cat.mlTrainedModels({
  v: "true",
  format: "json",
});
response = client.cat.ml_trained_models(
  v: "true",
  format: "json"
)
$resp = $client->cat()->mlTrainedModels([
    "v" => "true",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/trained_models?v=true&format=json"
client.cat().mlTrainedModels();
Response examples (200)
A successful response from `GET _cat/ml/trained_models?v=true&format=json`. 
[
  {
    "id": "ddddd-1580216177138",
    "heap_size": "0b",
    "operations": "196",
    "create_time": "2025-03-25T00:01:38.662Z",
    "type": "pytorch",
    "ingest.pipelines": "0",
    "data_frame.id": "__none__"
  },
  {
    "id": "lang_ident_model_1",
    "heap_size": "1mb",
    "operations": "39629",
    "create_time": "2019-12-05T12:28:34.594Z",
    "type": "lang_ident",
    "ingest.pipelines": "0",
    "data_frame.id": "__none__"
  }
]

Get index template information Generally available; Added in 5.2.0

GET /_cat/templates/{name}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_cat/templates

GET /_cat/templates/{name}

Get information about the index templates in a cluster. You can use index templates to apply index settings and field mappings to new indices at creation. IMPORTANT: cat APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the get index template API.

Required authorization

  • Cluster privileges: monitor

Path parameters

  • name string Required

    The name of the template to return. Accepts wildcard expressions. If omitted, all templates are returned.

Query parameters

  • h string | array[string]

    List of columns to appear in the response. Supports simple wildcards.

  • s string | array[string]

    List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting :asc or :desc as a suffix to the column name.

  • local boolean

    If true, the request computes the list of selected nodes from the local cluster state. If false the list of selected nodes are computed from the cluster state of the master node. In both cases the coordinating node will send requests for further information to each selected node.

  • master_timeout string

    Period to wait for a connection to the master node.

    Values are -1 or 0.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • name string

      The template name.

    • index_patterns string

      The template index patterns.

    • order string

      The template application order or priority number.

    • version string | null

      The template version.

      One of:
      VersionString string string-2 string | null
    • composed_of string

      The component templates that comprise the index template.
GET /_cat/templates/{name} 
GET _cat/templates/my-template-*?v=true&s=name&format=json

resp = client.cat.templates(
    name="my-template-*",
    v=True,
    s="name",
    format="json",
)
const response = await client.cat.templates({
  name: "my-template-*",
  v: "true",
  s: "name",
  format: "json",
});
response = client.cat.templates(
  name: "my-template-*",
  v: "true",
  s: "name",
  format: "json"
)
$resp = $client->cat()->templates([
    "name" => "my-template-*",
    "v" => "true",
    "s" => "name",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/templates/my-template-*?v=true&s=name&format=json"
client.cat().templates();
Response examples (200)
A successful response from `GET _cat/templates/my-template-*?v=true&s=name&format=json`. 
[
  {
    "name": "my-template-0",
    "index_patterns": "[te*]",
    "order": "500",
    "version": null,
    "composed_of": "[]"
  },
  {
    "name": "my-template-1",
    "index_patterns": "[tea*]",
    "order": "501",
    "version": null,
    "composed_of": "[]"
  },
  {
    "name": "my-template-2",
    "index_patterns": "[teak*]",
    "order": "502",
    "version": "7",
    "composed_of": "[]"
  }
]

Ping the cluster Generally available

HEAD /
Api key auth Basic auth Bearer auth

Get information about whether the cluster is running.

Responses

  • 200 application/json
HEAD / 
curl \
 --request HEAD 'http://api.example.com/' \
 --header "Authorization: $API_KEY"

Cluster - Health

Get the cluster health Generally available; Added in 8.7.0

GET /_health_report/{feature}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_health_report

GET /_health_report/{feature}

Get a report with the health status of an Elasticsearch cluster. The report contains a list of indicators that compose Elasticsearch functionality.

Each indicator has a health status of: green, unknown, yellow or red. The indicator will provide an explanation and metadata describing the reason for its current health status.

The cluster’s status is controlled by the worst indicator status.

In the event that an indicator’s status is non-green, a list of impacts may be present in the indicator result which detail the functionalities that are negatively affected by the health issue. Each impact carries with it a severity level, an area of the system that is affected, and a simple description of the impact on the system.

Some health indicators can determine the root cause of a health problem and prescribe a set of steps that can be performed in order to improve the health of the system. The root cause and remediation steps are encapsulated in a diagnosis. A diagnosis contains a cause detailing a root cause analysis, an action containing a brief description of the steps to take to fix the problem, the list of affected resources (if applicable), and a detailed step-by-step troubleshooting guide to fix the diagnosed problem.

NOTE: The health indicators perform root cause analysis of non-green health statuses. This can be computationally expensive when called frequently. When setting up automated polling of the API for health status, set verbose to false to disable the more expensive analysis logic.

