@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public interface AmazonEMRContainersAsync extends AmazonEMRContainers
AsyncHandler can be used to receive
notification when an asynchronous operation completes.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
AbstractAmazonEMRContainersAsync instead.
Amazon EMR on EKS provides a deployment option for Amazon EMR that allows you to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With this deployment option, you can focus on running analytics workloads while Amazon EMR on EKS builds, configures, and manages containers for open-source applications. For more information about Amazon EMR on EKS concepts and tasks, see What is shared id="EMR-EKS"/>.
Amazon EMR containers is the API name for Amazon EMR on EKS. The emr-containers prefix is used in
the following scenarios:
It is the prefix in the CLI commands for Amazon EMR on EKS. For example,
aws emr-containers start-job-run.
It is the prefix before IAM policy actions for Amazon EMR on EKS. For example,
"Action": [ "emr-containers:StartJobRun"]. For more information, see Policy actions for Amazon EMR on EKS.
It is the prefix used in Amazon EMR on EKS service endpoints. For example,
emr-containers.us-east-2.amazonaws.com. For more information, see Amazon EMR on EKSService Endpoints.
ENDPOINT_PREFIXcancelJobRun, createJobTemplate, createManagedEndpoint, createVirtualCluster, deleteJobTemplate, deleteManagedEndpoint, deleteVirtualCluster, describeJobRun, describeJobTemplate, describeManagedEndpoint, describeVirtualCluster, getCachedResponseMetadata, listJobRuns, listJobTemplates, listManagedEndpoints, listTagsForResource, listVirtualClusters, shutdown, startJobRun, tagResource, untagResourceFuture<CancelJobRunResult> cancelJobRunAsync(CancelJobRunRequest cancelJobRunRequest)
Cancels 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.
cancelJobRunRequest - Future<CancelJobRunResult> cancelJobRunAsync(CancelJobRunRequest cancelJobRunRequest, AsyncHandler<CancelJobRunRequest,CancelJobRunResult> asyncHandler)
Cancels 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.
cancelJobRunRequest - asyncHandler - 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.Future<CreateJobTemplateResult> createJobTemplateAsync(CreateJobTemplateRequest createJobTemplateRequest)
Creates 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.
createJobTemplateRequest - Future<CreateJobTemplateResult> createJobTemplateAsync(CreateJobTemplateRequest createJobTemplateRequest, AsyncHandler<CreateJobTemplateRequest,CreateJobTemplateResult> asyncHandler)
Creates 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.
createJobTemplateRequest - asyncHandler - 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.Future<CreateManagedEndpointResult> createManagedEndpointAsync(CreateManagedEndpointRequest createManagedEndpointRequest)
Creates 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.
createManagedEndpointRequest - Future<CreateManagedEndpointResult> createManagedEndpointAsync(CreateManagedEndpointRequest createManagedEndpointRequest, AsyncHandler<CreateManagedEndpointRequest,CreateManagedEndpointResult> asyncHandler)
Creates 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.
createManagedEndpointRequest - asyncHandler - 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.Future<CreateVirtualClusterResult> createVirtualClusterAsync(CreateVirtualClusterRequest createVirtualClusterRequest)
Creates 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.
createVirtualClusterRequest - Future<CreateVirtualClusterResult> createVirtualClusterAsync(CreateVirtualClusterRequest createVirtualClusterRequest, AsyncHandler<CreateVirtualClusterRequest,CreateVirtualClusterResult> asyncHandler)
Creates 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.
createVirtualClusterRequest - asyncHandler - 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.Future<DeleteJobTemplateResult> deleteJobTemplateAsync(DeleteJobTemplateRequest deleteJobTemplateRequest)
Deletes 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.
deleteJobTemplateRequest - Future<DeleteJobTemplateResult> deleteJobTemplateAsync(DeleteJobTemplateRequest deleteJobTemplateRequest, AsyncHandler<DeleteJobTemplateRequest,DeleteJobTemplateResult> asyncHandler)
Deletes 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.
deleteJobTemplateRequest - asyncHandler - 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.Future<DeleteManagedEndpointResult> deleteManagedEndpointAsync(DeleteManagedEndpointRequest deleteManagedEndpointRequest)
Deletes 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.
deleteManagedEndpointRequest - Future<DeleteManagedEndpointResult> deleteManagedEndpointAsync(DeleteManagedEndpointRequest deleteManagedEndpointRequest, AsyncHandler<DeleteManagedEndpointRequest,DeleteManagedEndpointResult> asyncHandler)
Deletes 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.
deleteManagedEndpointRequest - asyncHandler - 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.Future<DeleteVirtualClusterResult> deleteVirtualClusterAsync(DeleteVirtualClusterRequest deleteVirtualClusterRequest)
Deletes 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.
deleteVirtualClusterRequest - Future<DeleteVirtualClusterResult> deleteVirtualClusterAsync(DeleteVirtualClusterRequest deleteVirtualClusterRequest, AsyncHandler<DeleteVirtualClusterRequest,DeleteVirtualClusterResult> asyncHandler)
Deletes 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.
deleteVirtualClusterRequest - asyncHandler - 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.Future<DescribeJobRunResult> describeJobRunAsync(DescribeJobRunRequest describeJobRunRequest)
Displays 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.
