Class ElasticsearchMlAsyncClient
java.lang.Object
co.elastic.clients.ApiClient<ElasticsearchTransport,ElasticsearchMlAsyncClient>
co.elastic.clients.elasticsearch.ml.ElasticsearchMlAsyncClient
- All Implemented Interfaces:
Closeable,AutoCloseable
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 TypeMethodDescriptionClear trained model deployment cache.clearTrainedModelDeploymentCache(Function<ClearTrainedModelDeploymentCacheRequest.Builder, ObjectBuilder<ClearTrainedModelDeploymentCacheRequest>> fn) Clear trained model deployment cache.closeJob(CloseJobRequest request) Close anomaly detection jobs.Close anomaly detection jobs.deleteCalendar(DeleteCalendarRequest request) Delete a calendar.Delete a calendar.Delete events from a calendar.deleteCalendarEvent(Function<DeleteCalendarEventRequest.Builder, ObjectBuilder<DeleteCalendarEventRequest>> fn) Delete events from a calendar.Delete anomaly jobs from a calendar.deleteCalendarJob(Function<DeleteCalendarJobRequest.Builder, ObjectBuilder<DeleteCalendarJobRequest>> fn) Delete anomaly jobs from a calendar.deleteDatafeed(DeleteDatafeedRequest request) Delete a datafeed.Delete a datafeed.Delete a data frame analytics job.deleteDataFrameAnalytics(Function<DeleteDataFrameAnalyticsRequest.Builder, ObjectBuilder<DeleteDataFrameAnalyticsRequest>> fn) Delete a data frame analytics job.Delete expired ML data.Delete expired ML data.deleteExpiredData(Function<DeleteExpiredDataRequest.Builder, ObjectBuilder<DeleteExpiredDataRequest>> fn) Delete expired ML data.deleteFilter(DeleteFilterRequest request) Delete a filter.Delete a filter.deleteForecast(DeleteForecastRequest request) Delete forecasts from a job.Delete forecasts from a job.deleteJob(DeleteJobRequest request) Delete an anomaly detection job.Delete an anomaly detection job.Delete a model snapshot.deleteModelSnapshot(Function<DeleteModelSnapshotRequest.Builder, ObjectBuilder<DeleteModelSnapshotRequest>> fn) Delete a model snapshot.Delete an unreferenced trained model.deleteTrainedModel(Function<DeleteTrainedModelRequest.Builder, ObjectBuilder<DeleteTrainedModelRequest>> fn) Delete an unreferenced trained model.Delete a trained model alias.deleteTrainedModelAlias(Function<DeleteTrainedModelAliasRequest.Builder, ObjectBuilder<DeleteTrainedModelAliasRequest>> fn) Delete a trained model alias.Estimate job model memory usage.Estimate job model memory usage.estimateModelMemory(Function<EstimateModelMemoryRequest.Builder, ObjectBuilder<EstimateModelMemoryRequest>> fn) Estimate job model memory usage.Evaluate data frame analytics.evaluateDataFrame(Function<EvaluateDataFrameRequest.Builder, ObjectBuilder<EvaluateDataFrameRequest>> fn) Evaluate data frame analytics.Explain data frame analytics config.Explain data frame analytics config.explainDataFrameAnalytics(Function<ExplainDataFrameAnalyticsRequest.Builder, ObjectBuilder<ExplainDataFrameAnalyticsRequest>> fn) Explain data frame analytics config.flushJob(FlushJobRequest request) Force buffered data to be processed.Force buffered data to be processed.forecast(ForecastRequest request) Predict future behavior of a time series.Predict future behavior of a time series.getBuckets(GetBucketsRequest request) Get anomaly detection job results for buckets.Get anomaly detection job results for buckets.Get info about events in calendars.getCalendarEvents(Function<GetCalendarEventsRequest.Builder, ObjectBuilder<GetCalendarEventsRequest>> fn) Get info about events in calendars.Get calendar configuration info.getCalendars(GetCalendarsRequest request) Get calendar configuration info.Get calendar configuration info.getCategories(GetCategoriesRequest request) Get anomaly detection job results for categories.Get anomaly detection job results for categories.Get datafeeds configuration info.getDatafeeds(GetDatafeedsRequest request) Get datafeeds configuration info.Get datafeeds configuration info.Get datafeeds usage info.getDatafeedStats(GetDatafeedStatsRequest request) Get datafeeds usage info.getDatafeedStats(Function<GetDatafeedStatsRequest.Builder, ObjectBuilder<GetDatafeedStatsRequest>> fn) Get datafeeds usage info.Get data frame analytics job configuration info.Get data frame analytics job configuration info.getDataFrameAnalytics(Function<GetDataFrameAnalyticsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsRequest>> fn) Get