Path parameters

  • feature string | array[string] Required

    A feature of the cluster, as returned by the top-level health report API.

Query parameters

  • timeout string

    Explicit operation timeout.

    Values are -1 or 0.

  • verbose boolean

    Opt-in for more information about the health of the system.

  • size number

    Limit the number of affected resources the health report API returns.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • cluster_name string Required
    • indicators object Required
      Hide indicators attributes Show indicators attributes object
      • master_is_stable object

        MASTER_IS_STABLE

        Hide master_is_stable attributes Show master_is_stable attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • shards_availability object

        SHARDS_AVAILABILITY

        Hide shards_availability attributes Show shards_availability attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • disk object

        DISK

        Hide disk attributes Show disk attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • repository_integrity object

        REPOSITORY_INTEGRITY

        Hide repository_integrity attributes Show repository_integrity attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • data_stream_lifecycle object

        DATA_STREAM_LIFECYCLE

        Hide data_stream_lifecycle attributes Show data_stream_lifecycle attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • ilm object

        ILM

        Hide ilm attributes Show ilm attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • slm object

        SLM

        Hide slm attributes Show slm attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
      • shards_capacity object

        SHARDS_CAPACITY

        Hide shards_capacity attributes Show shards_capacity attributes object
        • symptom string Required
        • impacts array[object]
        • diagnosis array[object]
    • status string

      Values are green, yellow, red, unknown, or unavailable.
GET /_health_report/{feature} 
GET _health_report

resp = client.health_report()
const response = await client.healthReport();
response = client.health_report
$resp = $client->healthReport();
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_health_report"
client.healthReport(h -> h);

Update the connector name and description Beta; Added in 8.12.0

PUT /_connector/{connector_id}/_name
Api key auth Basic auth Bearer auth

Path parameters

  • connector_id string Required

    The unique identifier of the connector to be updated

application/json

Body Required

  • name string
  • description string

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • result string Required

      Values are created, updated, deleted, not_found, or noop.
PUT /_connector/{connector_id}/_name 
PUT _connector/my-connector/_name
{
    "name": "Custom connector",
    "description": "This is my customized connector"
}
resp = client.connector.update_name(
    connector_id="my-connector",
    name="Custom connector",
    description="This is my customized connector",
)
const response = await client.connector.updateName({
  connector_id: "my-connector",
  name: "Custom connector",
  description: "This is my customized connector",
});
response = client.connector.update_name(
  connector_id: "my-connector",
  body: {
    "name": "Custom connector",
    "description": "This is my customized connector"
  }
)
$resp = $client->connector()->updateName([
    "connector_id" => "my-connector",
    "body" => [
        "name" => "Custom connector",
        "description" => "This is my customized connector",
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"name":"Custom connector","description":"This is my customized connector"}' "$ELASTICSEARCH_URL/_connector/my-connector/_name"
client.connector().updateName(u -> u
    .connectorId("my-connector")
    .description("This is my customized connector")
    .name("Custom connector")
);
Request example 
{
    "name": "Custom connector",
    "description": "This is my customized connector"
}
Response examples (200) 
{
  "result": "updated"
}

Pause an auto-follow pattern Generally available; Added in 7.5.0

POST /_ccr/auto_follow/{name}/pause
Api key auth Basic auth Bearer auth

Pause a cross-cluster replication auto-follow pattern. When the API returns, the auto-follow pattern is inactive. New indices that are created on the remote cluster and match the auto-follow patterns are ignored.

You can resume auto-following with the resume auto-follow pattern API. When it resumes, the auto-follow pattern is active again and automatically configures follower indices for newly created indices on the remote cluster that match its patterns. Remote indices that were created while the pattern was paused will also be followed, unless they have been deleted or closed in the interim.

Required authorization

  • Cluster privileges: manage_ccr
External documentation

Path parameters

  • name string Required

    The name of the auto-follow pattern to pause.

Query parameters

  • master_timeout string

    The period to wait for a connection to the master node. If the master node is not available before the timeout expires, the request fails and returns an error. It can also be set to -1 to indicate that the request should never timeout.

    Values are -1 or 0.

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • acknowledged boolean Required

      For a successful response, this value is always true. On failure, an exception is returned instead.
POST /_ccr/auto_follow/{name}/pause 
POST /_ccr/auto_follow/my_auto_follow_pattern/pause

resp = client.ccr.pause_auto_follow_pattern(
    name="my_auto_follow_pattern",
)
const response = await client.ccr.pauseAutoFollowPattern({
  name: "my_auto_follow_pattern",
});
response = client.ccr.pause_auto_follow_pattern(
  name: "my_auto_follow_pattern"
)
$resp = $client->ccr()->pauseAutoFollowPattern([
    "name" => "my_auto_follow_pattern",
]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ccr/auto_follow/my_auto_follow_pattern/pause"
client.ccr().pauseAutoFollowPattern(p -> p
    .name("my_auto_follow_pattern")
);
Response examples (200)
A successful response from `POST /_ccr/auto_follow/my_auto_follow_pattern/pause`, which pauses an auto-follow pattern. 
{
  "acknowledged" : true
}

Get mapping definitions Generally available

GET /{index}/_mapping/field/{fields}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_mapping/field/{fields}

GET /{index}/_mapping/field/{fields}

Retrieves mapping definitions for one or more fields. For data streams, the API retrieves field mappings for the stream’s backing indices.