describeJobRunRequest - Future<DescribeJobRunResult> describeJobRunAsync(DescribeJobRunRequest describeJobRunRequest, AsyncHandler<DescribeJobRunRequest,DescribeJobRunResult> asyncHandler)
Displays 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.
describeJobRunRequest - asyncHandler - 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.Future<DescribeJobTemplateResult> describeJobTemplateAsync(DescribeJobTemplateRequest describeJobTemplateRequest)
Displays 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.
describeJobTemplateRequest - Future<DescribeJobTemplateResult> describeJobTemplateAsync(DescribeJobTemplateRequest describeJobTemplateRequest, AsyncHandler<DescribeJobTemplateRequest,DescribeJobTemplateResult> asyncHandler)
Displays 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.
describeJobTemplateRequest - asyncHandler - 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.Future<DescribeManagedEndpointResult> describeManagedEndpointAsync(DescribeManagedEndpointRequest describeManagedEndpointRequest)
Displays 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.
describeManagedEndpointRequest - Future<DescribeManagedEndpointResult> describeManagedEndpointAsync(DescribeManagedEndpointRequest describeManagedEndpointRequest, AsyncHandler<DescribeManagedEndpointRequest,DescribeManagedEndpointResult> asyncHandler)
Displays 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.
describeManagedEndpointRequest - asyncHandler - 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.Future<DescribeVirtualClusterResult> describeVirtualClusterAsync(DescribeVirtualClusterRequest describeVirtualClusterRequest)
Displays 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.
describeVirtualClusterRequest - Future<DescribeVirtualClusterResult> describeVirtualClusterAsync(DescribeVirtualClusterRequest describeVirtualClusterRequest, AsyncHandler<DescribeVirtualClusterRequest,DescribeVirtualClusterResult> asyncHandler)
Displays 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.
describeVirtualClusterRequest - asyncHandler - 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.Future<ListJobRunsResult> listJobRunsAsync(ListJobRunsRequest listJobRunsRequest)
Lists 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.
listJobRunsRequest - Future<ListJobRunsResult> listJobRunsAsync(ListJobRunsRequest listJobRunsRequest, AsyncHandler<ListJobRunsRequest,ListJobRunsResult> asyncHandler)
Lists 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.
listJobRunsRequest - asyncHandler - 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.Future<ListJobTemplatesResult> listJobTemplatesAsync(ListJobTemplatesRequest listJobTemplatesRequest)
Lists 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.
listJobTemplatesRequest - Future<ListJobTemplatesResult> listJobTemplatesAsync(ListJobTemplatesRequest listJobTemplatesRequest, AsyncHandler<ListJobTemplatesRequest,ListJobTemplatesResult> asyncHandler)
Lists 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.
listJobTemplatesRequest - asyncHandler - 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.Future<ListManagedEndpointsResult> listManagedEndpointsAsync(ListManagedEndpointsRequest listManagedEndpointsRequest)
Lists 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.
listManagedEndpointsRequest - Future<ListManagedEndpointsResult> listManagedEndpointsAsync(ListManagedEndpointsRequest listManagedEndpointsRequest, AsyncHandler<ListManagedEndpointsRequest,ListManagedEndpointsResult> asyncHandler)
Lists 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.
listManagedEndpointsRequest - asyncHandler - 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.Future<ListTagsForResourceResult> listTagsForResourceAsync(ListTagsForResourceRequest listTagsForResourceRequest)
Lists the tags assigned to the resources.
listTagsForResourceRequest - Future<ListTagsForResourceResult> listTagsForResourceAsync(ListTagsForResourceRequest listTagsForResourceRequest, AsyncHandler<ListTagsForResourceRequest,ListTagsForResourceResult> asyncHandler)
Lists the tags assigned to the resources.
listTagsForResourceRequest - asyncHandler - 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.Future<ListVirtualClustersResult> listVirtualClustersAsync(ListVirtualClustersRequest listVirtualClustersRequest)
Lists 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.
listVirtualClustersRequest - Future<ListVirtualClustersResult> listVirtualClustersAsync(ListVirtualClustersRequest listVirtualClustersRequest, AsyncHandler<ListVirtualClustersRequest,ListVirtualClustersResult> asyncHandler)
Lists 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.
listVirtualClustersRequest - asyncHandler - 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.Future<StartJobRunResult> startJobRunAsync(StartJobRunRequest startJobRunRequest)
Starts 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.
startJobRunRequest - Future<StartJobRunResult> startJobRunAsync(StartJobRunRequest startJobRunRequest, AsyncHandler<StartJobRunRequest,StartJobRunResult> asyncHandler)
Starts 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.
startJobRunRequest - asyncHandler - 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.Future<TagResourceResult> tagResourceAsync(TagResourceRequest tagResourceRequest)
Assigns 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.
tagResourceRequest - Future<TagResourceResult> tagResourceAsync(TagResourceRequest tagResourceRequest, AsyncHandler<TagResourceRequest,TagResourceResult> asyncHandler)
Assigns 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.
tagResourceRequest - asyncHandler - 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.Future<UntagResourceResult> untagResourceAsync(UntagResourceRequest untagResourceRequest)
Removes tags from resources.
untagResourceRequest - Future<UntagResourceResult> untagResourceAsync(UntagResourceRequest untagResourceRequest, AsyncHandler<UntagResourceRequest,UntagResourceResult> asyncHandler)
Removes tags from resources.
untagResourceRequest - asyncHandler - 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.