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This adds a setting to data frame analytics jobs called `max_number_threads`. The setting expects a positive integer. When used the user specifies the max number of threads that may be used by the analysis. Note that the actual number of threads used is limited by the number of processors on the node where the job is assigned. Also, the process may use a couple more threads for operational functionality that is not the analysis itself. This setting may also be updated for a stopped job. More threads may reduce the time it takes to complete the job at the cost of using more CPU. Backport of #59254 and #57274
107 lines
3.5 KiB
Plaintext
107 lines
3.5 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[update-dfanalytics]]
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=== Update {dfanalytics-jobs} API
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[subs="attributes"]
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++++
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<titleabbrev>Update {dfanalytics-jobs}</titleabbrev>
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++++
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Updates an existing {dfanalytics-job}.
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experimental[]
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[[ml-update-dfanalytics-request]]
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==== {api-request-title}
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`POST _ml/data_frame/analytics/<data_frame_analytics_id>/_update`
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[[ml-update-dfanalytics-prereq]]
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==== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following
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built-in roles and privileges:
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* `machine_learning_admin`
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* `kibana_admin` (UI only)
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* source indices: `read`, `view_index_metadata`
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* destination index: `read`, `create_index`, `manage` and `index`
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* cluster: `monitor` (UI only)
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For more information, see <<security-privileges>> and <<built-in-roles>>.
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NOTE: The {dfanalytics-job} remembers which roles the user who created it had at
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the time of creation. When you start the job, it performs the analysis using
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those same roles. If you provide
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<<http-clients-secondary-authorization,secondary authorization headers>>,
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those credentials are used instead.
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[[ml-update-dfanalytics-desc]]
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==== {api-description-title}
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This API updates an existing {dfanalytics-job} that performs an analysis on the source
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indices and stores the outcome in a destination index.
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[[ml-update-dfanalytics-path-params]]
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==== {api-path-parms-title}
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`<data_frame_analytics_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define]
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[role="child_attributes"]
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[[ml-update-dfanalytics-request-body]]
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==== {api-request-body-title}
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`allow_lazy_start`::
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(Optional, boolean)
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Specifies whether this job can start when there is insufficient {ml} node
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capacity for it to be immediately assigned to a node. The default is `false`; if
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a {ml} node with capacity to run the job cannot immediately be found, the API
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returns an error. However, this is also subject to the cluster-wide
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`xpack.ml.max_lazy_ml_nodes` setting. See <<advanced-ml-settings>>. If this
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option is set to `true`, the API does not return an error and the job waits in
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the `starting` state until sufficient {ml} node capacity is available.
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`description`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=description-dfa]
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`max_num_threads`::
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(Optional, integer)
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The maximum number of threads to be used by the analysis.
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The default value is `1`. Using more threads may decrease the time
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necessary to complete the analysis at the cost of using more CPU.
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Note that the process may use additional threads for operational
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functionality other than the analysis itself.
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`model_memory_limit`::
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(Optional, string)
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The approximate maximum amount of memory resources that are permitted for
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analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If
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your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
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setting, an error occurs when you try to create {dfanalytics-jobs} that have
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`model_memory_limit` values greater than that setting. For more information, see
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<<ml-settings>>.
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[[ml-update-dfanalytics-example]]
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==== {api-examples-title}
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[[ml-update-dfanalytics-example-preprocess]]
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===== Updating model memory limit example
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The following example shows how to update the model memory limit for the existing {dfanalytics} configuration.
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[source,console]
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--------------------------------------------------
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POST _ml/data_frame/analytics/model-flight-delays/_update
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{
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"model_memory_limit": "200mb"
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}
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--------------------------------------------------
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// TEST[skip:setup kibana sample data]
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