This API is useful if you don't need a complete mapping or if an index mapping contains a large number of fields.

Required authorization

  • Index privileges: view_index_metadata

Path parameters

  • index string | array[string] Required

    Comma-separated list of data streams, indices, and aliases used to limit the request. Supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.

  • fields string | array[string] Required

    Comma-separated list or wildcard expression of fields used to limit returned information. Supports wildcards (*).

Query parameters

  • allow_no_indices boolean

    If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.

  • expand_wildcards string | array[string]

    Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

    Supported values include:

    • all: Match any data stream or index, including hidden ones.
    • open: Match open, non-hidden indices. Also matches any non-hidden data stream.
    • closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
    • hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
    • none: Wildcard expressions are not accepted.

    Values are all, open, closed, hidden, or none.

  • ignore_unavailable boolean

    If false, the request returns an error if it targets a missing or closed index.

  • include_defaults boolean

    If true, return all default settings in the response.

  • local boolean

    If true, the request retrieves information from the local node only.

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • * object Additional properties
      Hide * attribute Show * attribute object
      • mappings object Required
        Hide mappings attribute Show mappings attribute object
        • * object Additional properties
          Hide * attributes Show * attributes object
          • full_name string Required
          • mapping object Required
GET /{index}/_mapping/field/{fields} 
GET publications/_mapping/field/title

resp = client.indices.get_field_mapping(
    index="publications",
    fields="title",
)
const response = await client.indices.getFieldMapping({
  index: "publications",
  fields: "title",
});
response = client.indices.get_field_mapping(
  index: "publications",
  fields: "title"
)
$resp = $client->indices()->getFieldMapping([
    "index" => "publications",
    "fields" => "title",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/publications/_mapping/field/title"
client.indices().getFieldMapping(g -> g
    .fields("title")
    .index("publications")
);
Response examples (200)
A sucessful response from `GET publications/_mapping/field/title`, which returns the mapping of a field called `title`. 
{
   "publications": {
      "mappings": {
          "title": {
             "full_name": "title",
             "mapping": {
                "title": {
                   "type": "text"
                }
             }
          }
       }
   }
}
A successful response from `GET publications/_mapping/field/author.id,abstract,name`. The get field mapping API also supports wildcard notation. 
{
   "publications": {
      "mappings": {
        "author.id": {
           "full_name": "author.id",
           "mapping": {
              "id": {
                 "type": "text"
              }
           }
        },
        "abstract": {
           "full_name": "abstract",
           "mapping": {
              "abstract": {
                 "type": "text"
              }
           }
        }
     }
   }
}
A successful response from `GET publications/_mapping/field/a*`. 
{
   "publications": {
      "mappings": {
         "author.name": {
            "full_name": "author.name",
            "mapping": {
               "name": {
                 "type": "text"
               }
            }
         },
         "abstract": {
            "full_name": "abstract",
            "mapping": {
               "abstract": {
                  "type": "text"
               }
            }
         },
         "author.id": {
            "full_name": "author.id",
            "mapping": {
               "id": {
                  "type": "text"
               }
            }
         }
      }
   }
}

Create a Mistral inference endpoint Generally available; Added in 8.15.0

PUT /_inference/{task_type}/{mistral_inference_id}
Api key auth Basic auth Bearer auth

Create an inference endpoint to perform an inference task with the mistral service.

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

    The type of the inference task that the model will perform.

    Values are text_embedding, completion, or chat_completion.

  • mistral_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

  • timeout string

    Specifies the amount of time to wait for the inference endpoint to be created.

    Values are -1 or 0.

application/json

Body

  • chunking_settings object

    The chunking configuration object.

    Hide chunking_settings attributes Show chunking_settings attributes object
    • max_chunk_size number

      The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

      Default value is 250.

    • overlap number

      The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

      Default value is 100.

    • sentence_overlap number

      The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

      Default value is 1.

    • strategy string

      The chunking strategy: sentence or word.

      Default value is sentence.

  • service string Required

    The type of service supported for the specified task type. In this case, mistral.

    Value is mistral.

  • service_settings object Required

    Settings used to install the inference model. These settings are specific to the mistral service.

    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key of your Mistral account. You can find your Mistral API keys or you can create a new one on the API Keys page.

      IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.

      External documentation
    • max_input_tokens number

      The maximum number of tokens per input before chunking occurs.

