emrcontainers/aws.sdk.kotlin.services.emrcontainers

Package-level declarations

Types

interface EmrContainersClient : SdkClient

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 Amazon EMR on EKS.

Properties

SdkVersion

const val SdkVersion: String

ServiceApiVersion

const val ServiceApiVersion: String

ServiceId

const val ServiceId: String

Inherited functions

cancelJobRun

inline suspend fun EmrContainersClient.cancelJobRun(crossinline block: CancelJobRunRequest.Builder.() -> Unit): CancelJobRunResponse

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.

createJobTemplate

inline suspend fun EmrContainersClient.createJobTemplate(crossinline block: CreateJobTemplateRequest.Builder.() -> Unit): CreateJobTemplateResponse

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.

createManagedEndpoint

inline suspend fun EmrContainersClient.createManagedEndpoint(crossinline block: CreateManagedEndpointRequest.Builder.() -> Unit): CreateManagedEndpointResponse

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.

createSecurityConfiguration

inline suspend fun EmrContainersClient.createSecurityConfiguration(crossinline block: CreateSecurityConfigurationRequest.Builder.() -> Unit): CreateSecurityConfigurationResponse

Creates 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.

createVirtualCluster

inline suspend fun EmrContainersClient.createVirtualCluster(crossinline block: CreateVirtualClusterRequest.Builder.() -> Unit): CreateVirtualClusterResponse

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.

deleteJobTemplate

inline suspend fun EmrContainersClient.deleteJobTemplate(crossinline block: DeleteJobTemplateRequest.Builder.() -> Unit): DeleteJobTemplateResponse

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.

deleteManagedEndpoint

inline suspend fun EmrContainersClient.deleteManagedEndpoint(crossinline block: DeleteManagedEndpointRequest.Builder.() -> Unit): DeleteManagedEndpointResponse

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.

deleteVirtualCluster

inline suspend fun EmrContainersClient.deleteVirtualCluster(crossinline block: DeleteVirtualClusterRequest.Builder.() -> Unit): DeleteVirtualClusterResponse

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.

describeJobRun

inline suspend fun EmrContainersClient.describeJobRun(crossinline block: DescribeJobRunRequest.Builder.() -> Unit): DescribeJobRunResponse

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.

describeJobTemplate

inline suspend fun EmrContainersClient.describeJobTemplate(crossinline block: DescribeJobTemplateRequest.Builder.() -> Unit): DescribeJobTemplateResponse

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.

describeManagedEndpoint

inline suspend fun EmrContainersClient.describeManagedEndpoint(crossinline block: DescribeManagedEndpointRequest.Builder.() -> Unit): DescribeManagedEndpointResponse

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.

describeSecurityConfiguration

inline suspend fun EmrContainersClient.describeSecurityConfiguration(crossinline block: DescribeSecurityConfigurationRequest.Builder.() -> Unit): DescribeSecurityConfigurationResponse

Displays 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.

describeVirtualCluster

inline suspend fun EmrContainersClient.describeVirtualCluster(crossinline block: DescribeVirtualClusterRequest.Builder.() -> Unit): DescribeVirtualClusterResponse

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.

getManagedEndpointSessionCredentials

inline suspend fun EmrContainersClient.getManagedEndpointSessionCredentials(crossinline block: GetManagedEndpointSessionCredentialsRequest.Builder.() -> Unit): GetManagedEndpointSessionCredentialsResponse

Generate a session token to connect to a managed endpoint.

listJobRuns

inline suspend fun EmrContainersClient.listJobRuns(crossinline block: ListJobRunsRequest.Builder.() -> Unit): ListJobRunsResponse

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.

listJobTemplates

inline suspend fun EmrContainersClient.listJobTemplates(crossinline block: ListJobTemplatesRequest.Builder.() -> Unit): ListJobTemplatesResponse

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.

listManagedEndpoints

inline suspend fun EmrContainersClient.listManagedEndpoints(crossinline block: ListManagedEndpointsRequest.Builder.() -> Unit): ListManagedEndpointsResponse

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.

listSecurityConfigurations

inline suspend fun EmrContainersClient.listSecurityConfigurations(crossinline block: ListSecurityConfigurationsRequest.Builder.() -> Unit): ListSecurityConfigurationsResponse

Lists 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.

listTagsForResource

inline suspend fun EmrContainersClient.listTagsForResource(crossinline block: ListTagsForResourceRequest.Builder.() -> Unit): ListTagsForResourceResponse

Lists the tags assigned to the resources.

listVirtualClusters

inline suspend fun EmrContainersClient.listVirtualClusters(crossinline block: ListVirtualClustersRequest.Builder.() -> Unit): ListVirtualClustersResponse

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.

startJobRun

inline suspend fun EmrContainersClient.startJobRun(crossinline block: StartJobRunRequest.Builder.() -> Unit): StartJobRunResponse

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.

tagResource

inline suspend fun EmrContainersClient.tagResource(crossinline block: TagResourceRequest.Builder.() -> Unit): TagResourceResponse

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.

untagResource

inline suspend fun EmrContainersClient.untagResource(crossinline block: UntagResourceRequest.Builder.() -> Unit): UntagResourceResponse

Removes tags from resources.

withConfig

fun EmrContainersClient.withConfig(block: EmrContainersClient.Config.Builder.() -> Unit): EmrContainersClient

Create a copy of the client with one or more configuration values overridden. This method allows the caller to perform scoped config overrides for one or more client operations.