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Get anomaly detection jobs API

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Get anomaly detection jobs API

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Retrieves configuration information for anomaly detection jobs.

Request

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GET _ml/anomaly_detectors/<job_id>

GET _ml/anomaly_detectors/<job_id>,<job_id>

GET _ml/anomaly_detectors/

GET _ml/anomaly_detectors/_all

Prerequisites

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Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

Description

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This API returns a maximum of 10,000 jobs.

Path parameters

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<job_id>: (Optional, string) Identifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. You can get information for multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can get information for all anomaly detection jobs by using _all, by specifying * as the job identifier, or by omitting the identifier.

Query parameters

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allow_no_match

(Optional, Boolean) Specifies what to do when the request:

Contains wildcard expressions and there are no jobs that match.
Contains the _all string or no identifiers and there are no matches.
Contains wildcard expressions and there are only partial matches.

The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

exclude_generated

(Optional, Boolean) Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster. Default is false.

Response body

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The API returns an array of anomaly detection job resources. For the full list of properties, see create anomaly detection jobs API.

blocked

(object) When present, it explains that a task is executed on the job that blocks it from opening.

Properties of blocked

reason: (string) The reason the job is blocked. Values may be delete, reset, revert. Each value means the corresponding action is being executed.
task_id: (string) The task id of the blocking action. You can use the Task management API to monitor progress.

create_time

(string) The time the job was created. For example, 1491007356077. This property is informational; you cannot change its value.

datafeed_config

(object) The datafeed configured for the current anomaly detection job.

Properties of datafeed_config

authorization

(Optional, object) The security privileges that the datafeed uses to run its queries. If Elastic Stack security features were disabled at the time of the most recent update to the datafeed, this property is omitted.

Properties of authorization

api_key

(object) If an API key was used for the most recent update to the datafeed, its name and identifier are listed in the response.

Properties of api_key

id: (string) The identifier for the API key.
name: (string) The name of the API key.

roles

(array of strings) If a user ID was used for the most recent update to the datafeed, its roles at the time of the update are listed in the response.

service_account

(string) If a service account was used for the most recent update to the datafeed, the account name is listed in the response.

datafeed_id

(Optional, string) A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

aggregations

(Optional, object) If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. For more information, see Aggregating data for faster performance.

chunking_config

(Optional, object) Datafeeds might be required to search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated and is an advanced configuration option.

Properties of chunking_config

mode

(string) There are three available modes:

auto: The chunk size is dynamically calculated. This is the default and recommended value when the datafeed does not use aggregations.
manual: Chunking is applied according to the specified time_span. Use this mode when the datafeed uses aggregations.
off: No chunking is applied.

time_span

(time units) The time span that each search will be querying. This setting is only applicable when the mode is set to manual. For example: 3h.

delayed_data_check_config

(Optional, object) Specifies whether the datafeed checks for missing data and the size of the window. For example: {"enabled": true, "check_window": "1h"}.

The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the query_delay option is set too low and the data is being indexed after the datafeed has passed that moment in time. See Working with delayed data.

This check runs only on real-time datafeeds.

Properties of delayed_data_check_config

check_window: (time units) The window of time that is searched for late data. This window of time ends with the latest finalized bucket. It defaults to null, which causes an appropriate check_window to be calculated when the real-time datafeed runs. In particular, the default check_window span calculation is based on the maximum of 2h or 8 * bucket_span.
enabled: (Boolean) Specifies whether the datafeed periodically checks for delayed data. Defaults to true.

frequency

(Optional, time units) The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. For example: 150s. When frequency is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation.

indices

(Required, array) An array of index names. Wildcards are supported. For example: ["it_ops_metrics", "server*"].

If any indices are in remote clusters then the machine learning nodes need to have the remote_cluster_client role.

indices_options

(Optional, object) Specifies index expansion options that are used during search.

