@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AbstractAmazonEMRContainersAsync extends AbstractAmazonEMRContainers implements AmazonEMRContainersAsync
AmazonEMRContainersAsync. Convenient method forms pass through to the
corresponding overload that takes a request object and an AsyncHandler, which throws an
UnsupportedOperationException.ENDPOINT_PREFIXcancelJobRun, createJobTemplate, createManagedEndpoint, createSecurityConfiguration, createVirtualCluster, deleteJobTemplate, deleteManagedEndpoint, deleteVirtualCluster, describeJobRun, describeJobTemplate, describeManagedEndpoint, describeSecurityConfiguration, describeVirtualCluster, getCachedResponseMetadata, getManagedEndpointSessionCredentials, listJobRuns, listJobTemplates, listManagedEndpoints, listSecurityConfigurations, listTagsForResource, listVirtualClusters, shutdown, startJobRun, tagResource, untagResourceequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcancelJobRun, createJobTemplate, createManagedEndpoint, createSecurityConfiguration, createVirtualCluster, deleteJobTemplate, deleteManagedEndpoint, deleteVirtualCluster, describeJobRun, describeJobTemplate, describeManagedEndpoint, describeSecurityConfiguration, describeVirtualCluster, getCachedResponseMetadata, getManagedEndpointSessionCredentials, listJobRuns, listJobTemplates, listManagedEndpoints, listSecurityConfigurations, listTagsForResource, listVirtualClusters, shutdown, startJobRun, tagResource, untagResourcepublic Future<CancelJobRunResult> cancelJobRunAsync(CancelJobRunRequest request)
AmazonEMRContainersAsyncCancels a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
cancelJobRunAsync in interface AmazonEMRContainersAsyncpublic Future<CancelJobRunResult> cancelJobRunAsync(CancelJobRunRequest request, AsyncHandler<CancelJobRunRequest,CancelJobRunResult> asyncHandler)
AmazonEMRContainersAsyncCancels a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
cancelJobRunAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateJobTemplateResult> createJobTemplateAsync(CreateJobTemplateRequest request)
AmazonEMRContainersAsyncCreates a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
createJobTemplateAsync in interface AmazonEMRContainersAsyncpublic Future<CreateJobTemplateResult> createJobTemplateAsync(CreateJobTemplateRequest request, AsyncHandler<CreateJobTemplateRequest,CreateJobTemplateResult> asyncHandler)
AmazonEMRContainersAsyncCreates a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
createJobTemplateAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateManagedEndpointResult> createManagedEndpointAsync(CreateManagedEndpointRequest request)
AmazonEMRContainersAsyncCreates a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
createManagedEndpointAsync in interface AmazonEMRContainersAsyncpublic Future<CreateManagedEndpointResult> createManagedEndpointAsync(CreateManagedEndpointRequest request, AsyncHandler<CreateManagedEndpointRequest,CreateManagedEndpointResult> asyncHandler)
AmazonEMRContainersAsyncCreates a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
createManagedEndpointAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateSecurityConfigurationResult> createSecurityConfigurationAsync(CreateSecurityConfigurationRequest request)
AmazonEMRContainersAsyncCreates a security configuration. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
createSecurityConfigurationAsync in interface AmazonEMRContainersAsyncpublic Future<CreateSecurityConfigurationResult> createSecurityConfigurationAsync(CreateSecurityConfigurationRequest request, AsyncHandler<CreateSecurityConfigurationRequest,CreateSecurityConfigurationResult> asyncHandler)
AmazonEMRContainersAsyncCreates a security configuration. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
createSecurityConfigurationAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateVirtualClusterResult> createVirtualClusterAsync(CreateVirtualClusterRequest request)
AmazonEMRContainersAsyncCreates a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
createVirtualClusterAsync in interface AmazonEMRContainersAsyncpublic Future<CreateVirtualClusterResult> createVirtualClusterAsync(CreateVirtualClusterRequest request, AsyncHandler<CreateVirtualClusterRequest,CreateVirtualClusterResult> asyncHandler)
AmazonEMRContainersAsyncCreates a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
createVirtualClusterAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteJobTemplateResult> deleteJobTemplateAsync(DeleteJobTemplateRequest request)
AmazonEMRContainersAsyncDeletes a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
deleteJobTemplateAsync in interface AmazonEMRContainersAsyncpublic Future<DeleteJobTemplateResult> deleteJobTemplateAsync(DeleteJobTemplateRequest request, AsyncHandler<DeleteJobTemplateRequest,DeleteJobTemplateResult> asyncHandler)
AmazonEMRContainersAsyncDeletes a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
deleteJobTemplateAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteManagedEndpointResult> deleteManagedEndpointAsync(DeleteManagedEndpointRequest request)
AmazonEMRContainersAsyncDeletes a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
deleteManagedEndpointAsync in interface AmazonEMRContainersAsyncpublic Future<DeleteManagedEndpointResult> deleteManagedEndpointAsync(DeleteManagedEndpointRequest request, AsyncHandler<DeleteManagedEndpointRequest,DeleteManagedEndpointResult> asyncHandler)
AmazonEMRContainersAsyncDeletes a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
deleteManagedEndpointAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteVirtualClusterResult> deleteVirtualClusterAsync(DeleteVirtualClusterRequest request)
AmazonEMRContainersAsyncDeletes a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
deleteVirtualClusterAsync in interface AmazonEMRContainersAsyncpublic Future<DeleteVirtualClusterResult> deleteVirtualClusterAsync(DeleteVirtualClusterRequest request, AsyncHandler<DeleteVirtualClusterRequest,DeleteVirtualClusterResult> asyncHandler)
AmazonEMRContainersAsyncDeletes a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
deleteVirtualClusterAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeJobRunResult> describeJobRunAsync(DescribeJobRunRequest request)
AmazonEMRContainersAsyncDisplays detailed information about a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
describeJobRunAsync in interface AmazonEMRContainersAsyncpublic Future<DescribeJobRunResult> describeJobRunAsync(DescribeJobRunRequest request, AsyncHandler<DescribeJobRunRequest,DescribeJobRunResult> asyncHandler)
AmazonEMRContainersAsyncDisplays detailed information about a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
describeJobRunAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeJobTemplateResult> describeJobTemplateAsync(DescribeJobTemplateRequest request)
AmazonEMRContainersAsyncDisplays detailed information about a specified job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
describeJobTemplateAsync in interface AmazonEMRContainersAsyncpublic Future<DescribeJobTemplateResult> describeJobTemplateAsync(DescribeJobTemplateRequest request, AsyncHandler<DescribeJobTemplateRequest,DescribeJobTemplateResult> asyncHandler)
AmazonEMRContainersAsyncDisplays detailed information about a specified job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
describeJobTemplateAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeManagedEndpointResult> describeManagedEndpointAsync(DescribeManagedEndpointRequest request)
AmazonEMRContainersAsyncDisplays detailed information about a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
describeManagedEndpointAsync in interface AmazonEMRContainersAsyncpublic Future<DescribeManagedEndpointResult> describeManagedEndpointAsync(DescribeManagedEndpointRequest request, AsyncHandler<DescribeManagedEndpointRequest,DescribeManagedEndpointResult> asyncHandler)
AmazonEMRContainersAsyncDisplays detailed information about a managed endpoint. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
describeManagedEndpointAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeSecurityConfigurationResult> describeSecurityConfigurationAsync(DescribeSecurityConfigurationRequest request)
AmazonEMRContainersAsyncDisplays detailed information about a specified security configuration. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
describeSecurityConfigurationAsync in interface AmazonEMRContainersAsyncpublic Future<DescribeSecurityConfigurationResult> describeSecurityConfigurationAsync(DescribeSecurityConfigurationRequest request, AsyncHandler<DescribeSecurityConfigurationRequest,DescribeSecurityConfigurationResult> asyncHandler)
AmazonEMRContainersAsyncDisplays detailed information about a specified security configuration. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
describeSecurityConfigurationAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeVirtualClusterResult> describeVirtualClusterAsync(DescribeVirtualClusterRequest request)
AmazonEMRContainersAsyncDisplays detailed information about a specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
describeVirtualClusterAsync in interface AmazonEMRContainersAsyncpublic Future<DescribeVirtualClusterResult> describeVirtualClusterAsync(DescribeVirtualClusterRequest request, AsyncHandler<DescribeVirtualClusterRequest,DescribeVirtualClusterResult> asyncHandler)
AmazonEMRContainersAsyncDisplays detailed information about a specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
describeVirtualClusterAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetManagedEndpointSessionCredentialsResult> getManagedEndpointSessionCredentialsAsync(GetManagedEndpointSessionCredentialsRequest request)
AmazonEMRContainersAsyncGenerate a session token to connect to a managed endpoint.