data frame analytics job configuration info.Get data frame analytics jobs usage info.Get data frame analytics jobs usage info.getDataFrameAnalyticsStats(Function<GetDataFrameAnalyticsStatsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsStatsRequest>> fn) Get data frame analytics jobs usage info.Get filters.getFilters(GetFiltersRequest request) Get filters.Get filters.getInfluencers(GetInfluencersRequest request) Get anomaly detection job results for influencers.Get anomaly detection job results for influencers.getJobs()Get anomaly detection jobs configuration info.getJobs(GetJobsRequest request) Get anomaly detection jobs configuration info.final CompletableFuture<GetJobsResponse>Get anomaly detection jobs configuration info.Get anomaly detection jobs usage info.getJobStats(GetJobStatsRequest request) Get anomaly detection jobs usage info.Get anomaly detection jobs usage info.Get machine learning memory usage info.getMemoryStats(GetMemoryStatsRequest request) Get machine learning memory usage info.Get machine learning memory usage info.Get model snapshots info.getModelSnapshots(Function<GetModelSnapshotsRequest.Builder, ObjectBuilder<GetModelSnapshotsRequest>> fn) Get model snapshots info.Get anomaly detection job model snapshot upgrade usage info.getModelSnapshotUpgradeStats(Function<GetModelSnapshotUpgradeStatsRequest.Builder, ObjectBuilder<GetModelSnapshotUpgradeStatsRequest>> fn) Get anomaly detection job model snapshot upgrade usage info.Get overall bucket results.getOverallBuckets(Function<GetOverallBucketsRequest.Builder, ObjectBuilder<GetOverallBucketsRequest>> fn) Get overall bucket results.getRecords(GetRecordsRequest request) Get anomaly records for an anomaly detection job.Get anomaly records for an anomaly detection job.Get trained model configuration info.getTrainedModels(GetTrainedModelsRequest request) Get trained model configuration info.getTrainedModels(Function<GetTrainedModelsRequest.Builder, ObjectBuilder<GetTrainedModelsRequest>> fn) Get trained model configuration info.Get trained models usage info.Get trained models usage info.getTrainedModelsStats(Function<GetTrainedModelsStatsRequest.Builder, ObjectBuilder<GetTrainedModelsStatsRequest>> fn) Get trained models usage info.Evaluate a trained model.inferTrainedModel(Function<InferTrainedModelRequest.Builder, ObjectBuilder<InferTrainedModelRequest>> fn) Evaluate a trained model.info()Get machine learning information.openJob(OpenJobRequest request) Open anomaly detection jobs.final CompletableFuture<OpenJobResponse>Open anomaly detection jobs.Add scheduled events to the calendar.postCalendarEvents(Function<PostCalendarEventsRequest.Builder, ObjectBuilder<PostCalendarEventsRequest>> fn) Add scheduled events to the calendar.<TData> CompletableFuture<PostDataResponse>postData(PostDataRequest<TData> request) Send data to an anomaly detection job for analysis.final <TData> CompletableFuture<PostDataResponse>postData(Function<PostDataRequest.Builder<TData>, ObjectBuilder<PostDataRequest<TData>>> fn) Send data to an anomaly detection job for analysis.previewDatafeed(PreviewDatafeedRequest request) Overload ofpreviewDatafeed(PreviewDatafeedRequest, Class), where Class is defined as Void, meaning the documents will not be deserialized.<TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(PreviewDatafeedRequest request, Class<TDocument> tDocumentClass) Preview a datafeed.<TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(PreviewDatafeedRequest request, Type tDocumentType) Preview a datafeed.Overload ofpreviewDatafeed(Function, Class), where Class is defined as Void, meaning the documents will not be deserialized.final <TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Class<TDocument> tDocumentClass) Preview a datafeed.final <TDocument>