For example:

{
   "expand_wildcards": ["all"],
   "ignore_unavailable": true,
   "allow_no_indices": "false",
   "ignore_throttled": true
}

For more information about these options, see Multi-target syntax.

job_id

(Required, string) Identifier for the anomaly detection job.

max_empty_searches

(Optional,integer) If a real-time datafeed has never seen any data (including during any initial training period) then it will automatically stop itself and close its associated job after this many real-time searches that return no documents. In other words, it will stop after frequency times max_empty_searches of real-time operation. If not set then a datafeed with no end time that sees no data will remain started until it is explicitly stopped. By default this setting is not set.

query

(Optional, 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": {"boost": 1}}.

query_delay

(Optional, time units) The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between 60s and 120s. This randomness improves the query performance when there are multiple jobs running on the same node. For more information, see Handling delayed data.

runtime_mappings

(Optional, object) Specifies runtime fields for the datafeed search.

For example:

{
  "day_of_week": {
    "type": "keyword",
    "script": {
      "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))"
    }
  }
}

script_fields

(Optional, object) Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields. For more information, see Transforming data with script fields and Script fields.

scroll_size

(Optional, unsigned integer) The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The default value is 1000. The maximum value is the value of index.max_result_window which is 10,000 by default.

finished_time

(string) If the job closed or failed, this is the time the job finished, otherwise it is null. This property is informational; you cannot change its value.

job_type

(string) Reserved for future use, currently set to anomaly_detector.

job_version

(string) The machine learning configuration version number at which the job was created.

From Elasticsearch 8.10.0, a new version number is used to track the configuration and state changes in the machine learning plugin. This new version number is decoupled from the product version and will increment independently. The job_version value represents the new version number.

model_snapshot_id: (string) A numerical character string that uniquely identifies the model snapshot. For example, 1575402236000.

Response codes

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404 (Missing resources): If allow_no_match is false, this code indicates that there are no resources that match the request or only partial matches for the request.

Examples

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resp = client.ml.get_jobs(
    job_id="high_sum_total_sales",
)
print(resp)

response = client.ml.get_jobs(
  job_id: 'high_sum_total_sales'
)
puts response

const response = await client.ml.getJobs({
  job_id: "high_sum_total_sales",
});
console.log(response);

GET _ml/anomaly_detectors/high_sum_total_sales

The API returns the following results:

{
  "count": 1,
  "jobs": [
    {
      "job_id" : "high_sum_total_sales",
      "job_type" : "anomaly_detector",
      "job_version" : "8.4.0",
      "create_time" : 1655852735889,
      "finished_time" : 1655852745980,
      "model_snapshot_id" : "1575402237",
      "custom_settings" : {
        "created_by" : "ml-module-sample",
        ...
      },
      "datafeed_config" : {
        "datafeed_id" : "datafeed-high_sum_total_sales",
        "job_id" : "high_sum_total_sales",
        "authorization" : {
          "roles" : [
            "superuser"
          ]
        },
        "query_delay" : "93169ms",
        "chunking_config" : {
          "mode" : "auto"
        },
        "indices_options" : {
          "expand_wildcards" : [
            "open"
          ],
          "ignore_unavailable" : false,
          "allow_no_indices" : true,
          "ignore_throttled" : true
        },
        "query" : {
          "bool" : {
            "filter" : [
              {
                "term" : {
                  "event.dataset" : "sample_ecommerce"
                }
              }
            ]
          }
        },
        "indices" : [
          "kibana_sample_data_ecommerce"
        ],
        "scroll_size" : 1000,
        "delayed_data_check_config" : {
          "enabled" : true
        }
      },
      "groups" : [
        "kibana_sample_data",
        "kibana_sample_ecommerce"
      ],
      "description" : "Find customers spending an unusually high amount in an hour",
      "analysis_config" : {
        "bucket_span" : "1h",
        "detectors" : [
          {
            "detector_description" : "High total sales",
            "function" : "high_sum",
            "field_name" : "taxful_total_price",
            "over_field_name" : "customer_full_name.keyword",
            "detector_index" : 0
          }
        ],
        "influencers" : [
          "customer_full_name.keyword",
          "category.keyword"
        ],
        "model_prune_window": "30d"
      },
      "analysis_limits" : {
        "model_memory_limit" : "13mb",
        "categorization_examples_limit" : 4
      },
      "data_description" : {
        "time_field" : "order_date",
        "time_format" : "epoch_ms"
      },
      "model_plot_config" : {
        "enabled" : true,
        "annotations_enabled" : true
      },
      "model_snapshot_retention_days" : 10,
      "daily_model_snapshot_retention_after_days" : 1,
      "results_index_name" : "shared",
      "allow_lazy_open" : false
    }
  ]
}

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