getManagedEndpointSessionCredentialsAsync in interface AmazonEMRContainersAsyncpublic Future<GetManagedEndpointSessionCredentialsResult> getManagedEndpointSessionCredentialsAsync(GetManagedEndpointSessionCredentialsRequest request, AsyncHandler<GetManagedEndpointSessionCredentialsRequest,GetManagedEndpointSessionCredentialsResult> asyncHandler)
AmazonEMRContainersAsyncGenerate a session token to connect to a managed endpoint.
getManagedEndpointSessionCredentialsAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListJobRunsResult> listJobRunsAsync(ListJobRunsRequest request)
AmazonEMRContainersAsyncLists job runs based on a set of parameters. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
listJobRunsAsync in interface AmazonEMRContainersAsyncpublic Future<ListJobRunsResult> listJobRunsAsync(ListJobRunsRequest request, AsyncHandler<ListJobRunsRequest,ListJobRunsResult> asyncHandler)
AmazonEMRContainersAsyncLists job runs based on a set of parameters. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
listJobRunsAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListJobTemplatesResult> listJobTemplatesAsync(ListJobTemplatesRequest request)
AmazonEMRContainersAsyncLists job templates based on a set of parameters. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
listJobTemplatesAsync in interface AmazonEMRContainersAsyncpublic Future<ListJobTemplatesResult> listJobTemplatesAsync(ListJobTemplatesRequest request, AsyncHandler<ListJobTemplatesRequest,ListJobTemplatesResult> asyncHandler)
AmazonEMRContainersAsyncLists job templates based on a set of parameters. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
listJobTemplatesAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListManagedEndpointsResult> listManagedEndpointsAsync(ListManagedEndpointsRequest request)
AmazonEMRContainersAsyncLists managed endpoints based on a set of parameters. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
listManagedEndpointsAsync in interface AmazonEMRContainersAsyncpublic Future<ListManagedEndpointsResult> listManagedEndpointsAsync(ListManagedEndpointsRequest request, AsyncHandler<ListManagedEndpointsRequest,ListManagedEndpointsResult> asyncHandler)
AmazonEMRContainersAsyncLists managed endpoints based on a set of parameters. A managed endpoint is a gateway that connects Amazon EMR Studio to Amazon EMR on EKS so that Amazon EMR Studio can communicate with your virtual cluster.
listManagedEndpointsAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListSecurityConfigurationsResult> listSecurityConfigurationsAsync(ListSecurityConfigurationsRequest request)
AmazonEMRContainersAsyncLists security configurations based on a set of parameters. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
listSecurityConfigurationsAsync in interface AmazonEMRContainersAsyncpublic Future<ListSecurityConfigurationsResult> listSecurityConfigurationsAsync(ListSecurityConfigurationsRequest request, AsyncHandler<ListSecurityConfigurationsRequest,ListSecurityConfigurationsResult> asyncHandler)
AmazonEMRContainersAsyncLists security configurations based on a set of parameters. Security configurations in Amazon EMR on EKS are templates for different security setups. You can use security configurations to configure the Lake Formation integration setup. You can also create a security configuration to re-use a security setup each time you create a virtual cluster.
listSecurityConfigurationsAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListTagsForResourceResult> listTagsForResourceAsync(ListTagsForResourceRequest request)
AmazonEMRContainersAsyncLists the tags assigned to the resources.
listTagsForResourceAsync in interface AmazonEMRContainersAsyncpublic Future<ListTagsForResourceResult> listTagsForResourceAsync(ListTagsForResourceRequest request, AsyncHandler<ListTagsForResourceRequest,ListTagsForResourceResult> asyncHandler)
AmazonEMRContainersAsyncLists the tags assigned to the resources.
listTagsForResourceAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<ListVirtualClustersResult> listVirtualClustersAsync(ListVirtualClustersRequest request)
AmazonEMRContainersAsyncLists information about the specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
listVirtualClustersAsync in interface AmazonEMRContainersAsyncpublic Future<ListVirtualClustersResult> listVirtualClustersAsync(ListVirtualClustersRequest request, AsyncHandler<ListVirtualClustersRequest,ListVirtualClustersResult> asyncHandler)
AmazonEMRContainersAsyncLists information about the specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
listVirtualClustersAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<StartJobRunResult> startJobRunAsync(StartJobRunRequest request)
AmazonEMRContainersAsyncStarts a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
startJobRunAsync in interface AmazonEMRContainersAsyncpublic Future<StartJobRunResult> startJobRunAsync(StartJobRunRequest request, AsyncHandler<StartJobRunRequest,StartJobRunResult> asyncHandler)
AmazonEMRContainersAsyncStarts a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
startJobRunAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<TagResourceResult> tagResourceAsync(TagResourceRequest request)
AmazonEMRContainersAsyncAssigns tags to resources. A tag is a label that you assign to an Amazon Web Services resource. Each tag consists of a key and an optional value, both of which you define. Tags enable you to categorize your Amazon Web Services resources by attributes such as purpose, owner, or environment. When you have many resources of the same type, you can quickly identify a specific resource based on the tags you've assigned to it. For example, you can define a set of tags for your Amazon EMR on EKS clusters to help you track each cluster's owner and stack level. We recommend that you devise a consistent set of tag keys for each resource type. You can then search and filter the resources based on the tags that you add.
tagResourceAsync in interface AmazonEMRContainersAsyncpublic Future<TagResourceResult> tagResourceAsync(TagResourceRequest request, AsyncHandler<TagResourceRequest,TagResourceResult> asyncHandler)
AmazonEMRContainersAsyncAssigns tags to resources. A tag is a label that you assign to an Amazon Web Services resource. Each tag consists of a key and an optional value, both of which you define. Tags enable you to categorize your Amazon Web Services resources by attributes such as purpose, owner, or environment. When you have many resources of the same type, you can quickly identify a specific resource based on the tags you've assigned to it. For example, you can define a set of tags for your Amazon EMR on EKS clusters to help you track each cluster's owner and stack level. We recommend that you devise a consistent set of tag keys for each resource type. You can then search and filter the resources based on the tags that you add.
tagResourceAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UntagResourceResult> untagResourceAsync(UntagResourceRequest request)
AmazonEMRContainersAsyncRemoves tags from resources.
untagResourceAsync in interface AmazonEMRContainersAsyncpublic Future<UntagResourceResult> untagResourceAsync(UntagResourceRequest request, AsyncHandler<UntagResourceRequest,UntagResourceResult> asyncHandler)
AmazonEMRContainersAsyncRemoves tags from resources.
untagResourceAsync in interface AmazonEMRContainersAsyncasyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.