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[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
--
:api: stop-data-frame-analytics
:request: StopDataFrameAnalyticsRequest
:response: StopDataFrameAnalyticsResponse
--
[DOCS] Adds missing icons to ML HLRC APIs (#46515)
2019-09-10 11:26:56 -04:00
[role="xpack"]
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
[id="{upid}-{api}"]
=== Stop Data Frame Analytics API
The Stop Data Frame Analytics API is used to stop a running {dataframe-analytics-config}.
It accepts a +{request}+ object and responds with a +{response}+ object.
[id="{upid}-{api}-request"]
==== Stop Data Frame Analytics Request
A +{request}+ object requires a {dataframe-analytics-config} id.
["source","java",subs="attributes,callouts,macros"]
---------------------------------------------------
include-tagged::{doc-tests-file}[{api}-request]
---------------------------------------------------
<1> Constructing a new stop request referencing an existing {dataframe-analytics-config}
[7.x][ML] Set df-analytics task state to failed when appropriate (#43880) (#43906) This introduces a `failed` state to which the data frame analytics persistent task is set to when something unexpected fails. It could be the process crashing, the results processor hitting some error, etc. The failure message is then captured and set on the task state. From there, it becomes available via the _stats API as `failure_reason`. The df-analytics stop API now has a `force` boolean parameter. This allows the user to call it for a failed task in order to reset it to `stopped` after we have ensured the failure has been communicated to the user. This commit also adds the analytics version in the persistent task params as this allows us to prevent tasks to run on unsuitable nodes in the future.
2019-07-03 05:41:56 -04:00
<2> Optionally used to stop a failed task
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
include::../execution.asciidoc[]
[id="{upid}-{api}-response"]
==== Response
The returned +{response}+ object acknowledges the {dataframe-job} has stopped.