Class ElasticsearchMlAsyncClient
java.lang.Object
co.elastic.clients.ApiClient<ElasticsearchTransport,ElasticsearchMlAsyncClient>
co.elastic.clients.elasticsearch.ml.ElasticsearchMlAsyncClient
public class ElasticsearchMlAsyncClient
extends ApiClient<ElasticsearchTransport,ElasticsearchMlAsyncClient>
Client for the ml namespace.
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Field Summary
Fields inherited from class co.elastic.clients.ApiClient
transport, transportOptions -
Constructor Summary
ConstructorsConstructorDescriptionElasticsearchMlAsyncClient(ElasticsearchTransport transport) ElasticsearchMlAsyncClient(ElasticsearchTransport transport, TransportOptions transportOptions) -
Method Summary
Modifier and TypeMethodDescriptionClears a trained model deployment cache on all nodes where the trained model is assigned.clearTrainedModelDeploymentCache(Function<ClearTrainedModelDeploymentCacheRequest.Builder, ObjectBuilder<ClearTrainedModelDeploymentCacheRequest>> fn) Clears a trained model deployment cache on all nodes where the trained model is assigned.closeJob(CloseJobRequest request) Close anomaly detection jobs A job can be opened and closed multiple times throughout its lifecycle.Close anomaly detection jobs A job can be opened and closed multiple times throughout its lifecycle.deleteCalendar(DeleteCalendarRequest request) Removes all scheduled events from a calendar, then deletes it.Removes all scheduled events from a calendar, then deletes it.Deletes scheduled events from a calendar.deleteCalendarEvent(Function<DeleteCalendarEventRequest.Builder, ObjectBuilder<DeleteCalendarEventRequest>> fn) Deletes scheduled events from a calendar.Deletes anomaly detection jobs from a calendar.deleteCalendarJob(Function<DeleteCalendarJobRequest.Builder, ObjectBuilder<DeleteCalendarJobRequest>> fn) Deletes anomaly detection jobs from a calendar.deleteDatafeed(DeleteDatafeedRequest request) Deletes an existing datafeed.Deletes an existing datafeed.Deletes a data frame analytics job.deleteDataFrameAnalytics(Function<DeleteDataFrameAnalyticsRequest.Builder, ObjectBuilder<DeleteDataFrameAnalyticsRequest>> fn) Deletes a data frame analytics job.Deletes expired and unused machine learning data.Deletes expired and unused machine learning data.deleteExpiredData(Function<DeleteExpiredDataRequest.Builder, ObjectBuilder<DeleteExpiredDataRequest>> fn) Deletes expired and unused machine learning data.deleteFilter(DeleteFilterRequest request) Deletes a filter.Deletes a filter.deleteForecast(DeleteForecastRequest request) Deletes forecasts from a machine learning job.Deletes forecasts from a machine learning job.deleteJob(DeleteJobRequest request) Deletes an anomaly detection job.Deletes an anomaly detection job.Deletes an existing model snapshot.deleteModelSnapshot(Function<DeleteModelSnapshotRequest.Builder, ObjectBuilder<DeleteModelSnapshotRequest>> fn) Deletes an existing model snapshot.Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.deleteTrainedModel(Function<DeleteTrainedModelRequest.Builder, ObjectBuilder<DeleteTrainedModelRequest>> fn) Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.Deletes a trained model alias.deleteTrainedModelAlias(Function<DeleteTrainedModelAliasRequest.Builder, ObjectBuilder<DeleteTrainedModelAliasRequest>> fn) Deletes a trained model alias.Makes an estimation of the memory usage for an anomaly detection job model.Makes an estimation of the memory usage for an anomaly detection job model.estimateModelMemory(Function<EstimateModelMemoryRequest.Builder, ObjectBuilder<EstimateModelMemoryRequest>> fn) Makes an estimation of the memory usage for an anomaly detection job model.Evaluates the data frame analytics for an annotated index.evaluateDataFrame(Function<EvaluateDataFrameRequest.Builder, ObjectBuilder<EvaluateDataFrameRequest>> fn) Evaluates the data frame analytics for an annotated index.Explains a data frame analytics config.Explains a data frame analytics config.explainDataFrameAnalytics(Function<ExplainDataFrameAnalyticsRequest.Builder, ObjectBuilder<ExplainDataFrameAnalyticsRequest>> fn) Explains a data frame analytics config.flushJob(FlushJobRequest request) Forces any buffered data to be processed by the job.Forces any buffered data to be processed by the job.forecast(ForecastRequest request) Predicts the future behavior of a time series by using its historical behavior.Predicts the future behavior of a time series by using its historical behavior.getBuckets(GetBucketsRequest request) Retrieves anomaly detection job results for one or more buckets.Retrieves anomaly detection job results for one or more buckets.Retrieves information about the scheduled events in calendars.getCalendarEvents(Function<GetCalendarEventsRequest.Builder, ObjectBuilder<GetCalendarEventsRequest>> fn) Retrieves information about the scheduled events in calendars.Retrieves configuration information for calendars.getCalendars(GetCalendarsRequest request) Retrieves configuration information for calendars.Retrieves configuration information for calendars.getCategories(GetCategoriesRequest request) Retrieves anomaly detection job results for one or more categories.Retrieves anomaly detection job results for one or more categories.Retrieves configuration information for datafeeds.getDatafeeds(GetDatafeedsRequest request) Retrieves configuration information for datafeeds.Retrieves configuration information for datafeeds.Retrieves usage information for datafeeds.getDatafeedStats(GetDatafeedStatsRequest request) Retrieves usage information for datafeeds.getDatafeedStats(Function<GetDatafeedStatsRequest.Builder, ObjectBuilder<GetDatafeedStatsRequest>> fn) Retrieves usage information for datafeeds.Retrieves configuration information for data frame analytics jobs.Retrieves configuration information for data frame analytics jobs.getDataFrameAnalytics(Function<GetDataFrameAnalyticsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsRequest>> fn) Retrieves configuration information for data frame analytics jobs.Retrieves usage information for data frame analytics jobs.Retrieves usage information for data frame analytics jobs.getDataFrameAnalyticsStats(Function<GetDataFrameAnalyticsStatsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsStatsRequest>> fn) Retrieves usage information for data frame analytics jobs.Retrieves filters.getFilters(GetFiltersRequest request) Retrieves filters.Retrieves filters.getInfluencers(GetInfluencersRequest request) Retrieves anomaly detection job results for one or more influencers.Retrieves anomaly detection job results for one or more influencers.getJobs()Retrieves configuration information for anomaly detection jobs.getJobs(GetJobsRequest request) Retrieves configuration information for anomaly detection jobs.final CompletableFuture<GetJobsResponse>Retrieves configuration information for anomaly detection jobs.Retrieves usage information for anomaly detection jobs.getJobStats(GetJobStatsRequest request) Retrieves usage information for anomaly detection jobs.Retrieves usage information for anomaly detection jobs.Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.getMemoryStats(GetMemoryStatsRequest request) Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.Retrieves information about model snapshots.getModelSnapshots(Function<GetModelSnapshotsRequest.Builder, ObjectBuilder<GetModelSnapshotsRequest>> fn) Retrieves information about model snapshots.Retrieves usage information for anomaly detection job model snapshot upgrades.getModelSnapshotUpgradeStats(Function<GetModelSnapshotUpgradeStatsRequest.Builder, ObjectBuilder<GetModelSnapshotUpgradeStatsRequest>> fn) Retrieves usage information for anomaly detection job model snapshot upgrades.Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.getOverallBuckets(Function<GetOverallBucketsRequest.Builder, ObjectBuilder<GetOverallBucketsRequest>> fn) Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.getRecords(GetRecordsRequest request) Retrieves anomaly records for an anomaly detection job.Retrieves anomaly records for an anomaly detection job.Retrieves configuration information for a trained model.getTrainedModels(GetTrainedModelsRequest request) Retrieves configuration information for a trained model.getTrainedModels(Function<GetTrainedModelsRequest.Builder, ObjectBuilder<GetTrainedModelsRequest>> fn) Retrieves configuration information for a trained model.Retrieves usage information for trained models.Retrieves usage information for trained models.getTrainedModelsStats(Function<GetTrainedModelsStatsRequest.Builder, ObjectBuilder<GetTrainedModelsStatsRequest>> fn) Retrieves usage information for trained models.Evaluates a trained model.inferTrainedModel(Function<InferTrainedModelRequest.Builder, ObjectBuilder<InferTrainedModelRequest>> fn) Evaluates a trained model.info()Returns defaults and limits used by machine learning.openJob(OpenJobRequest request) Opens one or more anomaly detection jobs.final CompletableFuture<OpenJobResponse>Opens one or more anomaly detection jobs.Adds scheduled events to a calendar.postCalendarEvents(Function<PostCalendarEventsRequest.Builder, ObjectBuilder<PostCalendarEventsRequest>> fn) Adds scheduled events to a calendar.<TData> CompletableFuture<PostDataResponse>postData(PostDataRequest<TData> request) Sends data to an anomaly detection job for analysis.final <TData> CompletableFuture<PostDataResponse>postData(Function<PostDataRequest.Builder<TData>, ObjectBuilder<PostDataRequest<TData>>> fn) Sends data to an anomaly detection job for analysis.<TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(PreviewDatafeedRequest request, Class<TDocument> tDocumentClass) Previews a datafeed.<TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(PreviewDatafeedRequest request, Type tDocumentType) Previews a datafeed.final <TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Class<TDocument> tDocumentClass) Previews a datafeed.final <TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Type tDocumentType) Previews a datafeed.Previews the extracted features used by a data frame analytics config.Previews the extracted features used by a data frame analytics config.previewDataFrameAnalytics(Function<PreviewDataFrameAnalyticsRequest.Builder, ObjectBuilder<PreviewDataFrameAnalyticsRequest>> fn) Previews the extracted features used by a data frame analytics config.putCalendar(PutCalendarRequest request) Creates a calendar.Creates a calendar.putCalendarJob(PutCalendarJobRequest request) Adds an anomaly detection job to a calendar.Adds an anomaly detection job to a calendar.putDatafeed(PutDatafeedRequest request) Instantiates a datafeed.Instantiates a datafeed.Instantiates a data frame analytics job.putDataFrameAnalytics(Function<PutDataFrameAnalyticsRequest.Builder, ObjectBuilder<PutDataFrameAnalyticsRequest>> fn) Instantiates a data frame analytics job.putFilter(PutFilterRequest request) Instantiates a filter.Instantiates a filter.putJob(PutJobRequest request) Instantiates an anomaly detection job.final CompletableFuture<PutJobResponse>Instantiates an anomaly detection job.putTrainedModel(PutTrainedModelRequest request) Enables you to supply a trained model that is not created by data frame analytics.Enables you to supply a trained model that is not created by data frame analytics.Creates or updates a trained model alias.putTrainedModelAlias(Function<PutTrainedModelAliasRequest.Builder, ObjectBuilder<PutTrainedModelAliasRequest>> fn) Creates or updates a trained model alias.Creates part of a trained model definition.putTrainedModelDefinitionPart(Function<PutTrainedModelDefinitionPartRequest.Builder, ObjectBuilder<PutTrainedModelDefinitionPartRequest>> fn) Creates part of a trained model definition.Creates a trained model vocabulary.putTrainedModelVocabulary(Function<PutTrainedModelVocabularyRequest.Builder, ObjectBuilder<PutTrainedModelVocabularyRequest>> fn) Creates a trained model vocabulary.resetJob(ResetJobRequest request) Resets an anomaly detection job.Resets an anomaly detection job.Reverts to a specific snapshot.revertModelSnapshot(Function<RevertModelSnapshotRequest.Builder, ObjectBuilder<RevertModelSnapshotRequest>> fn) Reverts to a specific snapshot.Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade.setUpgradeMode(SetUpgradeModeRequest request) Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade.Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade.startDatafeed(StartDatafeedRequest request) Starts one or more datafeeds.Starts one or more datafeeds.Starts a data frame analytics job.startDataFrameAnalytics(Function<StartDataFrameAnalyticsRequest.Builder, ObjectBuilder<StartDataFrameAnalyticsRequest>> fn) Starts a data frame analytics job.Starts a trained model deployment, which allocates the model to every machine learning node.startTrainedModelDeployment(Function<StartTrainedModelDeploymentRequest.Builder, ObjectBuilder<StartTrainedModelDeploymentRequest>> fn) Starts a trained model deployment, which allocates the model to every machine learning node.stopDatafeed(StopDatafeedRequest request) Stops one or more datafeeds.Stops one or more datafeeds.Stops one or more data frame analytics jobs.stopDataFrameAnalytics(Function<StopDataFrameAnalyticsRequest.Builder, ObjectBuilder<StopDataFrameAnalyticsRequest>> fn) Stops one or more data frame analytics jobs.Stops a trained model deployment.stopTrainedModelDeployment(Function<StopTrainedModelDeploymentRequest.Builder, ObjectBuilder<StopTrainedModelDeploymentRequest>> fn) Stops a trained model deployment.updateDatafeed(UpdateDatafeedRequest request) Updates the properties of a datafeed.Updates the properties of a datafeed.Updates an existing data frame analytics job.updateDataFrameAnalytics(Function<UpdateDataFrameAnalyticsRequest.Builder, ObjectBuilder<UpdateDataFrameAnalyticsRequest>> fn) Updates an existing data frame analytics job.updateFilter(UpdateFilterRequest request) Updates the description of a filter, adds items, or removes items from the list.Updates the description of a filter, adds items, or removes items from the list.updateJob(UpdateJobRequest request) Updates certain properties of an anomaly detection job.Updates certain properties of an anomaly detection job.Updates certain properties of a snapshot.updateModelSnapshot(Function<UpdateModelSnapshotRequest.Builder, ObjectBuilder<UpdateModelSnapshotRequest>> fn) Updates certain properties of a snapshot.Starts a trained model deployment, which allocates the model to every machine learning node.updateTrainedModelDeployment(Function<UpdateTrainedModelDeploymentRequest.Builder, ObjectBuilder<UpdateTrainedModelDeploymentRequest>> fn) Starts a trained model deployment, which allocates the model to every machine learning node.Upgrades an anomaly detection model snapshot to the latest major version.upgradeJobSnapshot(Function<UpgradeJobSnapshotRequest.Builder, ObjectBuilder<UpgradeJobSnapshotRequest>> fn) Upgrades an anomaly detection model snapshot to the latest major version.validate()Validates an anomaly detection job.validate(ValidateRequest request) Validates an anomaly detection job.Validates an anomaly detection job.Validates an anomaly detection detector.validateDetector(ValidateDetectorRequest request) Validates an anomaly detection detector.validateDetector(Function<ValidateDetectorRequest.Builder, ObjectBuilder<ValidateDetectorRequest>> fn) Validates an anomaly detection detector.withTransportOptions(TransportOptions transportOptions) Creates a new client with some request optionsMethods inherited from class co.elastic.clients.ApiClient
_jsonpMapper, _transport, _transportOptions, getDeserializer, withTransportOptions
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Constructor Details
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ElasticsearchMlAsyncClient
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ElasticsearchMlAsyncClient
public ElasticsearchMlAsyncClient(ElasticsearchTransport transport, @Nullable TransportOptions transportOptions)
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Method Details
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withTransportOptions
Description copied from class:ApiClientCreates a new client with some request options- Specified by:
withTransportOptionsin classApiClient<ElasticsearchTransport,ElasticsearchMlAsyncClient>
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clearTrainedModelDeploymentCache
public CompletableFuture<ClearTrainedModelDeploymentCacheResponse> clearTrainedModelDeploymentCache(ClearTrainedModelDeploymentCacheRequest request) Clears a trained model deployment cache on all nodes where the trained model is assigned. A trained model deployment may have an inference cache enabled. As requests are handled by each allocated node, their responses may be cached on that individual node. Calling this API clears the caches without restarting the deployment.- See Also:
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clearTrainedModelDeploymentCache
public final CompletableFuture<ClearTrainedModelDeploymentCacheResponse> clearTrainedModelDeploymentCache(Function<ClearTrainedModelDeploymentCacheRequest.Builder, ObjectBuilder<ClearTrainedModelDeploymentCacheRequest>> fn) Clears a trained model deployment cache on all nodes where the trained model is assigned. A trained model deployment may have an inference cache enabled. As requests are handled by each allocated node, their responses may be cached on that individual node. Calling this API clears the caches without restarting the deployment.- Parameters:
fn- a function that initializes a builder to create theClearTrainedModelDeploymentCacheRequest- See Also:
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closeJob
Close anomaly detection jobs A job can be opened and closed multiple times throughout its lifecycle. A closed job cannot receive data or perform analysis operations, but you can still explore and navigate results. When you close a job, it runs housekeeping tasks such as pruning the model history, flushing buffers, calculating final results and persisting the model snapshots. Depending upon the size of the job, it could take several minutes to close and the equivalent time to re-open. After it is closed, the job has a minimal overhead on the cluster except for maintaining its meta data. Therefore it is a best practice to close jobs that are no longer required to process data. If you close an anomaly detection job whose datafeed is running, the request first tries to stop the datafeed. This behavior is equivalent to calling stop datafeed API with the same timeout and force parameters as the close job request. When a datafeed that has a specified end date stops, it automatically closes its associated job.- See Also:
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closeJob
public final CompletableFuture<CloseJobResponse> closeJob(Function<CloseJobRequest.Builder, ObjectBuilder<CloseJobRequest>> fn) Close anomaly detection jobs A job can be opened and closed multiple times throughout its lifecycle. A closed job cannot receive data or perform analysis operations, but you can still explore and navigate results. When you close a job, it runs housekeeping tasks such as pruning the model history, flushing buffers, calculating final results and persisting the model snapshots. Depending upon the size of the job, it could take several minutes to close and the equivalent time to re-open. After it is closed, the job has a minimal overhead on the cluster except for maintaining its meta data. Therefore it is a best practice to close jobs that are no longer required to process data. If you close an anomaly detection job whose datafeed is running, the request first tries to stop the datafeed. This behavior is equivalent to calling stop datafeed API with the same timeout and force parameters as the close job request. When a datafeed that has a specified end date stops, it automatically closes its associated job.- Parameters:
fn- a function that initializes a builder to create theCloseJobRequest- See Also:
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deleteCalendar
Removes all scheduled events from a calendar, then deletes it.- See Also:
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deleteCalendar
public final CompletableFuture<DeleteCalendarResponse> deleteCalendar(Function<DeleteCalendarRequest.Builder, ObjectBuilder<DeleteCalendarRequest>> fn) Removes all scheduled events from a calendar, then deletes it.- Parameters:
fn- a function that initializes a builder to create theDeleteCalendarRequest- See Also:
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deleteCalendarEvent
public CompletableFuture<DeleteCalendarEventResponse> deleteCalendarEvent(DeleteCalendarEventRequest request) Deletes scheduled events from a calendar.- See Also:
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deleteCalendarEvent
public final CompletableFuture<DeleteCalendarEventResponse> deleteCalendarEvent(Function<DeleteCalendarEventRequest.Builder, ObjectBuilder<DeleteCalendarEventRequest>> fn) Deletes scheduled events from a calendar.- Parameters:
fn- a function that initializes a builder to create theDeleteCalendarEventRequest- See Also:
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deleteCalendarJob
public CompletableFuture<DeleteCalendarJobResponse> deleteCalendarJob(DeleteCalendarJobRequest request) Deletes anomaly detection jobs from a calendar.- See Also:
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deleteCalendarJob
public final CompletableFuture<DeleteCalendarJobResponse> deleteCalendarJob(Function<DeleteCalendarJobRequest.Builder, ObjectBuilder<DeleteCalendarJobRequest>> fn) Deletes anomaly detection jobs from a calendar.- Parameters:
fn- a function that initializes a builder to create theDeleteCalendarJobRequest- See Also:
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deleteDataFrameAnalytics
public CompletableFuture<DeleteDataFrameAnalyticsResponse> deleteDataFrameAnalytics(DeleteDataFrameAnalyticsRequest request) Deletes a data frame analytics job.- See Also:
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deleteDataFrameAnalytics
public final CompletableFuture<DeleteDataFrameAnalyticsResponse> deleteDataFrameAnalytics(Function<DeleteDataFrameAnalyticsRequest.Builder, ObjectBuilder<DeleteDataFrameAnalyticsRequest>> fn) Deletes a data frame analytics job.- Parameters:
fn- a function that initializes a builder to create theDeleteDataFrameAnalyticsRequest- See Also:
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deleteDatafeed
Deletes an existing datafeed.- See Also:
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deleteDatafeed