CompletableFuture<PreviewDatafeedResponse<TDocument>>previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn, Type tDocumentType) Preview a datafeed.Preview features used by data frame analytics.Preview features used by data frame analytics.previewDataFrameAnalytics(Function<PreviewDataFrameAnalyticsRequest.Builder, ObjectBuilder<PreviewDataFrameAnalyticsRequest>> fn) Preview features used by data frame analytics.putCalendar(PutCalendarRequest request) Create a calendar.Create a calendar.putCalendarJob(PutCalendarJobRequest request) Add anomaly detection job to calendar.Add anomaly detection job to calendar.putDatafeed(PutDatafeedRequest request) Create a datafeed.Create a datafeed.Create a data frame analytics job.putDataFrameAnalytics(Function<PutDataFrameAnalyticsRequest.Builder, ObjectBuilder<PutDataFrameAnalyticsRequest>> fn) Create a data frame analytics job.putFilter(PutFilterRequest request) Create a filter.Create a filter.putJob(PutJobRequest request) Create an anomaly detection job.final CompletableFuture<PutJobResponse>Create an anomaly detection job.putTrainedModel(PutTrainedModelRequest request) Create a trained model.Create a trained model.Create or update a trained model alias.putTrainedModelAlias(Function<PutTrainedModelAliasRequest.Builder, ObjectBuilder<PutTrainedModelAliasRequest>> fn) Create or update a trained model alias.Create part of a trained model definition.putTrainedModelDefinitionPart(Function<PutTrainedModelDefinitionPartRequest.Builder, ObjectBuilder<PutTrainedModelDefinitionPartRequest>> fn) Create part of a trained model definition.Create a trained model vocabulary.putTrainedModelVocabulary(Function<PutTrainedModelVocabularyRequest.Builder, ObjectBuilder<PutTrainedModelVocabularyRequest>> fn) Create a trained model vocabulary.resetJob(ResetJobRequest request) Reset an anomaly detection job.Reset an anomaly detection job.Revert to a snapshot.revertModelSnapshot(Function<RevertModelSnapshotRequest.Builder, ObjectBuilder<RevertModelSnapshotRequest>> fn) Revert to a snapshot.Set upgrade_mode for ML indices.setUpgradeMode(SetUpgradeModeRequest request) Set upgrade_mode for ML indices.Set upgrade_mode for ML indices.startDatafeed(StartDatafeedRequest request) Start datafeeds.Start datafeeds.Start a data frame analytics job.startDataFrameAnalytics(Function<StartDataFrameAnalyticsRequest.Builder, ObjectBuilder<StartDataFrameAnalyticsRequest>> fn) Start a data frame analytics job.Start a trained model deployment.startTrainedModelDeployment(Function<StartTrainedModelDeploymentRequest.Builder, ObjectBuilder<StartTrainedModelDeploymentRequest>> fn) Start a trained model deployment.stopDatafeed(StopDatafeedRequest request) Stop datafeeds.Stop datafeeds.Stop data frame analytics jobs.stopDataFrameAnalytics(Function<StopDataFrameAnalyticsRequest.Builder, ObjectBuilder<StopDataFrameAnalyticsRequest>> fn) Stop data frame analytics jobs.Stop a trained model deployment.stopTrainedModelDeployment(Function<StopTrainedModelDeploymentRequest.Builder, ObjectBuilder<StopTrainedModelDeploymentRequest>> fn) Stop a trained model deployment.updateDatafeed(UpdateDatafeedRequest request) Update a datafeed.Update a datafeed.Update a data frame analytics job.updateDataFrameAnalytics(Function<UpdateDataFrameAnalyticsRequest.Builder, ObjectBuilder<UpdateDataFrameAnalyticsRequest>> fn) Update a data frame analytics job.updateFilter(UpdateFilterRequest request) Update a filter.Update a filter.updateJob(UpdateJobRequest request) Update an anomaly detection job.Update an anomaly detection job.Update a snapshot.updateModelSnapshot(Function<UpdateModelSnapshotRequest.Builder, ObjectBuilder<UpdateModelSnapshotRequest>> fn) Update a snapshot.Update a trained model deployment.updateTrainedModelDeployment(Function<UpdateTrainedModelDeploymentRequest.Builder, ObjectBuilder<UpdateTrainedModelDeploymentRequest>> fn) Update a trained model deployment.Upgrade a snapshot.upgradeJobSnapshot(Function<UpgradeJobSnapshotRequest.Builder, ObjectBuilder<UpgradeJobSnapshotRequest>> fn) Upgrade a snapshot.validate()Validate an anomaly detection job.validate(ValidateRequest request) Validate an anomaly detection job.Validate an anomaly detection job.Validate an anomaly detection job.validateDetector(ValidateDetectorRequest request) Validate an anomaly detection job.validateDetector(Function<ValidateDetectorRequest.Builder, ObjectBuilder<ValidateDetectorRequest>> fn) Validate an anomaly detection job.withTransportOptions(TransportOptions transportOptions) Creates a new client with some request optionsMethods inherited from class co.elastic.clients.ApiClient
_jsonpMapper, _transport, _transportOptions, close, 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) Clear trained model deployment cache.Cache will be cleared 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.
