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984323783e
This change adds: - A new option, allow_lazy_open, to anomaly detection jobs - A new option, allow_lazy_start, to data frame analytics jobs Both work in the same way: they allow a job to be opened/started even if no ML node exists that can accommodate the job immediately. In this situation the job waits in the opening/starting state until ML node capacity is available. (The starting state for data frame analytics jobs is new in this change.) Additionally, the ML nightly maintenance tasks now creates audit warnings for ML jobs that are unassigned. This means that jobs that cannot be assigned to an ML node for a very long time will show a yellow warning triangle in the UI. A final change is that it is now possible to close a job that is not assigned to a node without using force. This is because previously jobs that were open but not assigned to a node were an aberration, whereas after this change they'll be relatively common.
158 lines
4.6 KiB
Plaintext
158 lines
4.6 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[ml-put-job]]
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=== Create {anomaly-jobs} API
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++++
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<titleabbrev>Create jobs</titleabbrev>
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++++
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Instantiates an {anomaly-job}.
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[[ml-put-job-request]]
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==== {api-request-title}
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`PUT _ml/anomaly_detectors/<job_id>`
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[[ml-put-job-prereqs]]
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==== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `manage_ml` or
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`manage` cluster privileges to use this API. See
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<<security-privileges>>.
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[[ml-put-job-desc]]
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==== {api-description-title}
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IMPORTANT: You must use {kib} or this API to create an {anomaly-job}. Do not put
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a job directly to the `.ml-config` index using the {es} index API. If {es}
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{security-features} are enabled, do not give users `write` privileges on the
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`.ml-config` index.
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[[ml-put-job-path-parms]]
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==== {api-path-parms-title}
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`<job_id>`::
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(Required, string) Identifier for the job. This identifier can contain
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lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It
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must start and end with alphanumeric characters.
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[[ml-put-job-request-body]]
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==== {api-request-body-title}
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`analysis_config`::
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(Required, object) The analysis configuration, which specifies how to analyze
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the data. See <<ml-analysisconfig, analysis configuration objects>>.
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`analysis_limits`::
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(Optional, object) Specifies runtime limits for the job. See
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<<ml-apilimits,analysis limits>>.
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`background_persist_interval`::
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(Optional, <<time-units, time units>>) Advanced configuration option. The time
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between each periodic persistence of the model. See <<ml-job-resource>>.
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`custom_settings`::
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(Optional, object) Advanced configuration option. Contains custom meta data
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about the job. See <<ml-job-resource>>.
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`data_description`::
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(Required, object) Describes the format of the input data. This object is
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required, but it can be empty (`{}`). See
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<<ml-datadescription,data description objects>>.
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`description`::
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(Optional, string) A description of the job.
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`groups`::
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(Optional, array of strings) A list of job groups. See <<ml-job-resource>>.
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`model_plot_config`::
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(Optional, object) Advanced configuration option. Specifies to store model
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information along with the results. This adds overhead to the performance of
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the system and is not feasible for jobs with many entities, see
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<<ml-apimodelplotconfig>>.
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`model_snapshot_retention_days`::
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(Optional, long) The time in days that model snapshots are retained for the
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job. Older snapshots are deleted. The default value is `1`, which means
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snapshots are retained for one day (twenty-four hours).
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`renormalization_window_days`::
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(Optional, long) Advanced configuration option. The period over which
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adjustments to the score are applied, as new data is seen. See
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<<ml-job-resource>>.
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`results_index_name`::
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(Optional, string) A text string that affects the name of the {ml} results
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index. The default value is `shared`, which generates an index named
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`.ml-anomalies-shared`.
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`results_retention_days`::
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(Optional, long) Advanced configuration option. The number of days for which
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job results are retained. See <<ml-job-resource>>.
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[[ml-put-job-example]]
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==== {api-examples-title}
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The following example creates the `total-requests` job:
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[source,console]
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--------------------------------------------------
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PUT _ml/anomaly_detectors/total-requests
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{
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"description" : "Total sum of requests",
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"analysis_config" : {
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"bucket_span":"10m",
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"detectors": [
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{
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"detector_description": "Sum of total",
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"function": "sum",
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"field_name": "total"
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}
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]
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},
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"data_description" : {
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"time_field":"timestamp",
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"time_format": "epoch_ms"
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}
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}
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--------------------------------------------------
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When the job is created, you receive the following results:
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[source,console-result]
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----
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{
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"job_id" : "total-requests",
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"job_type" : "anomaly_detector",
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"job_version" : "7.4.0",
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"description" : "Total sum of requests",
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"create_time" : 1562352500629,
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"analysis_config" : {
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"bucket_span" : "10m",
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"detectors" : [
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{
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"detector_description" : "Sum of total",
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"function" : "sum",
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"field_name" : "total",
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"detector_index" : 0
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}
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],
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"influencers" : [ ]
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},
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"analysis_limits" : {
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"model_memory_limit" : "1024mb",
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"categorization_examples_limit" : 4
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},
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"data_description" : {
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"time_field" : "timestamp",
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"time_format" : "epoch_ms"
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},
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"model_snapshot_retention_days" : 1,
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"results_index_name" : "shared",
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"allow_lazy_open" : false
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}
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----
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// TESTRESPONSE[s/"job_version" : "7.4.0"/"job_version" : $body.job_version/]
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// TESTRESPONSE[s/1562352500629/$body.$_path/]
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