    • model string Required

      The name of the model to use for the inference task. Refer to the Mistral models documentation for the list of available models.

      External documentation
    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from the Mistral API. By default, the mistral service sets the number of requests allowed per minute to 240.

      Hide rate_limit attribute Show rate_limit attribute object
      • requests_per_minute number

        The number of requests allowed per minute.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings object

      Chunking configuration object

      Hide chunking_settings attributes Show chunking_settings attributes object
      • max_chunk_size number

        The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

        Default value is 250.

      • overlap number

        The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

        Default value is 100.

      • sentence_overlap number

        The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

        Default value is 1.

      • strategy string

        The chunking strategy: sentence or word.

        Default value is sentence.

    • service string Required

      The service type

    • service_settings object Required

      Settings specific to the service

    • task_settings object

      Task settings specific to the service and task type

    • inference_id string Required

      The inference Id

    • task_type string Required

      The task type

      Value is text_embedding.
PUT /_inference/{task_type}/{mistral_inference_id} 
PUT _inference/text_embedding/mistral-embeddings-test
{
  "service": "mistral",
  "service_settings": {
    "api_key": "Mistral-API-Key",
    "model": "mistral-embed" 
  }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="mistral-embeddings-test",
    inference_config={
        "service": "mistral",
        "service_settings": {
            "api_key": "Mistral-API-Key",
            "model": "mistral-embed"
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "mistral-embeddings-test",
  inference_config: {
    service: "mistral",
    service_settings: {
      api_key: "Mistral-API-Key",
      model: "mistral-embed",
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "mistral-embeddings-test",
  body: {
    "service": "mistral",
    "service_settings": {
      "api_key": "Mistral-API-Key",
      "model": "mistral-embed"
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "mistral-embeddings-test",
    "body" => [
        "service" => "mistral",
        "service_settings" => [
            "api_key" => "Mistral-API-Key",
            "model" => "mistral-embed",
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"mistral","service_settings":{"api_key":"Mistral-API-Key","model":"mistral-embed"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/mistral-embeddings-test"
client.inference().put(p -> p
    .inferenceId("mistral-embeddings-test")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("mistral")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Mistral-API-Key\",\"model\":\"mistral-embed\"}"))
    )
);
Request example
Run `PUT _inference/text_embedding/mistral-embeddings-test` to create a Mistral inference endpoint that performs a text embedding task. 
{
  "service": "mistral",
  "service_settings": {
    "api_key": "Mistral-API-Key",
    "model": "mistral-embed" 
  }
}

Create a calendar Generally available; Added in 6.2.0

PUT /_ml/calendars/{calendar_id}
Api key auth Basic auth Bearer auth

Required authorization

  • Cluster privileges: manage_ml

Path parameters

  • calendar_id string Required

    A string that uniquely identifies a calendar.

application/json

Body

  • job_ids array[string]

    An array of anomaly detection job identifiers.

  • description string

    A description of the calendar.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • calendar_id string Required

      A string that uniquely identifies a calendar.

    • description string

      A description of the calendar.

    • job_ids string | array[string]

      A list of anomaly detection job identifiers or group names.

      One of:
      Id string array-2 array[string]

      A list of anomaly detection job identifiers or group names.
PUT /_ml/calendars/{calendar_id} 
PUT _ml/calendars/planned-outages

resp = client.ml.put_calendar(
    calendar_id="planned-outages",
)
const response = await client.ml.putCalendar({
  calendar_id: "planned-outages",
});
response = client.ml.put_calendar(
  calendar_id: "planned-outages"
)
$resp = $client->ml()->putCalendar([
    "calendar_id" => "planned-outages",
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages"
client.ml().putCalendar(p -> p
    .calendarId("planned-outages")
);

Delete anomaly jobs from a calendar Generally available; Added in 6.2.0

DELETE /_ml/calendars/{calendar_id}/jobs/{job_id}
Api key auth Basic auth Bearer auth

Required authorization

  • Cluster privileges: manage_ml

Path parameters

  • calendar_id string Required

    A string that uniquely identifies a calendar.

  • job_id string | array[string] Required

    An identifier for the anomaly detection jobs. It can be a job identifier, a group name, or a comma-separated list of jobs or groups.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • calendar_id string Required

      A string that uniquely identifies a calendar.

    • description string

      A description of the calendar.

    • job_ids string | array[string]

      A list of anomaly detection job identifiers or group names.