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clearTrainedModelDeploymentCache
public final CompletableFuture<ClearTrainedModelDeploymentCacheResponse> clearTrainedModelDeploymentCache(Function<ClearTrainedModelDeploymentCacheRequest.Builder, ObjectBuilder<ClearTrainedModelDeploymentCacheRequest>> fn) Clear trained model deployment cache.Cache will be cleared 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.
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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.
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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.
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fn- a function that initializes a builder to create theCloseJobRequest- See Also:
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deleteCalendar
Delete a calendar.Remove all scheduled events from a calendar, then delete it.
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deleteCalendar
public final CompletableFuture<DeleteCalendarResponse> deleteCalendar(Function<DeleteCalendarRequest.Builder, ObjectBuilder<DeleteCalendarRequest>> fn) Delete a calendar.Remove all scheduled events from a calendar, then delete it.
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fn- a function that initializes a builder to create theDeleteCalendarRequest- See Also:
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deleteCalendarEvent
public CompletableFuture<DeleteCalendarEventResponse> deleteCalendarEvent(DeleteCalendarEventRequest request) Delete events from a calendar.- See Also:
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deleteCalendarEvent
public final CompletableFuture<DeleteCalendarEventResponse> deleteCalendarEvent(Function<DeleteCalendarEventRequest.Builder, ObjectBuilder<DeleteCalendarEventRequest>> fn) Delete 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) Delete anomaly jobs from a calendar.- See Also:
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deleteCalendarJob
public final CompletableFuture<DeleteCalendarJobResponse> deleteCalendarJob(Function<DeleteCalendarJobRequest.Builder, ObjectBuilder<DeleteCalendarJobRequest>> fn) Delete anomaly 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) Delete a data frame analytics job.- See Also:
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deleteDataFrameAnalytics
public final CompletableFuture<DeleteDataFrameAnalyticsResponse> deleteDataFrameAnalytics(Function<DeleteDataFrameAnalyticsRequest.Builder, ObjectBuilder<DeleteDataFrameAnalyticsRequest>> fn) Delete a data frame analytics job.- Parameters:
fn- a function that initializes a builder to create theDeleteDataFrameAnalyticsRequest- See Also:
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deleteDatafeed
Delete a datafeed.- See Also:
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deleteDatafeed
public final CompletableFuture<DeleteDatafeedResponse> deleteDatafeed(Function<DeleteDatafeedRequest.Builder, ObjectBuilder<DeleteDatafeedRequest>> fn) Delete a 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) Delete expired ML data.Delete 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) Delete expired ML data.Delete 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
Delete expired ML data.Delete 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
Delete 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.
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deleteFilter
public final CompletableFuture<DeleteFilterResponse> deleteFilter(Function<DeleteFilterRequest.Builder, ObjectBuilder<DeleteFilterRequest>> fn) Delete 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.
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fn- a function that initializes a builder to create theDeleteFilterRequest- See Also:
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deleteForecast
Delete forecasts from a job.By default, forecasts are retained for 14 days. You can specify a different retention period with the
expires_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) Delete forecasts from a job.By default, forecasts are retained for 14 days. You can specify a different retention period with the
expires_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
Delete 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) Delete 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) Delete a 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 the
model_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) Delete a 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 the
model_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) Delete an unreferenced trained model.The request deletes a trained inference model that is not referenced by an ingest pipeline.
