This commit replaces the _estimate_memory_usage API with
a new API, the _explain API.
The API consolidates information that is useful before
creating a data frame analytics job.
It includes:
- memory estimation
- field selection explanation
Memory estimation is moved here from what was previously
calculated in the _estimate_memory_usage API.
Field selection is a new feature that explains to the user
whether each available field was selected to be included or
not in the analysis. In the case it was not included, it also
explains the reason why.
Backport of #49455
Adds the following parameters to `outlier_detection`:
- `compute_feature_influence` (boolean): whether to compute or not
feature influence scores
- `outlier_fraction` (double): the proportion of the data set assumed
to be outlying prior to running outlier detection
- `standardization_enabled` (boolean): whether to apply standardization
to the feature values
Backport of #47600
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.
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
* ML: Adding missing datacheck to datafeedjob
* Adding client side and docs
* Making adjustments to validations
* Making values default to on, having more sensible limits
* Intermittent commit, still need to figure out interval
* Adjusting delayed data check interval
* updating docs
* Making parameter Boolean, so it is nullable
* bumping bwc to 7 before backport
* changing to version current
* moving delayed data check config its own object
* Separation of duties for delayed data detection
* fixing checkstyles
* fixing checkstyles
* Adjusting default behavior so that null windows are allowed
* Mentioning the default value
* Fixing comments, syncing up validations
* HLRC: ML Add preview datafeed api
* Changing deprecation handling for parser
* Removing some duplication in docs, will address other APIs in another PR
* HLRC: ML Cleanup docs
* updating get datafeed stats docs
This changes the delete job API by adding
the choice to delete a job asynchronously.
The commit adds a `wait_for_completion` parameter
to the delete job request. When set to `false`,
the action returns immediately and the response
contains the task id.
This also changes the handling of subsequent
delete requests for a job that is already being
deleted. It now uses the task framework to check
if the job is being deleted instead of the cluster
state. This is a beneficial for it is going to also
be working once the job configs are moved out of the
cluster state and into an index. Also, force delete
requests that are waiting for the job to be deleted
will not proceed with the deletion if the first task
fails. This will prevent overloading the cluster. Instead,
the failure is communicated better via notifications
so that the user may retry.
Finally, this makes the `deleting` property of the job
visible (also it was renamed from `deleted`). This allows
a client to render a deleting job differently.
Closes#32836
* HLRC: ML Add preview datafeed api
* Changing deprecation handling for parser
* Removing some duplication in docs, will address other APIs in another PR
Dimitris Athanasiou
Benjamin Trent
Przemysław Witek
Lisa Cawley
Vladimir Dolzhenko
Ed Savage
David Roberts
Ignacio Vera
David Kyle