public final CompletableFuture<DeleteDatafeedResponse> deleteDatafeed(Function<DeleteDatafeedRequest.Builder, ObjectBuilder<DeleteDatafeedRequest>> fn) Deletes an existing datafeed.- Parameters:
fn- a function that initializes a builder to create theDeleteDatafeedRequest- See Also:
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deleteExpiredData
public CompletableFuture<DeleteExpiredDataResponse> deleteExpiredData(DeleteExpiredDataRequest request) Deletes expired and unused machine learning data. Deletes all job results, model snapshots and forecast data that have exceeded their retention days period. Machine learning state documents that are not associated with any job are also deleted. You can limit the request to a single or set of anomaly detection jobs by using a job identifier, a group name, a comma-separated list of jobs, or a wildcard expression. You can delete expired data for all anomaly detection jobs by using _all, by specifying * as the <job_id>, or by omitting the <job_id>.- See Also:
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deleteExpiredData
public final CompletableFuture<DeleteExpiredDataResponse> deleteExpiredData(Function<DeleteExpiredDataRequest.Builder, ObjectBuilder<DeleteExpiredDataRequest>> fn) Deletes expired and unused machine learning data. Deletes all job results, model snapshots and forecast data that have exceeded their retention days period. Machine learning state documents that are not associated with any job are also deleted. You can limit the request to a single or set of anomaly detection jobs by using a job identifier, a group name, a comma-separated list of jobs, or a wildcard expression. You can delete expired data for all anomaly detection jobs by using _all, by specifying * as the <job_id>, or by omitting the <job_id>.- Parameters:
fn- a function that initializes a builder to create theDeleteExpiredDataRequest- See Also:
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deleteExpiredData
Deletes expired and unused machine learning data. Deletes all job results, model snapshots and forecast data that have exceeded their retention days period. Machine learning state documents that are not associated with any job are also deleted. You can limit the request to a single or set of anomaly detection jobs by using a job identifier, a group name, a comma-separated list of jobs, or a wildcard expression. You can delete expired data for all anomaly detection jobs by using _all, by specifying * as the <job_id>, or by omitting the <job_id>.- See Also:
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deleteFilter
Deletes a filter. If an anomaly detection job references the filter, you cannot delete the filter. You must update or delete the job before you can delete the filter.- See Also:
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deleteFilter
public final CompletableFuture<DeleteFilterResponse> deleteFilter(Function<DeleteFilterRequest.Builder, ObjectBuilder<DeleteFilterRequest>> fn) Deletes a filter. If an anomaly detection job references the filter, you cannot delete the filter. You must update or delete the job before you can delete the filter.- Parameters:
fn- a function that initializes a builder to create theDeleteFilterRequest- See Also:
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deleteForecast
Deletes forecasts from a machine learning job. By default, forecasts are retained for 14 days. You can specify a different retention period with theexpires_inparameter in the forecast jobs API. The delete forecast API enables you to delete one or more forecasts before they expire.- See Also:
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deleteForecast
public final CompletableFuture<DeleteForecastResponse> deleteForecast(Function<DeleteForecastRequest.Builder, ObjectBuilder<DeleteForecastRequest>> fn) Deletes forecasts from a machine learning job. By default, forecasts are retained for 14 days. You can specify a different retention period with theexpires_inparameter in the forecast jobs API. The delete forecast API enables you to delete one or more forecasts before they expire.- Parameters:
fn- a function that initializes a builder to create theDeleteForecastRequest- See Also:
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deleteJob
Deletes an anomaly detection job.All job configuration, model state and results are deleted. It is not currently possible to delete multiple jobs using wildcards or a comma separated list. If you delete a job that has a datafeed, the request first tries to delete the datafeed. This behavior is equivalent to calling the delete datafeed API with the same timeout and force parameters as the delete job request.
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deleteJob
public final CompletableFuture<DeleteJobResponse> deleteJob(Function<DeleteJobRequest.Builder, ObjectBuilder<DeleteJobRequest>> fn) Deletes an anomaly detection job.All job configuration, model state and results are deleted. It is not currently possible to delete multiple jobs using wildcards or a comma separated list. If you delete a job that has a datafeed, the request first tries to delete the datafeed. This behavior is equivalent to calling the delete datafeed API with the same timeout and force parameters as the delete job request.
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fn- a function that initializes a builder to create theDeleteJobRequest- See Also:
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deleteModelSnapshot
public CompletableFuture<DeleteModelSnapshotResponse> deleteModelSnapshot(DeleteModelSnapshotRequest request) Deletes an existing model snapshot. You cannot delete the active model snapshot. To delete that snapshot, first revert to a different one. To identify the active model snapshot, refer to themodel_snapshot_idin the results from the get jobs API.- See Also:
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deleteModelSnapshot
public final CompletableFuture<DeleteModelSnapshotResponse> deleteModelSnapshot(Function<DeleteModelSnapshotRequest.Builder, ObjectBuilder<DeleteModelSnapshotRequest>> fn) Deletes an existing model snapshot. You cannot delete the active model snapshot. To delete that snapshot, first revert to a different one. To identify the active model snapshot, refer to themodel_snapshot_idin the results from the get jobs API.- Parameters:
fn- a function that initializes a builder to create theDeleteModelSnapshotRequest- See Also:
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deleteTrainedModel
public CompletableFuture<DeleteTrainedModelResponse> deleteTrainedModel(DeleteTrainedModelRequest request) Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.- See Also:
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deleteTrainedModel
public final CompletableFuture<DeleteTrainedModelResponse> deleteTrainedModel(Function<DeleteTrainedModelRequest.Builder, ObjectBuilder<DeleteTrainedModelRequest>> fn) Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.- Parameters:
fn- a function that initializes a builder to create theDeleteTrainedModelRequest- See Also:
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deleteTrainedModelAlias
public CompletableFuture<DeleteTrainedModelAliasResponse> deleteTrainedModelAlias(DeleteTrainedModelAliasRequest request) Deletes a trained model alias. This API deletes an existing model alias that refers to a trained model. If the model alias is missing or refers to a model other than the one identified by themodel_id, this API returns an error.- See Also:
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deleteTrainedModelAlias
public final CompletableFuture<DeleteTrainedModelAliasResponse> deleteTrainedModelAlias(Function<DeleteTrainedModelAliasRequest.Builder, ObjectBuilder<DeleteTrainedModelAliasRequest>> fn) Deletes a trained model alias. This API deletes an existing model alias that refers to a trained model. If the model alias is missing or refers to a model other than the one identified by themodel_id, this API returns an error.- Parameters:
fn- a function that initializes a builder to create theDeleteTrainedModelAliasRequest- See Also:
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estimateModelMemory
public CompletableFuture<EstimateModelMemoryResponse> estimateModelMemory(EstimateModelMemoryRequest request) Makes an estimation of the memory usage for an anomaly detection job model. It is based on analysis configuration details for the job and cardinality estimates for the fields it references.- See Also:
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estimateModelMemory
public final CompletableFuture<EstimateModelMemoryResponse> estimateModelMemory(Function<EstimateModelMemoryRequest.Builder, ObjectBuilder<EstimateModelMemoryRequest>> fn) Makes an estimation of the memory usage for an anomaly detection job model. It is based on analysis configuration details for the job and cardinality estimates for the fields it references.- Parameters:
fn- a function that initializes a builder to create theEstimateModelMemoryRequest- See Also:
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estimateModelMemory
Makes an estimation of the memory usage for an anomaly detection job model. It is based on analysis configuration details for the job and cardinality estimates for the fields it references.- See Also:
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evaluateDataFrame
public CompletableFuture<EvaluateDataFrameResponse> evaluateDataFrame(EvaluateDataFrameRequest request) Evaluates the data frame analytics for an annotated index. The API packages together commonly used evaluation metrics for various types of machine learning features. This has been designed for use on indexes created by data frame analytics. Evaluation requires both a ground truth field and an analytics result field to be present.- See Also:
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evaluateDataFrame
public final CompletableFuture<EvaluateDataFrameResponse> evaluateDataFrame(Function<EvaluateDataFrameRequest.Builder, ObjectBuilder<EvaluateDataFrameRequest>> fn) Evaluates the data frame analytics for an annotated index. The API packages together commonly used evaluation metrics for various types of machine learning features. This has been designed for use on indexes created by data frame analytics. Evaluation requires both a ground truth field and an analytics result field to be present.- Parameters:
fn- a function that initializes a builder to create theEvaluateDataFrameRequest- See Also:
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explainDataFrameAnalytics
public CompletableFuture<ExplainDataFrameAnalyticsResponse> explainDataFrameAnalytics(ExplainDataFrameAnalyticsRequest request) Explains a data frame analytics config. This API provides explanations for a data frame analytics config that either exists already or one that has not been created yet. The following explanations are provided:- which fields are included or not in the analysis and why,
- how much memory is estimated to be required. The estimate can be used when deciding the appropriate value for model_memory_limit setting later on. If you have object fields or fields that are excluded via source filtering, they are not included in the explanation.