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deleteTrainedModel
public final CompletableFuture<DeleteTrainedModelResponse> deleteTrainedModel(Function<DeleteTrainedModelRequest.Builder, ObjectBuilder<DeleteTrainedModelRequest>> fn) Delete an unreferenced trained model.The request deletes a trained inference model that is not referenced by an ingest pipeline.
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fn- a function that initializes a builder to create theDeleteTrainedModelRequest- See Also:
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deleteTrainedModelAlias
public CompletableFuture<DeleteTrainedModelAliasResponse> deleteTrainedModelAlias(DeleteTrainedModelAliasRequest request) Delete 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 the
model_id, this API returns an error.- See Also:
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deleteTrainedModelAlias
public final CompletableFuture<DeleteTrainedModelAliasResponse> deleteTrainedModelAlias(Function<DeleteTrainedModelAliasRequest.Builder, ObjectBuilder<DeleteTrainedModelAliasRequest>> fn) Delete 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 the
model_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) Estimate job model memory usage.Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references.
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estimateModelMemory
public final CompletableFuture<EstimateModelMemoryResponse> estimateModelMemory(Function<EstimateModelMemoryRequest.Builder, ObjectBuilder<EstimateModelMemoryRequest>> fn) Estimate job model memory usage.Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references.
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fn- a function that initializes a builder to create theEstimateModelMemoryRequest- See Also:
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estimateModelMemory
Estimate job model memory usage.Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references.
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evaluateDataFrame
public CompletableFuture<EvaluateDataFrameResponse> evaluateDataFrame(EvaluateDataFrameRequest request) Evaluate data frame analytics.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.
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evaluateDataFrame
public final CompletableFuture<EvaluateDataFrameResponse> evaluateDataFrame(Function<EvaluateDataFrameRequest.Builder, ObjectBuilder<EvaluateDataFrameRequest>> fn) Evaluate data frame analytics.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.
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fn- a function that initializes a builder to create theEvaluateDataFrameRequest- See Also:
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explainDataFrameAnalytics
public CompletableFuture<ExplainDataFrameAnalyticsResponse> explainDataFrameAnalytics(ExplainDataFrameAnalyticsRequest request) Explain 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) Explain 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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fn- a function that initializes a builder to create theExplainDataFrameAnalyticsRequest- See Also:
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explainDataFrameAnalytics
Explain 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
Force buffered data to be processed. 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) Force buffered data to be processed. 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
Predict future behavior of a time series.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. Forcasts predict future behavior based on historical data.- See Also:
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forecast
public final CompletableFuture<ForecastResponse> forecast(Function<ForecastRequest.Builder, ObjectBuilder<ForecastRequest>> fn) Predict future behavior of a time series.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. Forcasts predict future behavior based on historical data.- Parameters:
fn- a function that initializes a builder to create theForecastRequest- See Also:
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getBuckets
Get anomaly detection job results for 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) Get anomaly detection job results for 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) Get info about events in calendars.- See Also:
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getCalendarEvents
public final CompletableFuture<GetCalendarEventsResponse> getCalendarEvents(Function<GetCalendarEventsRequest.Builder, ObjectBuilder<GetCalendarEventsRequest>> fn) Get info about events in calendars.- Parameters:
fn- a function that initializes a builder to create theGetCalendarEventsRequest- See Also:
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getCalendars
Get calendar configuration info.- See Also:
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getCalendars
public final CompletableFuture<GetCalendarsResponse> getCalendars(Function<GetCalendarsRequest.Builder, ObjectBuilder<GetCalendarsRequest>> fn) Get calendar configuration info.- Parameters:
fn- a function that initializes a builder to create theGetCalendarsRequest- See Also:
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getCalendars
Get calendar configuration info.- See Also:
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getCategories
Get anomaly detection job results for categories.- See Also:
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getCategories
public final CompletableFuture<GetCategoriesResponse> getCategories(Function<GetCategoriesRequest.Builder, ObjectBuilder<GetCategoriesRequest>> fn) Get anomaly detection job results for 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) Get data frame analytics job configuration info. 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) Get data frame analytics job configuration info. 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
Get data frame analytics job configuration info. 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) Get data frame analytics jobs usage info.- See Also:
