elasticsearch/docs/reference/ml/anomaly-detection/apis/get-job.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-get-job]]
= Get {anomaly-jobs} API
++++
<titleabbrev>Get jobs</titleabbrev>
++++

Retrieves configuration information for {anomaly-jobs}.

[[ml-get-job-request]]
== {api-request-title}

`GET _ml/anomaly_detectors/<job_id>` +

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

`GET _ml/anomaly_detectors/` +

`GET _ml/anomaly_detectors/_all`

[[ml-get-job-prereqs]]
== {api-prereq-title}

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

[[ml-get-job-desc]]
== {api-description-title}

You can get information for multiple {anomaly-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-jobs} by using `_all`, by specifying
`*` as the `<job_id>`, or by omitting the `<job_id>`.

IMPORTANT: This API returns a maximum of 10,000 jobs.

[[ml-get-job-path-parms]]
== {api-path-parms-title}

`<job_id>`::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection-default]

[[ml-get-job-query-parms]]
== {api-query-parms-title}

`allow_no_match`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-jobs]

`exclude_generated`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=exclude-generated]

[[ml-get-job-results]]
== {api-response-body-title}

The API returns an array of {anomaly-job} resources. For the full list of
properties, see <<ml-put-job-request-body,create {anomaly-jobs} API>>.

//Begin blocked
`blocked`::
(object) When present, it explains that a task is executed on the job
that blocks it from opening.
+
.Properties of `blocked`
[%collapsible%open]
====
`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 <<tasks>> API to
monitor progress.
====
//End blocked

`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 {dfeed} configured for the current {anomaly-job}.
+
.Properties of `datafeed_config`
[%collapsible%open]
====
`datafeed_id`:::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=datafeed-id]

`aggregations`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=aggregations]

`chunking_config`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=chunking-config]
+
.Properties of `chunking_config`
[%collapsible%open]
=====
`mode`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=mode]

`time_span`:::
(<<time-units,time units>>)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=time-span]
=====

`delayed_data_check_config`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=delayed-data-check-config]
+
.Properties of `delayed_data_check_config`
[%collapsible%open]
=====
`check_window`::
(<<time-units,time units>>)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=delayed-data-check-config-check-window]

`enabled`::
(Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=delayed-data-check-config-enabled]
=====

`frequency`:::
(Optional, <<time-units, time units>>)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=frequency]

`indices`:::
(Required, array)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=indices]

`indices_options`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=indices-options]

`job_id`:::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`max_empty_searches`:::
(Optional,integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=max-empty-searches]

`query`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=query]

`query_delay`:::
(Optional, <<time-units, time units>>)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=query-delay]

`runtime_mappings`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=runtime-mappings]

`script_fields`:::
(Optional, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=script-fields]

`scroll_size`:::
(Optional, unsigned integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=scroll-size]
====

`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 version of {es} that existed on the node when the job was created.

`model_snapshot_id`::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-snapshot-id]

[[ml-get-job-response-codes]]
== {api-response-codes-title}

`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.

[[ml-get-job-example]]
== {api-examples-title}

[source,console]
--------------------------------------------------
GET _ml/anomaly_detectors/high_sum_total_sales
--------------------------------------------------
// TEST[skip:Kibana sample data]

The API returns the following results:

[source,js]
----
{
  "count": 1,
  "jobs": [
    {
      "job_id" : "high_sum_total_sales",
      "job_type" : "anomaly_detector",
      "job_version" : "8.0.0",
      "groups" : [
        "kibana_sample_data",
        "kibana_sample_ecommerce"
      ],
      "description" : "Find customers spending an unusually high amount in an hour",
      "create_time" : 1575402224732,
      "finished_time" : 1575402238311,
      "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"
        ]
      },
      "analysis_limits" : {
        "model_memory_limit" : "10mb",
        "categorization_examples_limit" : 4
      },
      "data_description" : {
        "time_field" : "order_date",
        "time_format" : "epoch_ms"
      },
      "model_plot_config" : {
        "enabled" : true
      },
      "model_snapshot_retention_days" : 10,
      "daily_model_snapshot_retention_after_days" : 1,
      "custom_settings" : {
        "created_by" : "ml-module-sample",
        ...
      },
      "model_snapshot_id" : "1575402237",
      "results_index_name" : "shared",
      "allow_lazy_open" : false
    }
  ]
}
----