      One of:
      Id string array-2 array[string]

      A list of anomaly detection job identifiers or group names.
DELETE /_ml/calendars/{calendar_id}/jobs/{job_id} 
DELETE _ml/calendars/planned-outages/jobs/total-requests

resp = client.ml.delete_calendar_job(
    calendar_id="planned-outages",
    job_id="total-requests",
)
const response = await client.ml.deleteCalendarJob({
  calendar_id: "planned-outages",
  job_id: "total-requests",
});
response = client.ml.delete_calendar_job(
  calendar_id: "planned-outages",
  job_id: "total-requests"
)
$resp = $client->ml()->deleteCalendarJob([
    "calendar_id" => "planned-outages",
    "job_id" => "total-requests",
]);
curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages/jobs/total-requests"
client.ml().deleteCalendarJob(d -> d
    .calendarId("planned-outages")
    .jobId("total-requests")
);
Response examples (200)
A successful response when deleting an anomaly detection job from a calendar. 
{
  "calendar_id": "planned-outages",
  "job_ids": []
}

Estimate job model memory usage Generally available; Added in 7.7.0

POST /_ml/anomaly_detectors/_estimate_model_memory
Api key auth Basic auth Bearer auth

Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references.

Required authorization

  • Cluster privileges: manage_ml
application/json

Body Required

  • analysis_config object

    For a list of the properties that you can specify in the analysis_config component of the body of this API.

    Hide analysis_config attributes Show analysis_config attributes object
    • bucket_span string

      The size of the interval that the analysis is aggregated into, typically between 5m and 1h. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation.

    • categorization_analyzer string | object

      If categorization_field_name is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as categorization_filters. The categorization analyzer specifies how the categorization_field is interpreted by the categorization process. The categorization_analyzer field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin.

      One of:
      string-1 string CategorizationAnalyzerDefinition object

      If categorization_field_name is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time as categorization_filters. The categorization analyzer specifies how the categorization_field is interpreted by the categorization process. The categorization_analyzer field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin.

    • categorization_field_name string

      If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting by_field_name, over_field_name, or partition_field_name to the keyword mlcategory.

    • categorization_filters array[string]

      If categorization_field_name is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time as categorization_analyzer. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the categorization_analyzer property instead and include the filters as pattern_replace character filters. The effect is exactly the same.

    • detectors array[object] Required

      Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned.

      Hide detectors attributes Show detectors attributes object
      • by_field_name 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.

      • custom_rules array[object]

        Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules.

        Hide custom_rules attributes Show custom_rules attributes object
        • actions array[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.

          Supported values include:

          • skip_result: The result will not be created. Unless you also specify skip_model_update, the model will be updated as usual with the corresponding series value.
          • skip_model_update: The value for that series will not be used to update the model. Unless you also specify skip_result, the results will be created as usual. This action is suitable when certain values are expected to be consistently anomalous and they affect the model in a way that negatively impacts the rest of the results.

          Values are skip_result or skip_model_update. Default value is ["skip_result"].

        • conditions array[object]

          An array of numeric conditions when the rule applies. A rule must either have a non-empty scope or at least one condition. Multiple conditions are combined together with a logical AND.

        • scope object

          A scope of series where the rule applies. A rule must either have a non-empty scope or at least one condition. By default, the scope includes all series. Scoping is allowed for any of the fields that are also specified in by_field_name, over_field_name, or partition_field_name.

      • detector_description string

        A description of the detector.

      • detector_index number

        A unique identifier for the detector. This identifier is based on the order of the detectors in the analysis_config, starting at zero. If you specify a value for this property, it is ignored.

      • exclude_frequent string

        If set, frequent entities are excluded from influencing the anomaly results. Entities can be considered frequent over time or frequent in a population. If you are working with both over and by fields, you can set exclude_frequent to all for both fields, or to by or over for those specific fields.

        Values are all, none, by, or over.

      • field_name string

        The field that the detector uses in the function. If you use an event rate function such as count or rare, do not specify this field. The field_name cannot contain double quotes or backslashes.

      • function string

        The analysis function that is used. For example, count, rare, mean, min, max, or sum.

      • over_field_name 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.

      • partition_field_name string

        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 boolean

        Defines whether a new series is used as the null series when there is no value for the by or partition fields.

        Default value is false.

    • influencers array[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. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity.

    • 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. NOTE: Latency is applicable only when you send data by using the post data API.

    • model_prune_window string

      Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the bucket_span. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of 30d or 20 times bucket_span.

    • multivariate_by_fields 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. If set to true, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use the multivariate_by_fields property, you must also specify by_field_name in your detector.

    • per_partition_categorization object

      Settings related to how categorization interacts with partition fields.

      Hide per_partition_categorization attributes Show per_partition_categorization attributes object
      • enabled boolean

        To enable this setting, you must also set the partition_field_name property to the same value in every detector that uses the keyword mlcategory. Otherwise, job creation fails.

      • stop_on_warn boolean

        This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly.

    • summary_count_field_name string

      If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same summary_count_field_name applies to all detectors in the job. NOTE: The summary_count_field_name property cannot be used with the metric function.

  • max_bucket_cardinality object

    Estimates of the highest cardinality in a single bucket that is observed for influencer fields over the time period that the job analyzes data. To produce a good answer, values must be provided for all influencer fields. Providing values for fields that are not listed as influencers has no effect on the estimation.