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getDataFrameAnalyticsStats
public final CompletableFuture<GetDataFrameAnalyticsStatsResponse> getDataFrameAnalyticsStats(Function<GetDataFrameAnalyticsStatsRequest.Builder, ObjectBuilder<GetDataFrameAnalyticsStatsRequest>> fn) Get data frame analytics jobs usage info.- Parameters:
fn- a function that initializes a builder to create theGetDataFrameAnalyticsStatsRequest- See Also:
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getDataFrameAnalyticsStats
Get data frame analytics jobs usage info.- See Also:
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getDatafeedStats
public CompletableFuture<GetDatafeedStatsResponse> getDatafeedStats(GetDatafeedStatsRequest request) Get datafeeds usage info. 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) Get datafeeds usage info. 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
Get datafeeds usage info. 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
Get datafeeds configuration info. 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) Get datafeeds configuration info. 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
Get datafeeds configuration info. 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
Get 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) Get 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
Get filters. You can get a single filter or all filters.- See Also:
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getInfluencers
Get anomaly detection job results for 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) Get anomaly detection job results for 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
Get anomaly detection jobs usage info.- See Also:
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getJobStats
public final CompletableFuture<GetJobStatsResponse> getJobStats(Function<GetJobStatsRequest.Builder, ObjectBuilder<GetJobStatsRequest>> fn) Get anomaly detection jobs usage info.- Parameters:
fn- a function that initializes a builder to create theGetJobStatsRequest- See Also:
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getJobStats
Get anomaly detection jobs usage info.- See Also:
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getJobs
Get anomaly detection jobs configuration info. 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) Get anomaly detection jobs configuration info. 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
Get anomaly detection jobs configuration info. 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 machine learning memory usage info. 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 machine learning memory usage info. 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 machine learning memory usage info. 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) Get anomaly detection job model snapshot upgrade usage info.- See Also:
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getModelSnapshotUpgradeStats
public final CompletableFuture<GetModelSnapshotUpgradeStatsResponse> getModelSnapshotUpgradeStats(Function<GetModelSnapshotUpgradeStatsRequest.Builder, ObjectBuilder<GetModelSnapshotUpgradeStatsRequest>> fn) Get anomaly detection job model snapshot upgrade usage info.- Parameters:
fn- a function that initializes a builder to create theGetModelSnapshotUpgradeStatsRequest- See Also:
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getModelSnapshots
public CompletableFuture<GetModelSnapshotsResponse> getModelSnapshots(GetModelSnapshotsRequest request) Get model snapshots info.- See Also:
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getModelSnapshots
public final CompletableFuture<GetModelSnapshotsResponse> getModelSnapshots(Function<GetModelSnapshotsRequest.Builder, ObjectBuilder<GetModelSnapshotsRequest>> fn) Get model snapshots info.- Parameters:
fn- a function that initializes a builder to create theGetModelSnapshotsRequest- See Also:
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getOverallBuckets
public CompletableFuture<GetOverallBucketsResponse> getOverallBuckets(GetOverallBucketsRequest request) Get overall bucket results.Retrievs 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) Get overall bucket results.Retrievs 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
Get 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) Get 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) Get trained model configuration info.- See Also:
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getTrainedModels
public final CompletableFuture<GetTrainedModelsResponse> getTrainedModels(Function<GetTrainedModelsRequest.Builder, ObjectBuilder<GetTrainedModelsRequest>> fn) Get trained model configuration info.- Parameters:
fn- a function that initializes a builder to create theGetTrainedModelsRequest- See Also:
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getTrainedModels
Get trained model configuration info.- See Also:
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getTrainedModelsStats
public CompletableFuture<GetTrainedModelsStatsResponse> getTrainedModelsStats(GetTrainedModelsStatsRequest request) Get trained models usage info. 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) Get trained models usage info. 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
Get trained models usage info. 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) Evaluate a trained model.- See Also:
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inferTrainedModel
public final CompletableFuture<InferTrainedModelResponse> inferTrainedModel(Function<InferTrainedModelRequest.Builder, ObjectBuilder<InferTrainedModelRequest>> fn) Evaluate a trained model.- Parameters:
fn- a function that initializes a builder to create theInferTrainedModelRequest- See Also:
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info
Get machine learning information. Get 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
Open anomaly detection jobs.An anomaly detection job must be opened 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.
