2017-06-19 21:23:58 -04:00
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[role="xpack"]
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2018-08-31 19:49:24 -04:00
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[testenv="platinum"]
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2017-04-04 18:26:39 -04:00
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[[ml-get-category]]
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2018-12-20 13:23:28 -05:00
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=== Get categories API
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2017-12-14 13:52:49 -05:00
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++++
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<titleabbrev>Get categories</titleabbrev>
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++++
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2019-07-30 13:52:23 -04:00
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Retrieves {anomaly-job} results for one or more categories.
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-request]]
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==== {api-request-title}
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2017-04-04 18:26:39 -04:00
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2018-12-07 15:34:11 -05:00
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`GET _ml/anomaly_detectors/<job_id>/results/categories` +
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2017-04-04 18:26:39 -04:00
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2018-12-07 15:34:11 -05:00
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`GET _ml/anomaly_detectors/<job_id>/results/categories/<category_id>`
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2017-04-18 18:13:21 -04:00
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2019-06-27 16:58:42 -04:00
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[[ml-get-category-prereqs]]
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==== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `monitor_ml`,
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`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. You also
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need `read` index privilege on the index that stores the results. The
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`machine_learning_admin` and `machine_learning_user` roles provide these
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2019-10-07 18:23:19 -04:00
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privileges. See <<security-privileges>> and
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<<built-in-roles>>.
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2019-06-27 16:58:42 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-desc]]
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==== {api-description-title}
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2017-06-12 13:41:14 -04:00
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2019-12-31 16:21:17 -05:00
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When `categorization_field_name` is specified in the job configuration, it is
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possible to view the definitions of the resulting categories. A category
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definition describes the common terms matched and contains examples of matched
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values.
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The anomaly results from a categorization analysis are available as bucket,
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influencer, and record results. For example, the results might indicate that
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at 16:45 there was an unusual count of log message category 11. You can then
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examine the description and examples of that category. For more information, see
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2019-12-20 13:01:19 -05:00
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
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2017-04-18 18:13:21 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-path-parms]]
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==== {api-path-parms-title}
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2017-04-04 18:26:39 -04:00
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2019-12-31 16:21:17 -05:00
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`<category_id>`::
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(Optional, long) Identifier for the category. If you do not specify this
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parameter, the API returns information about all categories in the {anomaly-job}.
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2019-07-12 11:26:31 -04:00
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`<job_id>`::
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(Required, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-request-body]]
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==== {api-request-body-title}
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2017-04-10 19:14:26 -04:00
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2019-12-31 16:21:17 -05:00
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`page`.`from`::
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(Optional, integer) Skips the specified number of categories.
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`page`.`size`::
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(Optional, integer) Specifies the maximum number of categories to obtain.
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2017-04-04 18:26:39 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-results]]
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==== {api-response-body-title}
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2017-04-10 19:14:26 -04:00
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2019-12-31 16:21:17 -05:00
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The API returns an array of category objects, which have the following
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properties:
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`category_id`::
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(unsigned integer) A unique identifier for the category.
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`examples`::
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(array) A list of examples of actual values that matched the category.
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2019-12-31 16:21:17 -05:00
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`grok_pattern`::
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experimental[] (string) A Grok pattern that could be used in {ls} or an ingest
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pipeline to extract fields from messages that match the category. This field is
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experimental and may be changed or removed in a future release. The Grok
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patterns that are found are not optimal, but are often a good starting point for
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manual tweaking.
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`job_id`::
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(string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`max_matching_length`::
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(unsigned integer) The maximum length of the fields that matched the category.
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The value is increased by 10% to enable matching for similar fields that have
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not been analyzed.
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`regex`::
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(string) A regular expression that is used to search for values that match the
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category.
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`terms`::
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(string) A space separated list of the common tokens that are matched in values
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of the category.
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2017-04-25 17:08:29 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-category-example]]
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==== {api-examples-title}
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2017-04-04 18:26:39 -04:00
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2019-09-06 11:31:13 -04:00
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[source,console]
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2017-04-10 19:14:26 -04:00
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--------------------------------------------------
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2018-12-07 15:34:11 -05:00
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GET _ml/anomaly_detectors/esxi_log/results/categories
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2017-04-28 12:14:53 -04:00
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{
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"page":{
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"size": 1
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}
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}
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2017-04-10 19:14:26 -04:00
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--------------------------------------------------
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// TEST[skip:todo]
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2017-04-28 12:14:53 -04:00
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In this example, the API returns the following information:
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2017-04-21 11:23:27 -04:00
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[source,js]
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2017-04-04 18:26:39 -04:00
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----
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{
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2017-04-10 19:14:26 -04:00
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"count": 11,
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"categories": [
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{
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2018-05-15 04:02:38 -04:00
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"job_id" : "esxi_log",
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"category_id" : 1,
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"terms" : "Vpxa verbose vpxavpxaInvtVm opID VpxaInvtVmChangeListener Guest DiskInfo Changed",
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"regex" : ".*?Vpxa.+?verbose.+?vpxavpxaInvtVm.+?opID.+?VpxaInvtVmChangeListener.+?Guest.+?DiskInfo.+?Changed.*",
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"max_matching_length": 154,
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"examples" : [
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"Oct 19 17:04:44 esxi1.acme.com Vpxa: [3CB3FB90 verbose 'vpxavpxaInvtVm' opID=WFU-33d82c31] [VpxaInvtVmChangeListener] Guest DiskInfo Changed",
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"Oct 19 17:04:45 esxi2.acme.com Vpxa: [3CA66B90 verbose 'vpxavpxaInvtVm' opID=WFU-33927856] [VpxaInvtVmChangeListener] Guest DiskInfo Changed",
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"Oct 19 17:04:51 esxi1.acme.com Vpxa: [FFDBAB90 verbose 'vpxavpxaInvtVm' opID=WFU-25e0d447] [VpxaInvtVmChangeListener] Guest DiskInfo Changed",
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"Oct 19 17:04:58 esxi2.acme.com Vpxa: [FFDDBB90 verbose 'vpxavpxaInvtVm' opID=WFU-bbff0134] [VpxaInvtVmChangeListener] Guest DiskInfo Changed"
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],
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"grok_pattern" : ".*?%{SYSLOGTIMESTAMP:timestamp}.+?Vpxa.+?%{BASE16NUM:field}.+?verbose.+?vpxavpxaInvtVm.+?opID.+?VpxaInvtVmChangeListener.+?Guest.+?DiskInfo.+?Changed.*"
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2017-04-28 12:14:53 -04:00
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}
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2017-04-04 18:26:39 -04:00
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]
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}
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----
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