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explainDataFrameAnalytics
public final CompletableFuture<ExplainDataFrameAnalyticsResponse> explainDataFrameAnalytics(Function<ExplainDataFrameAnalyticsRequest.Builder, ObjectBuilder<ExplainDataFrameAnalyticsRequest>> fn) Explains a data frame analytics config. This API provides explanations for a data frame analytics config that either exists already or one that has not been created yet. The following explanations are provided:- which fields are included or not in the analysis and why,
- how much memory is estimated to be required. The estimate can be used when deciding the appropriate value for model_memory_limit setting later on. If you have object fields or fields that are excluded via source filtering, they are not included in the explanation.
- Parameters:
fn- a function that initializes a builder to create theExplainDataFrameAnalyticsRequest- See Also:
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explainDataFrameAnalytics
Explains a data frame analytics config. This API provides explanations for a data frame analytics config that either exists already or one that has not been created yet. The following explanations are provided:- which fields are included or not in the analysis and why,
- how much memory is estimated to be required. The estimate can be used when deciding the appropriate value for model_memory_limit setting later on. If you have object fields or fields that are excluded via source filtering, they are not included in the explanation.
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flushJob
Forces any buffered data to be processed by the job. The flush jobs API is only applicable when sending data for analysis using the post data API. Depending on the content of the buffer, then it might additionally calculate new results. Both flush and close operations are similar, however the flush is more efficient if you are expecting to send more data for analysis. When flushing, the job remains open and is available to continue analyzing data. A close operation additionally prunes and persists the model state to disk and the job must be opened again before analyzing further data.- See Also:
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flushJob
public final CompletableFuture<FlushJobResponse> flushJob(Function<FlushJobRequest.Builder, ObjectBuilder<FlushJobRequest>> fn) Forces any buffered data to be processed by the job. The flush jobs API is only applicable when sending data for analysis using the post data API. Depending on the content of the buffer, then it might additionally calculate new results. Both flush and close operations are similar, however the flush is more efficient if you are expecting to send more data for analysis. When flushing, the job remains open and is available to continue analyzing data. A close operation additionally prunes and persists the model state to disk and the job must be opened again before analyzing further data.- Parameters:
fn- a function that initializes a builder to create theFlushJobRequest- See Also:
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forecast
Predicts the future behavior of a time series by using its historical behavior.Forecasts are not supported for jobs that perform population analysis; an error occurs if you try to create a forecast for a job that has an
over_field_namein its configuration.- See Also:
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forecast
public final CompletableFuture<ForecastResponse> forecast(Function<ForecastRequest.Builder, ObjectBuilder<ForecastRequest>> fn) Predicts the future behavior of a time series by using its historical behavior.Forecasts are not supported for jobs that perform population analysis; an error occurs if you try to create a forecast for a job that has an
over_field_namein its configuration.- Parameters:
fn- a function that initializes a builder to create theForecastRequest- See Also:
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getBuckets
Retrieves anomaly detection job results for one or more buckets. The API presents a chronological view of the records, grouped by bucket.- See Also:
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getBuckets
public final CompletableFuture<GetBucketsResponse> getBuckets(Function<GetBucketsRequest.Builder, ObjectBuilder<GetBucketsRequest>> fn) Retrieves anomaly detection job results for one or more buckets. The API presents a chronological view of the records, grouped by bucket.- Parameters:
fn- a function that initializes a builder to create theGetBucketsRequest- See Also:
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getCalendarEvents
public CompletableFuture<GetCalendarEventsResponse> getCalendarEvents(GetCalendarEventsRequest request) Retrieves information about the scheduled events in calendars.- See Also:
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getCalendarEvents
public final CompletableFuture<GetCalendarEventsResponse> getCalendarEvents(Function<GetCalendarEventsRequest.Builder, ObjectBuilder<GetCalendarEventsRequest>> fn) Retrieves information about the scheduled events in calendars.- Parameters:
fn- a function that initializes a builder to create theGetCalendarEventsRequest- See Also:
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getCalendars
Retrieves configuration information for calendars.- See Also:
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getCalendars
public final CompletableFuture<GetCalendarsResponse> getCalendars(Function<GetCalendarsRequest.Builder, ObjectBuilder<GetCalendarsRequest>> fn) Retrieves configuration information for calendars.- Parameters:
fn- a function that initializes a builder to create theGetCalendarsRequest- See Also:
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getCalendars
Retrieves configuration information for calendars.- See Also:
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getCategories
Retrieves anomaly detection job results for one or more categories.- See Also:
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getCategories
public final CompletableFuture<GetCategoriesResponse> getCategories(Function<GetCategoriesRequest.Builder, ObjectBuilder<GetCategoriesRequest>> fn) Retrieves anomaly detection job results for one or more categories.- Parameters:
fn- a function that initializes a builder to create theGetCategoriesRequest- See Also:
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getDataFrameAnalytics
public CompletableFuture<GetDataFrameAnalyticsResponse> getDataFrameAnalytics(GetDataFrameAnalyticsRequest request) Retrieves configuration information for data frame analytics jobs. You can get information for multiple data frame analytics jobs in a single API request by using a comma-separated list of data frame analytics jobs or a wildcard expression.- See Also:
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getDataFrameAnalytics
public final CompletableFuture<GetDataFrameAnalyticsResponse> getDataFrameAnalytics(Function<GetDataFrameAnalyticsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsRequest>> fn) Retrieves configuration information for data frame analytics jobs. You can get information for multiple data frame analytics jobs in a single API request by using a comma-separated list of data frame analytics jobs or a wildcard expression.- Parameters:
fn- a function that initializes a builder to create theGetDataFrameAnalyticsRequest- See Also:
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getDataFrameAnalytics
Retrieves configuration information for data frame analytics jobs. You can get information for multiple data frame analytics jobs in a single API request by using a comma-separated list of data frame analytics jobs or a wildcard expression.- See Also:
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getDataFrameAnalyticsStats
public CompletableFuture<GetDataFrameAnalyticsStatsResponse> getDataFrameAnalyticsStats(GetDataFrameAnalyticsStatsRequest request) Retrieves usage information for data frame analytics jobs.- See Also:
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getDataFrameAnalyticsStats
public final CompletableFuture<GetDataFrameAnalyticsStatsResponse> getDataFrameAnalyticsStats(Function<GetDataFrameAnalyticsStatsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsStatsRequest>> fn) Retrieves usage information for data frame analytics jobs.- Parameters:
fn- a function that initializes a builder to create theGetDataFrameAnalyticsStatsRequest- See Also:
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getDataFrameAnalyticsStats
Retrieves usage information for data frame analytics jobs.- See Also:
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getDatafeedStats
public CompletableFuture<GetDatafeedStatsResponse> getDatafeedStats(GetDatafeedStatsRequest request) Retrieves usage information for datafeeds. You can get statistics for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get statistics for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. If the datafeed is stopped, the only information you receive is thedatafeed_idand thestate. This API returns a maximum of 10,000 datafeeds.- See Also:
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getDatafeedStats
public final CompletableFuture<GetDatafeedStatsResponse> getDatafeedStats(Function<GetDatafeedStatsRequest.Builder, ObjectBuilder<GetDatafeedStatsRequest>> fn) Retrieves usage information for datafeeds. You can get statistics for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get statistics for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. If the datafeed is stopped, the only information you receive is thedatafeed_idand thestate. This API returns a maximum of 10,000 datafeeds.- Parameters:
fn- a function that initializes a builder to create theGetDatafeedStatsRequest- See Also:
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getDatafeedStats
Retrieves usage information for datafeeds. You can get statistics for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get statistics for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. If the datafeed is stopped, the only information you receive is thedatafeed_idand thestate. This API returns a maximum of 10,000 datafeeds.- See Also:
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getDatafeeds
Retrieves configuration information for datafeeds. You can get information for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get information for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. This API returns a maximum of 10,000 datafeeds.- See Also:
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getDatafeeds
public final CompletableFuture<GetDatafeedsResponse> getDatafeeds(Function<GetDatafeedsRequest.Builder, ObjectBuilder<GetDatafeedsRequest>> fn) Retrieves configuration information for datafeeds. You can get information for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get information for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. This API returns a maximum of 10,000 datafeeds.- Parameters:
fn- a function that initializes a builder to create theGetDatafeedsRequest- See Also:
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getDatafeeds
Retrieves configuration information for datafeeds. You can get information for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get information for all datafeeds by using_all, by specifying*as the<feed_id>, or by omitting the<feed_id>. This API returns a maximum of 10,000 datafeeds.- See Also:
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getFilters