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openJob
public final CompletableFuture<OpenJobResponse> openJob(Function<OpenJobRequest.Builder, ObjectBuilder<OpenJobRequest>> fn) Open anomaly detection jobs.An anomaly detection job must be opened 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) Add scheduled events to the calendar.- See Also:
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postCalendarEvents
public final CompletableFuture<PostCalendarEventsResponse> postCalendarEvents(Function<PostCalendarEventsRequest.Builder, ObjectBuilder<PostCalendarEventsRequest>> fn) Add scheduled events to the calendar.- Parameters:
fn- a function that initializes a builder to create thePostCalendarEventsRequest- See Also:
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postData
Send 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.
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postData
public final <TData> CompletableFuture<PostDataResponse> postData(Function<PostDataRequest.Builder<TData>, ObjectBuilder<PostDataRequest<TData>>> fn) Send 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) Preview features used by data frame analytics. Preview 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) Preview features used by data frame analytics. Preview 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
Preview features used by data frame analytics. Preview 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) Preview 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) Preview 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 CompletableFuture<PreviewDatafeedResponse<Void>> previewDatafeed(PreviewDatafeedRequest request) Overload ofpreviewDatafeed(PreviewDatafeedRequest, Class), where Class is defined as Void, meaning the documents will not be deserialized. -
previewDatafeed
public final CompletableFuture<PreviewDatafeedResponse<Void>> previewDatafeed(Function<PreviewDatafeedRequest.Builder, ObjectBuilder<PreviewDatafeedRequest>> fn) Overload ofpreviewDatafeed(Function, Class), where Class is defined as Void, meaning the documents will not be deserialized. -
previewDatafeed
public <TDocument> CompletableFuture<PreviewDatafeedResponse<TDocument>> previewDatafeed(PreviewDatafeedRequest request, Type tDocumentType) Preview 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) Preview 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
Create a calendar.- See Also:
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putCalendar
public final CompletableFuture<PutCalendarResponse> putCalendar(Function<PutCalendarRequest.Builder, ObjectBuilder<PutCalendarRequest>> fn) Create a calendar.- Parameters:
fn- a function that initializes a builder to create thePutCalendarRequest- See Also:
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putCalendarJob
Add anomaly detection job to calendar.- See Also:
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putCalendarJob
public final CompletableFuture<PutCalendarJobResponse> putCalendarJob(Function<PutCalendarJobRequest.Builder, ObjectBuilder<PutCalendarJobRequest>> fn) Add anomaly detection job to 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) Create 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. By default, the query used in the source configuration is{"match_all": {}}.If the destination index does not exist, it is created automatically when you start the job.
If you supply only a subset of the regression or classification parameters, hyperparameter optimization occurs. It determines a value for each of the undefined parameters.
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putDataFrameAnalytics
public final CompletableFuture<PutDataFrameAnalyticsResponse> putDataFrameAnalytics(Function<PutDataFrameAnalyticsRequest.Builder, ObjectBuilder<PutDataFrameAnalyticsRequest>> fn) Create 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. By default, the query used in the source configuration is{"match_all": {}}.If the destination index does not exist, it is created automatically when you start the job.
If you supply only a subset of the regression or classification parameters, hyperparameter optimization occurs. It determines a value for each of the undefined parameters.
