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-revert-snapshot]]
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2018-12-20 13:23:28 -05:00
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=== Revert model snapshots API
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2017-12-14 13:52:49 -05:00
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++++
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2018-12-20 13:23:28 -05:00
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<titleabbrev>Revert model snapshots</titleabbrev>
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2017-12-14 13:52:49 -05:00
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++++
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2017-04-04 18:26:39 -04:00
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2018-06-13 16:37:35 -04:00
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Reverts to a specific snapshot.
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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-revert-snapshot-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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`POST _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>/_revert`
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2017-04-11 16:25:38 -04:00
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2019-06-27 16:58:42 -04:00
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[[ml-revert-snapshot-prereqs]]
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==== {api-prereq-title}
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2019-07-12 11:26:31 -04:00
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* Before you revert to a saved snapshot, you must close the job.
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2019-06-27 16:58:42 -04:00
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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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2019-10-07 18:23:19 -04:00
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<<security-privileges>>.
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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-revert-snapshot-desc]]
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==== {api-description-title}
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2017-04-04 18:26:39 -04:00
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2018-01-15 11:44:08 -05:00
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The {ml} feature in {xpack} reacts quickly to anomalous input, learning new
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behaviors in data. Highly anomalous input increases the variance in the models
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whilst the system learns whether this is a new step-change in behavior or a
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one-off event. In the case where this anomalous input is known to be a one-off,
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then it might be appropriate to reset the model state to a time before this
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event. For example, you might consider reverting to a saved snapshot after Black
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Friday or a critical system failure.
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2017-04-11 16:25:38 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-revert-snapshot-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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2019-07-12 11:26:31 -04:00
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`<job_id>`::
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(Required, string) Identifier for the job.
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`<snapshot_id>`::
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(Required, string) Identifier for the model snapshot.
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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-revert-snapshot-request-body]]
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==== {api-request-body-title}
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`delete_intervening_results`::
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(Optional, boolean) If true, deletes the results in the time period between
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the latest results and the time of the reverted snapshot. It also resets the
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2017-05-08 09:53:04 -04:00
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model to accept records for this time period. The default value is false.
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2017-04-04 18:26:39 -04:00
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2017-04-11 16:25:38 -04:00
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NOTE: If you choose not to delete intervening results when reverting a snapshot,
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the job will not accept input data that is older than the current time.
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If you want to resend data, then delete the intervening results.
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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-revert-snapshot-example]]
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==== {api-examples-title}
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2017-04-04 18:26:39 -04:00
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2017-04-11 16:25:38 -04:00
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The following example reverts to the `1491856080` snapshot for the
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`it_ops_new_kpi` job:
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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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2017-04-04 18:26:39 -04:00
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--------------------------------------------------
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2017-04-11 16:25:38 -04:00
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POST
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2018-12-07 15:34:11 -05:00
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_ml/anomaly_detectors/it_ops_new_kpi/model_snapshots/1491856080/_revert
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2017-04-04 18:26:39 -04:00
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{
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2017-04-11 16:25:38 -04:00
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"delete_intervening_results": true
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2017-04-04 18:26:39 -04:00
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}
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--------------------------------------------------
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// TEST[skip:todo]
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2017-04-11 16:25:38 -04:00
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When the operation is complete, you receive the following results:
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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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2017-04-11 16:25:38 -04:00
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"model": {
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"job_id": "it_ops_new_kpi",
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2018-03-15 13:38:52 -04:00
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"min_version": "6.3.0",
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2017-04-11 16:25:38 -04:00
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"timestamp": 1491856080000,
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"description": "State persisted due to job close at 2017-04-10T13:28:00-0700",
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"snapshot_id": "1491856080",
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"snapshot_doc_count": 1,
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"model_size_stats": {
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"job_id": "it_ops_new_kpi",
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"result_type": "model_size_stats",
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"model_bytes": 29518,
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"total_by_field_count": 3,
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"total_over_field_count": 0,
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"total_partition_field_count": 2,
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"bucket_allocation_failures_count": 0,
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"memory_status": "ok",
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"log_time": 1491856080000,
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"timestamp": 1455318000000
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},
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"latest_record_time_stamp": 1455318669000,
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"latest_result_time_stamp": 1455318000000,
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"retain": false
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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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