Retrieves filters. You can get a single filter or all filters.- See Also:
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getFilters
public final CompletableFuture<GetFiltersResponse> getFilters(Function<GetFiltersRequest.Builder, ObjectBuilder<GetFiltersRequest>> fn) Retrieves filters. You can get a single filter or all filters.- Parameters:
fn- a function that initializes a builder to create theGetFiltersRequest- See Also:
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getFilters
Retrieves filters. You can get a single filter or all filters.- See Also:
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getInfluencers
Retrieves anomaly detection job results for one or more influencers. Influencers are the entities that have contributed to, or are to blame for, the anomalies. Influencer results are available only if aninfluencer_field_nameis specified in the job configuration.- See Also:
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getInfluencers
public final CompletableFuture<GetInfluencersResponse> getInfluencers(Function<GetInfluencersRequest.Builder, ObjectBuilder<GetInfluencersRequest>> fn) Retrieves anomaly detection job results for one or more influencers. Influencers are the entities that have contributed to, or are to blame for, the anomalies. Influencer results are available only if aninfluencer_field_nameis specified in the job configuration.- Parameters:
fn- a function that initializes a builder to create theGetInfluencersRequest- See Also:
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getJobStats
Retrieves usage information for anomaly detection jobs.- See Also:
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getJobStats
public final CompletableFuture<GetJobStatsResponse> getJobStats(Function<GetJobStatsRequest.Builder, ObjectBuilder<GetJobStatsRequest>> fn) Retrieves usage information for anomaly detection jobs.- Parameters:
fn- a function that initializes a builder to create theGetJobStatsRequest- See Also:
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getJobStats
Retrieves usage information for anomaly detection jobs.- See Also:
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getJobs
Retrieves configuration information for anomaly detection jobs. You can get information for multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can get information for all anomaly detection jobs by using_all, by specifying*as the<job_id>, or by omitting the<job_id>.- See Also:
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getJobs
public final CompletableFuture<GetJobsResponse> getJobs(Function<GetJobsRequest.Builder, ObjectBuilder<GetJobsRequest>> fn) Retrieves configuration information for anomaly detection jobs. You can get information for multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can get information for all anomaly detection jobs by using_all, by specifying*as the<job_id>, or by omitting the<job_id>.- Parameters:
fn- a function that initializes a builder to create theGetJobsRequest- See Also:
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getJobs
Retrieves configuration information for anomaly detection jobs. You can get information for multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can get information for all anomaly detection jobs by using_all, by specifying*as the<job_id>, or by omitting the<job_id>.- See Also:
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getMemoryStats
Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.- See Also:
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getMemoryStats
public final CompletableFuture<GetMemoryStatsResponse> getMemoryStats(Function<GetMemoryStatsRequest.Builder, ObjectBuilder<GetMemoryStatsRequest>> fn) Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.- Parameters:
fn- a function that initializes a builder to create theGetMemoryStatsRequest- See Also:
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getMemoryStats
Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.- See Also:
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getModelSnapshotUpgradeStats
public CompletableFuture<GetModelSnapshotUpgradeStatsResponse> getModelSnapshotUpgradeStats(GetModelSnapshotUpgradeStatsRequest request) Retrieves usage information for anomaly detection job model snapshot upgrades.- See Also:
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getModelSnapshotUpgradeStats
public final CompletableFuture<GetModelSnapshotUpgradeStatsResponse> getModelSnapshotUpgradeStats(Function<GetModelSnapshotUpgradeStatsRequest.Builder, ObjectBuilder<GetModelSnapshotUpgradeStatsRequest>> fn) Retrieves usage information for anomaly detection job model snapshot upgrades.- Parameters:
fn- a function that initializes a builder to create theGetModelSnapshotUpgradeStatsRequest- See Also:
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getModelSnapshots
public CompletableFuture<GetModelSnapshotsResponse> getModelSnapshots(GetModelSnapshotsRequest request) Retrieves information about model snapshots.- See Also:
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getModelSnapshots
public final CompletableFuture<GetModelSnapshotsResponse> getModelSnapshots(Function<GetModelSnapshotsRequest.Builder, ObjectBuilder<GetModelSnapshotsRequest>> fn) Retrieves information about model snapshots.- Parameters:
fn- a function that initializes a builder to create theGetModelSnapshotsRequest- See Also:
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getOverallBuckets
public CompletableFuture<GetOverallBucketsResponse> getOverallBuckets(GetOverallBucketsRequest request) Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.The
overall_scoreis calculated by combining the scores of all the buckets within the overall bucket span. First, the maximumanomaly_scoreper anomaly detection job in the overall bucket is calculated. Then thetop_nof those scores are averaged to result in theoverall_score. This means that you can fine-tune theoverall_scoreso that it is more or less sensitive to the number of jobs that detect an anomaly at the same time. For example, if you settop_nto1, theoverall_scoreis the maximum bucket score in the overall bucket. Alternatively, if you settop_nto the number of jobs, theoverall_scoreis high only when all jobs detect anomalies in that overall bucket. If you set thebucket_spanparameter (to a value greater than its default), theoverall_scoreis the maximumoverall_scoreof the overall buckets that have a span equal to the jobs' largest bucket span.- See Also:
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getOverallBuckets
public final CompletableFuture<GetOverallBucketsResponse> getOverallBuckets(Function<GetOverallBucketsRequest.Builder, ObjectBuilder<GetOverallBucketsRequest>> fn) Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.The
overall_scoreis calculated by combining the scores of all the buckets within the overall bucket span. First, the maximumanomaly_scoreper anomaly detection job in the overall bucket is calculated. Then thetop_nof those scores are averaged to result in theoverall_score. This means that you can fine-tune theoverall_scoreso that it is more or less sensitive to the number of jobs that detect an anomaly at the same time. For example, if you settop_nto1, theoverall_scoreis the maximum bucket score in the overall bucket. Alternatively, if you settop_nto the number of jobs, theoverall_scoreis high only when all jobs detect anomalies in that overall bucket. If you set thebucket_spanparameter (to a value greater than its default), theoverall_scoreis the maximumoverall_scoreof the overall buckets that have a span equal to the jobs' largest bucket span.- Parameters:
fn- a function that initializes a builder to create theGetOverallBucketsRequest- See Also:
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getRecords
Retrieves anomaly records for an anomaly detection job. Records contain the detailed analytical results. They describe the anomalous activity that has been identified in the input data based on the detector configuration. There can be many anomaly records depending on the characteristics and size of the input data. In practice, there are often too many to be able to manually process them. The machine learning features therefore perform a sophisticated aggregation of the anomaly records into buckets. The number of record results depends on the number of anomalies found in each bucket, which relates to the number of time series being modeled and the number of detectors.- See Also:
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getRecords
public final CompletableFuture<GetRecordsResponse> getRecords(Function<GetRecordsRequest.Builder, ObjectBuilder<GetRecordsRequest>> fn) Retrieves anomaly records for an anomaly detection job. Records contain the detailed analytical results. They describe the anomalous activity that has been identified in the input data based on the detector configuration. There can be many anomaly records depending on the characteristics and size of the input data. In practice, there are often too many to be able to manually process them. The machine learning features therefore perform a sophisticated aggregation of the anomaly records into buckets. The number of record results depends on the number of anomalies found in each bucket, which relates to the number of time series being modeled and the number of detectors.- Parameters:
fn- a function that initializes a builder to create theGetRecordsRequest- See Also:
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getTrainedModels
public CompletableFuture<GetTrainedModelsResponse> getTrainedModels(GetTrainedModelsRequest request) Retrieves configuration information for a trained model.- See Also:
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getTrainedModels
public final CompletableFuture<GetTrainedModelsResponse> getTrainedModels(Function<GetTrainedModelsRequest.Builder, ObjectBuilder<GetTrainedModelsRequest>> fn) Retrieves configuration information for a trained model.- Parameters:
fn- a function that initializes a builder to create theGetTrainedModelsRequest- See Also:
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getTrainedModels
Retrieves configuration information for a trained model.- See Also:
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getTrainedModelsStats
public CompletableFuture<GetTrainedModelsStatsResponse> getTrainedModelsStats(GetTrainedModelsStatsRequest request) Retrieves usage information for trained models. You can get usage information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.- See Also:
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getTrainedModelsStats
public final CompletableFuture<GetTrainedModelsStatsResponse> getTrainedModelsStats(Function<GetTrainedModelsStatsRequest.Builder, ObjectBuilder<GetTrainedModelsStatsRequest>> fn) Retrieves usage information for trained models. You can get usage information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.- Parameters:
fn- a function that initializes a builder to create theGetTrainedModelsStatsRequest- See Also:
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getTrainedModelsStats
Retrieves usage information for trained models. You can get usage information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.- See Also:
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inferTrainedModel
public CompletableFuture<InferTrainedModelResponse> inferTrainedModel(InferTrainedModelRequest request) Evaluates a trained model.- See Also:
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inferTrainedModel