- Parameters:
fn- a function that initializes a builder to create thePutDataFrameAnalyticsRequest- See Also:
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putDatafeed
Create 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. By default, the datafeed uses the following query:{"match_all": {"boost": 1}}`.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-configindex.- See Also:
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putDatafeed
public final CompletableFuture<PutDatafeedResponse> putDatafeed(Function<PutDatafeedRequest.Builder, ObjectBuilder<PutDatafeedRequest>> fn) Create 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. By default, the datafeed uses the following query:{"match_all": {"boost": 1}}`.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-configindex.- Parameters:
fn- a function that initializes a builder to create thePutDatafeedRequest- See Also:
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putFilter
Create 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) Create 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
Create an anomaly detection job.If you include a
datafeed_config, you must have read index privileges on the source index. If you include adatafeed_configbut do not provide a query, the datafeed uses{"match_all": {"boost": 1}}.- See Also:
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putJob
public final CompletableFuture<PutJobResponse> putJob(Function<PutJobRequest.Builder, ObjectBuilder<PutJobRequest>> fn) Create an anomaly detection job.If you include a
datafeed_config, you must have read index privileges on the source index. If you include adatafeed_configbut do not provide a query, the datafeed uses{"match_all": {"boost": 1}}.- Parameters:
fn- a function that initializes a builder to create thePutJobRequest- See Also:
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putTrainedModel
Create a trained model. Enable 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) Create a trained model. Enable 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) Create or update 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) Create or update 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) Create part of a trained model definition.- See Also:
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putTrainedModelDefinitionPart
public final CompletableFuture<PutTrainedModelDefinitionPartResponse> putTrainedModelDefinitionPart(Function<PutTrainedModelDefinitionPartRequest.Builder, ObjectBuilder<PutTrainedModelDefinitionPartRequest>> fn) Create 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) Create 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) Create 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
Reset 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) Reset 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) Revert to a 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) Revert to a 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
Set upgrade_mode for ML indices. 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) Set upgrade_mode for ML indices. 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
Set upgrade_mode for ML indices. 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) Start 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) Start 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
Start 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) Start 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) Start a trained model deployment. It 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) Start a trained model deployment. It 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) Stop 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) Stop 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
Stop 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) Stop 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) Stop a trained model deployment.- See Also:
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stopTrainedModelDeployment
public final CompletableFuture<StopTrainedModelDeploymentResponse> stopTrainedModelDeployment(Function<StopTrainedModelDeploymentRequest.Builder, ObjectBuilder<StopTrainedModelDeploymentRequest>> fn) Stop 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) Update a data frame analytics job.- See Also:
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updateDataFrameAnalytics
public final CompletableFuture<UpdateDataFrameAnalyticsResponse> updateDataFrameAnalytics(Function<UpdateDataFrameAnalyticsRequest.Builder, ObjectBuilder<UpdateDataFrameAnalyticsRequest>> fn) Update a data frame analytics job.- Parameters:
fn- a function that initializes a builder to create theUpdateDataFrameAnalyticsRequest- See Also:
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updateDatafeed
Update 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) Update 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
Update a filter. 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) Update a filter. 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
Update an anomaly detection job. 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) Update an anomaly detection job. 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) Update a snapshot. Updates certain properties of a snapshot.- See Also:
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updateModelSnapshot
public final CompletableFuture<UpdateModelSnapshotResponse> updateModelSnapshot(Function<UpdateModelSnapshotRequest.Builder, ObjectBuilder<UpdateModelSnapshotRequest>> fn) Update a snapshot. 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) Update a trained model deployment.- See Also:
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updateTrainedModelDeployment
public final CompletableFuture<UpdateTrainedModelDeploymentResponse> updateTrainedModelDeployment(Function<UpdateTrainedModelDeploymentRequest.Builder, ObjectBuilder<UpdateTrainedModelDeploymentRequest>> fn) Update a trained model deployment.- Parameters:
fn- a function that initializes a builder to create theUpdateTrainedModelDeploymentRequest- See Also:
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upgradeJobSnapshot
public CompletableFuture<UpgradeJobSnapshotResponse> upgradeJobSnapshot(UpgradeJobSnapshotRequest request) Upgrade a snapshot. Upgrade 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) Upgrade a snapshot. Upgrade 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
Validate an anomaly detection job.- See Also:
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validate
public final CompletableFuture<ValidateResponse> validate(Function<ValidateRequest.Builder, ObjectBuilder<ValidateRequest>> fn) Validate an anomaly detection job.- Parameters:
fn- a function that initializes a builder to create theValidateRequest- See Also:
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validate
Validate an anomaly detection job.- See Also:
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validateDetector
public CompletableFuture<ValidateDetectorResponse> validateDetector(ValidateDetectorRequest request) Validate an anomaly detection job.- See Also:
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validateDetector
public final CompletableFuture<ValidateDetectorResponse> validateDetector(Function<ValidateDetectorRequest.Builder, ObjectBuilder<ValidateDetectorRequest>> fn) Validate an anomaly detection job.- Parameters:
fn- a function that initializes a builder to create theValidateDetectorRequest- See Also:
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validateDetector
Validate an anomaly detection job.- See Also:
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