    Hide max_bucket_cardinality attribute Show max_bucket_cardinality attribute object
    • * number Additional properties
  • overall_cardinality object

    Estimates of the cardinality that is observed for fields over the whole time period that the job analyzes data. To produce a good answer, values must be provided for fields referenced in the by_field_name, over_field_name and partition_field_name of any detectors. Providing values for other fields has no effect on the estimation. It can be omitted from the request if no detectors have a by_field_name, over_field_name or partition_field_name.

    Hide overall_cardinality attribute Show overall_cardinality attribute object
    • * number Additional properties

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • model_memory_estimate string Required
POST /_ml/anomaly_detectors/_estimate_model_memory 
POST _ml/anomaly_detectors/_estimate_model_memory
{
  "analysis_config": {
    "bucket_span": "5m",
    "detectors": [
      {
        "function": "sum",
        "field_name": "bytes",
        "by_field_name": "status",
        "partition_field_name": "app"
      }
    ],
    "influencers": [
      "source_ip",
      "dest_ip"
    ]
  },
  "overall_cardinality": {
    "status": 10,
    "app": 50
  },
  "max_bucket_cardinality": {
    "source_ip": 300,
    "dest_ip": 30
  }
}
resp = client.ml.estimate_model_memory(
    analysis_config={
        "bucket_span": "5m",
        "detectors": [
            {
                "function": "sum",
                "field_name": "bytes",
                "by_field_name": "status",
                "partition_field_name": "app"
            }
        ],
        "influencers": [
            "source_ip",
            "dest_ip"
        ]
    },
    overall_cardinality={
        "status": 10,
        "app": 50
    },
    max_bucket_cardinality={
        "source_ip": 300,
        "dest_ip": 30
    },
)
const response = await client.ml.estimateModelMemory({
  analysis_config: {
    bucket_span: "5m",
    detectors: [
      {
        function: "sum",
        field_name: "bytes",
        by_field_name: "status",
        partition_field_name: "app",
      },
    ],
    influencers: ["source_ip", "dest_ip"],
  },
  overall_cardinality: {
    status: 10,
    app: 50,
  },
  max_bucket_cardinality: {
    source_ip: 300,
    dest_ip: 30,
  },
});
response = client.ml.estimate_model_memory(
  body: {
    "analysis_config": {
      "bucket_span": "5m",
      "detectors": [
        {
          "function": "sum",
          "field_name": "bytes",
          "by_field_name": "status",
          "partition_field_name": "app"
        }
      ],
      "influencers": [
        "source_ip",
        "dest_ip"
      ]
    },
    "overall_cardinality": {
      "status": 10,
      "app": 50
    },
    "max_bucket_cardinality": {
      "source_ip": 300,
      "dest_ip": 30
    }
  }
)
$resp = $client->ml()->estimateModelMemory([
    "body" => [
        "analysis_config" => [
            "bucket_span" => "5m",
            "detectors" => array(
                [
                    "function" => "sum",
                    "field_name" => "bytes",
                    "by_field_name" => "status",
                    "partition_field_name" => "app",
                ],
            ),
            "influencers" => array(
                "source_ip",
                "dest_ip",
            ),
        ],
        "overall_cardinality" => [
            "status" => 10,
            "app" => 50,
        ],
        "max_bucket_cardinality" => [
            "source_ip" => 300,
            "dest_ip" => 30,
        ],
    ],
]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"analysis_config":{"bucket_span":"5m","detectors":[{"function":"sum","field_name":"bytes","by_field_name":"status","partition_field_name":"app"}],"influencers":["source_ip","dest_ip"]},"overall_cardinality":{"status":10,"app":50},"max_bucket_cardinality":{"source_ip":300,"dest_ip":30}}' "$ELASTICSEARCH_URL/_ml/anomaly_detectors/_estimate_model_memory"
client.ml().estimateModelMemory(e -> e
    .analysisConfig(a -> a
        .bucketSpan(b -> b
            .time("5m")
        )
        .detectors(d -> d
            .byFieldName("status")
            .fieldName("bytes")
            .function("sum")
            .partitionFieldName("app")
        )
        .influencers(List.of("source_ip","dest_ip"))
    )
    .maxBucketCardinality(Map.of("dest_ip", 30L,"source_ip", 300L))
    .overallCardinality(Map.of("app", 50L,"status", 10L))
);
Request example
Run `POST _ml/anomaly_detectors/_estimate_model_memory` to estimate the model memory limit based on the analysis configuration details provided in the request body. 
{
  "analysis_config": {
    "bucket_span": "5m",
    "detectors": [
      {
        "function": "sum",
        "field_name": "bytes",
        "by_field_name": "status",
        "partition_field_name": "app"
      }
    ],
    "influencers": [
      "source_ip",
      "dest_ip"
    ]
  },
  "overall_cardinality": {
    "status": 10,
    "app": 50
  },
  "max_bucket_cardinality": {
    "source_ip": 300,
    "dest_ip": 30
  }
}
Response examples (200)
A successful response from `POST _ml/anomaly_detectors/_estimate_model_memory`. 
{
  "model_memory_estimate": "21mb"
}

Preview features used by data frame analytics Generally available; Added in 7.13.0

POST /_ml/data_frame/analytics/{id}/_preview
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_ml/data_frame/analytics/_preview

POST /_ml/data_frame/analytics/_preview
GET /_ml/data_frame/analytics/{id}/_preview
POST /_ml/data_frame/analytics/{id}/_preview

Preview the extracted features used by a data frame analytics config.