public final CompletableFuture<InferTrainedModelResponse> inferTrainedModel(Function<InferTrainedModelRequest.Builder, ObjectBuilder<InferTrainedModelRequest>> fn) Evaluates a trained model.- Parameters:
fn- a function that initializes a builder to create theInferTrainedModelRequest- See Also:
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info
Returns defaults and limits used by machine learning. This endpoint is designed to be used by a user interface that needs to fully understand machine learning configurations where some options are not specified, meaning that the defaults should be used. This endpoint may be used to find out what those defaults are. It also provides information about the maximum size of machine learning jobs that could run in the current cluster configuration.- See Also:
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openJob
Opens one or more anomaly detection jobs. An anomaly detection job must be opened in order for it to be ready to receive and analyze data. It can be opened and closed multiple times throughout its lifecycle. When you open a new job, it starts with an empty model. When you open an existing job, the most recent model state is automatically loaded. The job is ready to resume its analysis from where it left off, once new data is received.- See Also:
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openJob
public final CompletableFuture<OpenJobResponse> openJob(Function<OpenJobRequest.Builder, ObjectBuilder<OpenJobRequest>> fn) Opens one or more anomaly detection jobs. An anomaly detection job must be opened in order for it to be ready to receive and analyze data. It can be opened and closed multiple times throughout its lifecycle. When you open a new job, it starts with an empty model. When you open an existing job, the most recent model state is automatically loaded. The job is ready to resume its analysis from where it left off, once new data is received.- Parameters:
fn- a function that initializes a builder to create theOpenJobRequest- See Also:
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postCalendarEvents
public CompletableFuture<PostCalendarEventsResponse> postCalendarEvents(PostCalendarEventsRequest request) Adds scheduled events to a calendar.- See Also:
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postCalendarEvents
public final CompletableFuture<PostCalendarEventsResponse> postCalendarEvents(Function<PostCalendarEventsRequest.Builder, ObjectBuilder<PostCalendarEventsRequest>> fn) Adds scheduled events to a calendar.- Parameters:
fn- a function that initializes a builder to create thePostCalendarEventsRequest- See Also:
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postData
Sends data to an anomaly detection job for analysis.IMPORTANT: For each job, data can be accepted from only a single connection at a time. It is not currently possible to post data to multiple jobs using wildcards or a comma-separated list.
- See Also:
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postData
public final <TData> CompletableFuture<PostDataResponse> postData(Function<PostDataRequest.Builder<TData>, ObjectBuilder<PostDataRequest<TData>>> fn) Sends data to an anomaly detection job for analysis.IMPORTANT: For each job, data can be accepted from only a single connection at a time. It is not currently possible to post data to multiple jobs using wildcards or a comma-separated list.
- Parameters:
fn- a function that initializes a builder to create thePostDataRequest- See Also:
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previewDataFrameAnalytics
public CompletableFuture<PreviewDataFrameAnalyticsResponse> previewDataFrameAnalytics(PreviewDataFrameAnalyticsRequest request) Previews the extracted features used by a data frame analytics config.- See Also:
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previewDataFrameAnalytics
public final CompletableFuture<PreviewDataFrameAnalyticsResponse> previewDataFrameAnalytics(Function<PreviewDataFrameAnalyticsRequest.Builder, ObjectBuilder<PreviewDataFrameAnalyticsRequest>> fn) Previews the extracted features used by a data frame analytics config.- Parameters:
fn- a function that initializes a builder to create thePreviewDataFrameAnalyticsRequest- See Also:
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previewDataFrameAnalytics
Previews the extracted features used by a data frame analytics config.- See Also:
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previewDatafeed
public <TDocument> CompletableFuture<PreviewDatafeedResponse<TDocument>> previewDatafeed(PreviewDatafeedRequest request, Class<TDocument> tDocumentClass) Previews a datafeed. This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.- See Also:
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previewDatafeed
public final <TDocument> CompletableFuture<PreviewDatafeedResponse<TDocument>> previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Class<TDocument> tDocumentClass) Previews a datafeed. This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.- Parameters:
fn- a function that initializes a builder to create thePreviewDatafeedRequest- See Also:
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previewDatafeed
public <TDocument> CompletableFuture<PreviewDatafeedResponse<TDocument>> previewDatafeed(PreviewDatafeedRequest request, Type tDocumentType) Previews a datafeed. This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.- See Also:
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previewDatafeed
public final <TDocument> CompletableFuture<PreviewDatafeedResponse<TDocument>> previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Type tDocumentType) Previews a datafeed. This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.- Parameters:
fn- a function that initializes a builder to create thePreviewDatafeedRequest- See Also:
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putCalendar
Creates a calendar.- See Also:
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putCalendar
public final CompletableFuture<PutCalendarResponse> putCalendar(Function<PutCalendarRequest.Builder, ObjectBuilder<PutCalendarRequest>> fn) Creates a calendar.- Parameters:
fn- a function that initializes a builder to create thePutCalendarRequest- See Also:
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putCalendarJob
Adds an anomaly detection job to a calendar.- See Also:
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putCalendarJob
public final CompletableFuture<PutCalendarJobResponse> putCalendarJob(Function<PutCalendarJobRequest.Builder, ObjectBuilder<PutCalendarJobRequest>> fn) Adds an anomaly detection job to a calendar.- Parameters:
fn- a function that initializes a builder to create thePutCalendarJobRequest- See Also:
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putDataFrameAnalytics
public CompletableFuture<PutDataFrameAnalyticsResponse> putDataFrameAnalytics(PutDataFrameAnalyticsRequest request) Instantiates a data frame analytics job. This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index.- See Also:
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putDataFrameAnalytics
public final CompletableFuture<PutDataFrameAnalyticsResponse> putDataFrameAnalytics(Function<PutDataFrameAnalyticsRequest.Builder, ObjectBuilder<PutDataFrameAnalyticsRequest>> fn) Instantiates a data frame analytics job. This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index.- Parameters:
fn- a function that initializes a builder to create thePutDataFrameAnalyticsRequest- See Also:
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putDatafeed
Instantiates a datafeed. Datafeeds retrieve data from Elasticsearch for analysis by an anomaly detection job. You can associate only one datafeed with each anomaly detection job. The datafeed contains a query that runs at a defined interval (frequency). If you are concerned about delayed data, you can add a delay (query_delay') at each interval. When Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead. You must use Kibana, this API, or the create anomaly detection jobs API to create a datafeed. Do not add a datafeed directly to the.ml-configindex. Do not give userswriteprivileges on the.ml-config` index.- See Also:
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putDatafeed
public final CompletableFuture<PutDatafeedResponse> putDatafeed(Function<PutDatafeedRequest.Builder, ObjectBuilder<PutDatafeedRequest>> fn) Instantiates a datafeed. Datafeeds retrieve data from Elasticsearch for analysis by an anomaly detection job. You can associate only one datafeed with each anomaly detection job. The datafeed contains a query that runs at a defined interval (frequency). If you are concerned about delayed data, you can add a delay (query_delay') at each interval. When Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead. You must use Kibana, this API, or the create anomaly detection jobs API to create a datafeed. Do not add a datafeed directly to the.ml-configindex. Do not give userswriteprivileges on the.ml-config` index.- Parameters:
fn- a function that initializes a builder to create thePutDatafeedRequest- See Also:
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putFilter
Instantiates a filter. A filter contains a list of strings. It can be used by one or more anomaly detection jobs. Specifically, filters are referenced in thecustom_rulesproperty of detector configuration objects.- See Also:
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putFilter
public final CompletableFuture<PutFilterResponse> putFilter(Function<PutFilterRequest.Builder, ObjectBuilder<PutFilterRequest>> fn) Instantiates a filter. A filter contains a list of strings. It can be used by one or more anomaly detection jobs. Specifically, filters are referenced in thecustom_rulesproperty of detector configuration objects.- Parameters:
fn- a function that initializes a builder to create thePutFilterRequest- See Also:
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putJob
Instantiates an anomaly detection job. If you include adatafeed_config, you must have read index privileges on the source index.- See Also:
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putJob
public final CompletableFuture<PutJobResponse> putJob(Function<PutJobRequest.Builder, ObjectBuilder<PutJobRequest>> fn) Instantiates an anomaly detection job. If you include adatafeed_config, you must have read index privileges on the source index.- Parameters:
fn- a function that initializes a builder to create thePutJobRequest- See Also:
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putTrainedModel
Enables you to supply a trained model that is not created by data frame analytics.- See Also:
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putTrainedModel
public final CompletableFuture<PutTrainedModelResponse> putTrainedModel(Function<PutTrainedModelRequest.Builder, ObjectBuilder<PutTrainedModelRequest>> fn) Enables you to supply a trained model that is not created by data frame analytics.- Parameters:
fn- a function that initializes a builder to create thePutTrainedModelRequest- See Also:
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putTrainedModelAlias
