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* Remove `es-test-dir` book-scoped variable * Remove `plugins-examples-dir` book-scoped variable * Remove `:dependencies-dir:` and `:xes-repo-dir:` book-scoped variables - In `index.asciidoc`, two variables (`:dependencies-dir:` and `:xes-repo-dir:`) were removed. - In `sql/index.asciidoc`, the `:sql-tests:` path was updated to fuller path - In `esql/index.asciidoc`, the `:esql-tests:` path was updated idem * Replace `es-repo-dir` with `es-ref-dir` * Move `:include-xpack: true` to few files that use it, remove from index.asciidoc
129 lines
4 KiB
Text
129 lines
4 KiB
Text
[role="xpack"]
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[[ml-revert-snapshot]]
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= Revert model snapshots API
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++++
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<titleabbrev>Revert model snapshots</titleabbrev>
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++++
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Reverts to a specific snapshot.
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[[ml-revert-snapshot-request]]
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== {api-request-title}
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`POST _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>/_revert`
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[[ml-revert-snapshot-prereqs]]
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== {api-prereq-title}
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* Before you revert to a saved snapshot, you must close the job.
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* Requires the `manage_ml` cluster privilege. This privilege is included in the
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`machine_learning_admin` built-in role.
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[[ml-revert-snapshot-desc]]
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== {api-description-title}
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The {ml-features} react 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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NOTE: Reverting to a snapshot does not change the `data_counts` values of the
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{anomaly-job}, these values are not reverted to the earlier state.
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[[ml-revert-snapshot-path-parms]]
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== {api-path-parms-title}
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`<job_id>`::
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(Required, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`<snapshot_id>`::
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(Required, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=snapshot-id]
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+
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--
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You can specify `empty` as the <snapshot_id>. Reverting to the `empty` snapshot
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means the {anomaly-job} starts learning a new model from scratch when it is
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started.
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--
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[[ml-revert-snapshot-query-parms]]
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== {api-query-parms-title}
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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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model to accept records for this time period. The default value is false.
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+
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--
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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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--
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[[ml-revert-snapshot-request-body]]
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== {api-request-body-title}
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You can also specify the `delete_intervening_results` query parameter in the
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request body.
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[[ml-revert-snapshot-example]]
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== {api-examples-title}
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[source,console]
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--------------------------------------------------
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POST _ml/anomaly_detectors/low_request_rate/model_snapshots/1637092688/_revert
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{
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"delete_intervening_results": true
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}
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--------------------------------------------------
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// TEST[skip:todo]
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When the operation is complete, you receive the following results:
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[source,js]
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----
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{
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"model" : {
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"job_id" : "low_request_rate",
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"min_version" : "7.11.0",
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"timestamp" : 1637092688000,
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"description" : "State persisted due to job close at 2021-11-16T19:58:08+0000",
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"snapshot_id" : "1637092688",
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"snapshot_doc_count" : 1,
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"model_size_stats" : {
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"job_id" : "low_request_rate",
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"result_type" : "model_size_stats",
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"model_bytes" : 45200,
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"peak_model_bytes" : 101552,
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"model_bytes_exceeded" : 0,
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"model_bytes_memory_limit" : 11534336,
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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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"assignment_memory_basis" : "current_model_bytes",
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"categorized_doc_count" : 0,
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"total_category_count" : 0,
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"frequent_category_count" : 0,
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"rare_category_count" : 0,
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"dead_category_count" : 0,
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"failed_category_count" : 0,
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"categorization_status" : "ok",
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"log_time" : 1637092688530,
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"timestamp" : 1641495600000
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},
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"latest_record_time_stamp" : 1641502169000,
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"latest_result_time_stamp" : 1641495600000,
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"retain" : false
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}
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}
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----
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For a description of these properties, see the
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<<ml-get-snapshot-results,get model snapshots API>>.
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