Required authorization

  • Cluster privileges: monitor_ml

Path parameters

  • id string Required

    Identifier for the data frame analytics job.

application/json

Body

  • config object

    A data frame analytics config as described in create data frame analytics jobs. Note that id and dest don’t need to be provided in the context of this API.

    Hide config attributes Show config attributes object
    • source object Required
      Hide source attributes Show source attributes object
      • index string | array[string] Required

        Index or indices on which to perform the analysis. It can be a single index or index pattern as well as an array of indices or patterns. NOTE: If your source indices contain documents with the same IDs, only the document that is indexed last appears in the destination index.

      • runtime_mappings object

        Definitions of runtime fields that will become part of the mapping of the destination index.

        Hide runtime_mappings attribute Show runtime_mappings attribute object
        • * object Additional properties
      • _source object

        Specify includes and/or `excludes patterns to select which fields will be present in the destination. Fields that are excluded cannot be included in the analysis.

        Hide _source attributes Show _source attributes object
        • includes array[string] Required

          An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.

        • excludes array[string] Required

          An array of strings that defines the fields that will be included in the analysis.

      • query object

        The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value: {"match_all": {}}.

        Query DSL
    • analysis object Required
      Hide analysis attributes Show analysis attributes object
      • outlier_detection object

        The configuration information necessary to perform outlier detection. NOTE: Advanced parameters are for fine-tuning classification analysis. They are set automatically by hyperparameter optimization to give the minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters.

        Hide outlier_detection attributes Show outlier_detection attributes object
        • compute_feature_influence boolean

          Specifies whether the feature influence calculation is enabled.

          Default value is true.

        • feature_influence_threshold number

          The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.

          Default value is 0.1.

        • method string

          The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.

          Default value is ensemble.

        • n_neighbors number

          Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.

        • outlier_fraction number

          The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.

        • standardization_enabled boolean

          If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).

          Default value is true.

    • model_memory_limit string
    • max_num_threads number
    • analyzed_fields object
      Hide analyzed_fields attributes Show analyzed_fields attributes object
      • includes array[string] Required

        An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.

      • excludes array[string] Required

        An array of strings that defines the fields that will be included in the analysis.

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • feature_values array[object] Required

      An array of objects that contain feature name and value pairs. The features have been processed and indicate what will be sent to the model for training.

      Hide feature_values attribute Show feature_values attribute object
      • * string Additional properties
POST /_ml/data_frame/analytics/{id}/_preview 
POST _ml/data_frame/analytics/_preview
{
  "config": {
    "source": {
      "index": "houses_sold_last_10_yrs"
    },
    "analysis": {
      "regression": {
        "dependent_variable": "price"
      }
    }
  }
}
resp = client.ml.preview_data_frame_analytics(
    config={
        "source": {
            "index": "houses_sold_last_10_yrs"
        },
        "analysis": {
            "regression": {
                "dependent_variable": "price"
            }
        }
    },
)
const response = await client.ml.previewDataFrameAnalytics({
  config: {
    source: {
      index: "houses_sold_last_10_yrs",
    },
    analysis: {
      regression: {
        dependent_variable: "price",
      },
    },
  },
});
response = client.ml.preview_data_frame_analytics(
  body: {
    "config": {
      "source": {
        "index": "houses_sold_last_10_yrs"
      },
      "analysis": {
        "regression": {
          "dependent_variable": "price"
        }
      }
    }
  }
)
$resp = $client->ml()->previewDataFrameAnalytics([
    "body" => [
        "config" => [
            "source" => [
                "index" => "houses_sold_last_10_yrs",
            ],
            "analysis" => [
                "regression" => [
                    "dependent_variable" => "price",
                ],
            ],
        ],
    ],
]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"config":{"source":{"index":"houses_sold_last_10_yrs"},"analysis":{"regression":{"dependent_variable":"price"}}}}' "$ELASTICSEARCH_URL/_ml/data_frame/analytics/_preview"
client.ml().previewDataFrameAnalytics(p -> p
    .config(c -> c
        .source(s -> s
            .index("houses_sold_last_10_yrs")
        )
        .analysis(a -> a
            .regression(r -> r
                .dependentVariable("price")
            )
        )
    )
);
Request example
An example body for a `POST _ml/data_frame/analytics/_preview` request. 
{
  "config": {
    "source": {
      "index": "houses_sold_last_10_yrs"
    },
    "analysis": {
      "regression": {
        "dependent_variable": "price"
      }
    }
  }
}

Get the async search status Generally available; Added in 7.11.0

GET /_async_search/status/{id}
Api key auth Basic auth Bearer auth

Get the status of a previously submitted async search request given its identifier, without retrieving search results. If the Elasticsearch security features are enabled, the access to the status of a specific async search is restricted to:

  • The user or API key that submitted the original async search request.
  • Users that have the monitor cluster privilege or greater privileges.