public CompletableFuture<PutTrainedModelAliasResponse> putTrainedModelAlias(PutTrainedModelAliasRequest request) Creates or updates a trained model alias. A trained model alias is a logical name used to reference a single trained model. You can use aliases instead of trained model identifiers to make it easier to reference your models. For example, you can use aliases in inference aggregations and processors. An alias must be unique and refer to only a single trained model. However, you can have multiple aliases for each trained model. If you use this API to update an alias such that it references a different trained model ID and the model uses a different type of data frame analytics, an error occurs. For example, this situation occurs if you have a trained model for regression analysis and a trained model for classification analysis; you cannot reassign an alias from one type of trained model to another. If you use this API to update an alias and there are very few input fields in common between the old and new trained models for the model alias, the API returns a warning.- See Also:
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putTrainedModelAlias
public final CompletableFuture<PutTrainedModelAliasResponse> putTrainedModelAlias(Function<PutTrainedModelAliasRequest.Builder, ObjectBuilder<PutTrainedModelAliasRequest>> fn) Creates or updates a trained model alias. A trained model alias is a logical name used to reference a single trained model. You can use aliases instead of trained model identifiers to make it easier to reference your models. For example, you can use aliases in inference aggregations and processors. An alias must be unique and refer to only a single trained model. However, you can have multiple aliases for each trained model. If you use this API to update an alias such that it references a different trained model ID and the model uses a different type of data frame analytics, an error occurs. For example, this situation occurs if you have a trained model for regression analysis and a trained model for classification analysis; you cannot reassign an alias from one type of trained model to another. If you use this API to update an alias and there are very few input fields in common between the old and new trained models for the model alias, the API returns a warning.- Parameters:
fn- a function that initializes a builder to create thePutTrainedModelAliasRequest- See Also:
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putTrainedModelDefinitionPart
public CompletableFuture<PutTrainedModelDefinitionPartResponse> putTrainedModelDefinitionPart(PutTrainedModelDefinitionPartRequest request) Creates part of a trained model definition.- See Also:
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putTrainedModelDefinitionPart
public final CompletableFuture<PutTrainedModelDefinitionPartResponse> putTrainedModelDefinitionPart(Function<PutTrainedModelDefinitionPartRequest.Builder, ObjectBuilder<PutTrainedModelDefinitionPartRequest>> fn) Creates part of a trained model definition.- Parameters:
fn- a function that initializes a builder to create thePutTrainedModelDefinitionPartRequest- See Also:
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putTrainedModelVocabulary
public CompletableFuture<PutTrainedModelVocabularyResponse> putTrainedModelVocabulary(PutTrainedModelVocabularyRequest request) Creates a trained model vocabulary. This API is supported only for natural language processing (NLP) models. The vocabulary is stored in the index as described ininference_config.*.vocabularyof the trained model definition.- See Also:
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putTrainedModelVocabulary
public final CompletableFuture<PutTrainedModelVocabularyResponse> putTrainedModelVocabulary(Function<PutTrainedModelVocabularyRequest.Builder, ObjectBuilder<PutTrainedModelVocabularyRequest>> fn) Creates a trained model vocabulary. This API is supported only for natural language processing (NLP) models. The vocabulary is stored in the index as described ininference_config.*.vocabularyof the trained model definition.- Parameters:
fn- a function that initializes a builder to create thePutTrainedModelVocabularyRequest- See Also:
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resetJob
Resets an anomaly detection job. All model state and results are deleted. The job is ready to start over as if it had just been created. It is not currently possible to reset multiple jobs using wildcards or a comma separated list.- See Also:
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resetJob
public final CompletableFuture<ResetJobResponse> resetJob(Function<ResetJobRequest.Builder, ObjectBuilder<ResetJobRequest>> fn) Resets an anomaly detection job. All model state and results are deleted. The job is ready to start over as if it had just been created. It is not currently possible to reset multiple jobs using wildcards or a comma separated list.- Parameters:
fn- a function that initializes a builder to create theResetJobRequest- See Also:
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revertModelSnapshot
public CompletableFuture<RevertModelSnapshotResponse> revertModelSnapshot(RevertModelSnapshotRequest request) Reverts to a specific snapshot. The machine learning features react quickly to anomalous input, learning new behaviors in data. Highly anomalous input increases the variance in the models whilst the system learns whether this is a new step-change in behavior or a one-off event. In the case where this anomalous input is known to be a one-off, then it might be appropriate to reset the model state to a time before this event. For example, you might consider reverting to a saved snapshot after Black Friday or a critical system failure.- See Also:
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revertModelSnapshot
public final CompletableFuture<RevertModelSnapshotResponse> revertModelSnapshot(Function<RevertModelSnapshotRequest.Builder, ObjectBuilder<RevertModelSnapshotRequest>> fn) Reverts to a specific snapshot. The machine learning features react quickly to anomalous input, learning new behaviors in data. Highly anomalous input increases the variance in the models whilst the system learns whether this is a new step-change in behavior or a one-off event. In the case where this anomalous input is known to be a one-off, then it might be appropriate to reset the model state to a time before this event. For example, you might consider reverting to a saved snapshot after Black Friday or a critical system failure.- Parameters:
fn- a function that initializes a builder to create theRevertModelSnapshotRequest- See Also:
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setUpgradeMode
Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade. When upgrading your cluster, in some circumstances you must restart your nodes and reindex your machine learning indices. In those circumstances, there must be no machine learning jobs running. You can close the machine learning jobs, do the upgrade, then open all the jobs again. Alternatively, you can use this API to temporarily halt tasks associated with the jobs and datafeeds and prevent new jobs from opening. You can also use this API during upgrades that do not require you to reindex your machine learning indices, though stopping jobs is not a requirement in that case. You can see the current value for the upgrade_mode setting by using the get machine learning info API.- See Also:
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setUpgradeMode
public final CompletableFuture<SetUpgradeModeResponse> setUpgradeMode(Function<SetUpgradeModeRequest.Builder, ObjectBuilder<SetUpgradeModeRequest>> fn) Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade. When upgrading your cluster, in some circumstances you must restart your nodes and reindex your machine learning indices. In those circumstances, there must be no machine learning jobs running. You can close the machine learning jobs, do the upgrade, then open all the jobs again. Alternatively, you can use this API to temporarily halt tasks associated with the jobs and datafeeds and prevent new jobs from opening. You can also use this API during upgrades that do not require you to reindex your machine learning indices, though stopping jobs is not a requirement in that case. You can see the current value for the upgrade_mode setting by using the get machine learning info API.- Parameters:
fn- a function that initializes a builder to create theSetUpgradeModeRequest- See Also:
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setUpgradeMode
Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade. When upgrading your cluster, in some circumstances you must restart your nodes and reindex your machine learning indices. In those circumstances, there must be no machine learning jobs running. You can close the machine learning jobs, do the upgrade, then open all the jobs again. Alternatively, you can use this API to temporarily halt tasks associated with the jobs and datafeeds and prevent new jobs from opening. You can also use this API during upgrades that do not require you to reindex your machine learning indices, though stopping jobs is not a requirement in that case. You can see the current value for the upgrade_mode setting by using the get machine learning info API.- See Also:
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startDataFrameAnalytics
public CompletableFuture<StartDataFrameAnalyticsResponse> startDataFrameAnalytics(StartDataFrameAnalyticsRequest request) Starts a data frame analytics job. A data frame analytics job can be started and stopped multiple times throughout its lifecycle. If the destination index does not exist, it is created automatically the first time you start the data frame analytics job. Theindex.number_of_shardsandindex.number_of_replicassettings for the destination index are copied from the source index. If there are multiple source indices, the destination index copies the highest setting values. The mappings for the destination index are also copied from the source indices. If there are any mapping conflicts, the job fails to start. If the destination index exists, it is used as is. You can therefore set up the destination index in advance with custom settings and mappings.- See Also:
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startDataFrameAnalytics
public final CompletableFuture<StartDataFrameAnalyticsResponse> startDataFrameAnalytics(Function<StartDataFrameAnalyticsRequest.Builder, ObjectBuilder<StartDataFrameAnalyticsRequest>> fn) Starts a data frame analytics job. A data frame analytics job can be started and stopped multiple times throughout its lifecycle. If the destination index does not exist, it is created automatically the first time you start the data frame analytics job. Theindex.number_of_shardsandindex.number_of_replicassettings for the destination index are copied from the source index. If there are multiple source indices, the destination index copies the highest setting values. The mappings for the destination index are also copied from the source indices. If there are any mapping conflicts, the job fails to start. If the destination index exists, it is used as is. You can therefore set up the destination index in advance with custom settings and mappings.- Parameters:
fn- a function that initializes a builder to create theStartDataFrameAnalyticsRequest- See Also:
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startDatafeed
Starts one or more datafeeds.A datafeed must be started in order to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.
Before you can start a datafeed, the anomaly detection job must be open. Otherwise, an error occurs.
If you restart a stopped datafeed, it continues processing input data from the next millisecond after it was stopped. If new data was indexed for that exact millisecond between stopping and starting, it will be ignored.