Required authorization

  • Cluster privileges: monitor

Path parameters

  • id string Required

    A unique identifier for the async search.

Query parameters

  • keep_alive string

    The length of time that the async search needs to be available. Ongoing async searches and any saved search results are deleted after this period.

    Values are -1 or 0.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string
    • is_partial boolean Required

      When the query is no longer running, this property indicates whether the search failed or was successfully completed on all shards. While the query is running, is_partial is always set to true.

    • is_running boolean Required

      Indicates whether the search is still running or has completed.


      If the search failed after some shards returned their results or the node that is coordinating the async search dies, results may be partial even though is_running is false.

    • expiration_time string | number

      Indicates when the async search will expire.

      One of:
      string-1 string UnitMillis number

      Indicates when the async search will expire.

    • Time unit for milliseconds

    • start_time string | number

      One of:
      string-1 string UnitMillis number

    • Time unit for milliseconds

    • completion_time string | number

      Indicates when the async search completed. It is present only when the search has completed.

      One of:
      string-1 string UnitMillis number

      Indicates when the async search completed. It is present only when the search has completed.

    • Time unit for milliseconds

    • _shards object Required

      The number of shards that have run the query so far.

      Hide _shards attributes Show _shards attributes object
      • failed number Required

        The number of shards the operation or search attempted to run on but failed.

      • successful number Required

        The number of shards the operation or search succeeded on.

      • total number Required

        The number of shards the operation or search will run on overall.

      • failures array[object]
        Hide failures attributes Show failures attributes object
        • index
        • node string
        • reason
        • shard number
        • status string
        • primary boolean
      • skipped number
    • _clusters object

      Metadata about clusters involved in the cross-cluster search. It is not shown for local-only searches.

      Hide _clusters attributes Show _clusters attributes object
      • skipped number Required
      • successful number Required
      • total number Required
      • running number Required
      • partial number Required
      • failed number Required
      • details object
        Hide details attribute Show details attribute object
        • * object Additional properties
          Hide * attributes Show * attributes object
          • indices string Required
          • timed_out boolean Required
          • failures array[object]
    • completion_status number

      If the async search completed, this field shows the status code of the search. For example, 200 indicates that the async search was successfully completed. 503 indicates that the async search was completed with an error.
GET /_async_search/status/{id} 
GET /_async_search/status/FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=

resp = client.async_search.status(
    id="FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
)
const response = await client.asyncSearch.status({
  id: "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
});
response = client.async_search.status(
  id: "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc="
)
$resp = $client->asyncSearch()->status([
    "id" => "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_async_search/status/FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc="
client.asyncSearch().status(s -> s
    .id("FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=")
);
Response examples (200)
A succesful response from `GET /_async_search/status/FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=`, which retrieves the status of a previously submitted async search without the results. 
{
  "id" : "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
  "is_running" : true,
  "is_partial" : true,
  "start_time_in_millis" : 1583945890986,
  "expiration_time_in_millis" : 1584377890986,
  "_shards" : {
      "total" : 562,
      "successful" : 188, 
      "skipped" : 0,
      "failed" : 0
  }
}
A succesful response from `GET /_async_search/status/FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=` for an async search that has completed. The status response has an additional `completion_status` field that shows the status code of the completed async search. 
{
  "id" : "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
  "is_running" : false,
  "is_partial" : false,
  "start_time_in_millis" : 1583945890986,
  "expiration_time_in_millis" : 1584377890986,
  "_shards" : {
      "total" : 562,
      "successful" : 562,
      "skipped" : 0,
      "failed" : 0
  },
"completion_status" : 200 
}
A response from `GET /_async_search/status/FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=` for an async search that has completed with an error. The status response has an additional `completion_status` field that shows the status code of the completed async search. 
{
  "id" : "FmRldE8zREVEUzA2ZVpUeGs2ejJFUFEaMkZ5QTVrSTZSaVN3WlNFVmtlWHJsdzoxMDc=",
  "is_running" : false,
  "is_partial" : true,
  "start_time_in_millis" : 1583945890986,
  "expiration_time_in_millis" : 1584377890986,
  "_shards" : {
      "total" : 562,
      "successful" : 450,
      "skipped" : 0,
      "failed" : 112
  },
"completion_status" : 503 
}