When Elasticsearch security features are enabled, your datafeed remembers which roles the last user to create or update it had at the time of creation or update and runs the query using those same roles. If you provided secondary authorization headers when you created or updated the datafeed, those credentials are used instead.
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startDatafeed
public final CompletableFuture<StartDatafeedResponse> startDatafeed(Function<StartDatafeedRequest.Builder, ObjectBuilder<StartDatafeedRequest>> fn) Starts one or more datafeeds.A datafeed must be started in order to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.
Before you can start a datafeed, the anomaly detection job must be open. Otherwise, an error occurs.
If you restart a stopped datafeed, it continues processing input data from the next millisecond after it was stopped. If new data was indexed for that exact millisecond between stopping and starting, it will be ignored.
When Elasticsearch security features are enabled, your datafeed remembers which roles the last user to create or update it had at the time of creation or update and runs the query using those same roles. If you provided secondary authorization headers when you created or updated the datafeed, those credentials are used instead.
- Parameters:
fn- a function that initializes a builder to create theStartDatafeedRequest- See Also:
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startTrainedModelDeployment
public CompletableFuture<StartTrainedModelDeploymentResponse> startTrainedModelDeployment(StartTrainedModelDeploymentRequest request) Starts a trained model deployment, which allocates the model to every machine learning node.- See Also:
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startTrainedModelDeployment
public final CompletableFuture<StartTrainedModelDeploymentResponse> startTrainedModelDeployment(Function<StartTrainedModelDeploymentRequest.Builder, ObjectBuilder<StartTrainedModelDeploymentRequest>> fn) Starts a trained model deployment, which allocates the model to every machine learning node.- Parameters:
fn- a function that initializes a builder to create theStartTrainedModelDeploymentRequest- See Also:
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stopDataFrameAnalytics
public CompletableFuture<StopDataFrameAnalyticsResponse> stopDataFrameAnalytics(StopDataFrameAnalyticsRequest request) Stops one or more data frame analytics jobs. A data frame analytics job can be started and stopped multiple times throughout its lifecycle.- See Also:
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stopDataFrameAnalytics
public final CompletableFuture<StopDataFrameAnalyticsResponse> stopDataFrameAnalytics(Function<StopDataFrameAnalyticsRequest.Builder, ObjectBuilder<StopDataFrameAnalyticsRequest>> fn) Stops one or more data frame analytics jobs. A data frame analytics job can be started and stopped multiple times throughout its lifecycle.- Parameters:
fn- a function that initializes a builder to create theStopDataFrameAnalyticsRequest- See Also:
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stopDatafeed
Stops one or more datafeeds. A datafeed that is stopped ceases to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.- See Also:
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stopDatafeed
public final CompletableFuture<StopDatafeedResponse> stopDatafeed(Function<StopDatafeedRequest.Builder, ObjectBuilder<StopDatafeedRequest>> fn) Stops one or more datafeeds. A datafeed that is stopped ceases to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.- Parameters:
fn- a function that initializes a builder to create theStopDatafeedRequest- See Also:
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stopTrainedModelDeployment
public CompletableFuture<StopTrainedModelDeploymentResponse> stopTrainedModelDeployment(StopTrainedModelDeploymentRequest request) Stops a trained model deployment.- See Also:
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stopTrainedModelDeployment
public final CompletableFuture<StopTrainedModelDeploymentResponse> stopTrainedModelDeployment(Function<StopTrainedModelDeploymentRequest.Builder, ObjectBuilder<StopTrainedModelDeploymentRequest>> fn) Stops a trained model deployment.- Parameters:
fn- a function that initializes a builder to create theStopTrainedModelDeploymentRequest- See Also:
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updateDataFrameAnalytics
public CompletableFuture<UpdateDataFrameAnalyticsResponse> updateDataFrameAnalytics(UpdateDataFrameAnalyticsRequest request) Updates an existing data frame analytics job.- See Also:
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updateDataFrameAnalytics
public final CompletableFuture<UpdateDataFrameAnalyticsResponse> updateDataFrameAnalytics(Function<UpdateDataFrameAnalyticsRequest.Builder, ObjectBuilder<UpdateDataFrameAnalyticsRequest>> fn) Updates an existing data frame analytics job.- Parameters:
fn- a function that initializes a builder to create theUpdateDataFrameAnalyticsRequest- See Also:
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updateDatafeed
Updates the properties of a datafeed. You must stop and start the datafeed for the changes to be applied. When Elasticsearch security features are enabled, your datafeed remembers which roles the user who updated it had at the time of the update and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.- See Also:
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updateDatafeed
public final CompletableFuture<UpdateDatafeedResponse> updateDatafeed(Function<UpdateDatafeedRequest.Builder, ObjectBuilder<UpdateDatafeedRequest>> fn) Updates the properties of a datafeed. You must stop and start the datafeed for the changes to be applied. When Elasticsearch security features are enabled, your datafeed remembers which roles the user who updated it had at the time of the update and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.- Parameters:
fn- a function that initializes a builder to create theUpdateDatafeedRequest- See Also:
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updateFilter
Updates the description of a filter, adds items, or removes items from the list.- See Also:
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updateFilter
public final CompletableFuture<UpdateFilterResponse> updateFilter(Function<UpdateFilterRequest.Builder, ObjectBuilder<UpdateFilterRequest>> fn) Updates the description of a filter, adds items, or removes items from the list.- Parameters:
fn- a function that initializes a builder to create theUpdateFilterRequest- See Also:
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updateJob
Updates certain properties of an anomaly detection job.- See Also:
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updateJob
public final CompletableFuture<UpdateJobResponse> updateJob(Function<UpdateJobRequest.Builder, ObjectBuilder<UpdateJobRequest>> fn) Updates certain properties of an anomaly detection job.- Parameters:
fn- a function that initializes a builder to create theUpdateJobRequest- See Also:
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updateModelSnapshot
public CompletableFuture<UpdateModelSnapshotResponse> updateModelSnapshot(UpdateModelSnapshotRequest request) Updates certain properties of a snapshot.- See Also:
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updateModelSnapshot
public final CompletableFuture<UpdateModelSnapshotResponse> updateModelSnapshot(Function<UpdateModelSnapshotRequest.Builder, ObjectBuilder<UpdateModelSnapshotRequest>> fn) Updates certain properties of a snapshot.- Parameters:
fn- a function that initializes a builder to create theUpdateModelSnapshotRequest- See Also:
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updateTrainedModelDeployment
public CompletableFuture<UpdateTrainedModelDeploymentResponse> updateTrainedModelDeployment(UpdateTrainedModelDeploymentRequest request) Starts a trained model deployment, which allocates the model to every machine learning node.- See Also:
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updateTrainedModelDeployment
public final CompletableFuture<UpdateTrainedModelDeploymentResponse> updateTrainedModelDeployment(Function<UpdateTrainedModelDeploymentRequest.Builder, ObjectBuilder<UpdateTrainedModelDeploymentRequest>> fn) Starts a trained model deployment, which allocates the model to every machine learning node.- Parameters:
fn- a function that initializes a builder to create theUpdateTrainedModelDeploymentRequest- See Also:
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upgradeJobSnapshot
public CompletableFuture<UpgradeJobSnapshotResponse> upgradeJobSnapshot(UpgradeJobSnapshotRequest request) Upgrades an anomaly detection model snapshot to the latest major version. Over time, older snapshot formats are deprecated and removed. Anomaly detection jobs support only snapshots that are from the current or previous major version. This API provides a means to upgrade a snapshot to the current major version. This aids in preparing the cluster for an upgrade to the next major version. Only one snapshot per anomaly detection job can be upgraded at a time and the upgraded snapshot cannot be the current snapshot of the anomaly detection job.- See Also:
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upgradeJobSnapshot
public final CompletableFuture<UpgradeJobSnapshotResponse> upgradeJobSnapshot(Function<UpgradeJobSnapshotRequest.Builder, ObjectBuilder<UpgradeJobSnapshotRequest>> fn) Upgrades an anomaly detection model snapshot to the latest major version. Over time, older snapshot formats are deprecated and removed. Anomaly detection jobs support only snapshots that are from the current or previous major version. This API provides a means to upgrade a snapshot to the current major version. This aids in preparing the cluster for an upgrade to the next major version. Only one snapshot per anomaly detection job can be upgraded at a time and the upgraded snapshot cannot be the current snapshot of the anomaly detection job.- Parameters:
fn- a function that initializes a builder to create theUpgradeJobSnapshotRequest- See Also:
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validate
Validates an anomaly detection job.- See Also:
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validate
public final CompletableFuture<ValidateResponse> validate(Function<ValidateRequest.Builder, ObjectBuilder<ValidateRequest>> fn) Validates an anomaly detection job.- Parameters:
fn- a function that initializes a builder to create theValidateRequest- See Also:
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validate
Validates an anomaly detection job.- See Also:
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validateDetector
public CompletableFuture<ValidateDetectorResponse> validateDetector(ValidateDetectorRequest request) Validates an anomaly detection detector.- See Also:
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validateDetector
public final CompletableFuture<ValidateDetectorResponse> validateDetector(Function<ValidateDetectorRequest.Builder, ObjectBuilder<ValidateDetectorRequest>> fn) Validates an anomaly detection detector.- Parameters:
fn- a function that initializes a builder to create theValidateDetectorRequest- See Also:
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validateDetector
Validates an anomaly detection detector.- See Also:
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