Emr Containers

cancel_job_run(job_run_id, virtual_cluster_id)
cancel_job_run(job_run_id, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• job_run_id: The ID of the job run to cancel.
• virtual_cluster_id: The ID of the virtual cluster for which the job run will be canceled.

create_job_template(client_token, job_template_data, name)
create_job_template(client_token, job_template_data, name, params::Dict{String,<:Any})

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.

Arguments

• client_token: The client token of the job template.
• job_template_data: The job template data which holds values of StartJobRun API request.
• name: The specified name of the job template.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "kmsKeyArn": The KMS key ARN used to encrypt the job template.
• "tags": The tags that are associated with the job template.

create_managed_endpoint(client_token, execution_role_arn, name, release_label, type, virtual_cluster_id)
create_managed_endpoint(client_token, execution_role_arn, name, release_label, type, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• client_token: The client idempotency token for this create call.
• execution_role_arn: The ARN of the execution role.
• name: The name of the managed endpoint.
• release_label: The Amazon EMR release version.
• type: The type of the managed endpoint.
• virtual_cluster_id: The ID of the virtual cluster for which a managed endpoint is created.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "certificateArn": The certificate ARN provided by users for the managed endpoint. This field is under deprecation and will be removed in future releases.
• "configurationOverrides": The configuration settings that will be used to override existing configurations.
• "tags": The tags of the managed endpoint.

create_security_configuration(client_token, name, security_configuration_data)
create_security_configuration(client_token, name, security_configuration_data, params::Dict{String,<:Any})

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.

Arguments

• client_token: The client idempotency token to use when creating the security configuration.
• name: The name of the security configuration.
• security_configuration_data: Security configuration input for the request.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "tags": The tags to add to the security configuration.

create_virtual_cluster(client_token, container_provider, name)
create_virtual_cluster(client_token, container_provider, name, params::Dict{String,<:Any})

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.

Arguments

• client_token: The client token of the virtual cluster.
• container_provider: The container provider of the virtual cluster.
• name: The specified name of the virtual cluster.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "securityConfigurationId": The ID of the security configuration.
• "tags": The tags assigned to the virtual cluster.

delete_job_template(template_id)
delete_job_template(template_id, params::Dict{String,<:Any})

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.

Arguments

• template_id: The ID of the job template that will be deleted.

delete_managed_endpoint(endpoint_id, virtual_cluster_id)
delete_managed_endpoint(endpoint_id, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• endpoint_id: The ID of the managed endpoint.
• virtual_cluster_id: The ID of the endpoint's virtual cluster.

delete_virtual_cluster(virtual_cluster_id)
delete_virtual_cluster(virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• virtual_cluster_id: The ID of the virtual cluster that will be deleted.

describe_job_run(job_run_id, virtual_cluster_id)
describe_job_run(job_run_id, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• job_run_id: The ID of the job run request.
• virtual_cluster_id: The ID of the virtual cluster for which the job run is submitted.

describe_job_template(template_id)
describe_job_template(template_id, params::Dict{String,<:Any})

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.

Arguments

• template_id: The ID of the job template that will be described.

describe_managed_endpoint(endpoint_id, virtual_cluster_id)
describe_managed_endpoint(endpoint_id, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• endpoint_id: This output displays ID of the managed endpoint.
• virtual_cluster_id: The ID of the endpoint's virtual cluster.

describe_security_configuration(security_configuration_id)
describe_security_configuration(security_configuration_id, params::Dict{String,<:Any})

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.

Arguments

• security_configuration_id: The ID of the security configuration.

describe_virtual_cluster(virtual_cluster_id)
describe_virtual_cluster(virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• virtual_cluster_id: The ID of the virtual cluster that will be described.

get_managed_endpoint_session_credentials(credential_type, endpoint_id, execution_role_arn, virtual_cluster_id)
get_managed_endpoint_session_credentials(credential_type, endpoint_id, execution_role_arn, virtual_cluster_id, params::Dict{String,<:Any})

Generate a session token to connect to a managed endpoint.

Arguments

• credential_type: Type of the token requested. Currently supported and default value of this field is “TOKEN.”
• endpoint_id: The ARN of the managed endpoint for which the request is submitted.
• execution_role_arn: The IAM Execution Role ARN that will be used by the job run.
• virtual_cluster_id: The ARN of the Virtual Cluster which the Managed Endpoint belongs to.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "clientToken": The client idempotency token of the job run request.
• "durationInSeconds": Duration in seconds for which the session token is valid. The default duration is 15 minutes and the maximum is 12 hours.
• "logContext": String identifier used to separate sections of the execution logs uploaded to S3.

list_job_runs(virtual_cluster_id)
list_job_runs(virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• virtual_cluster_id: The ID of the virtual cluster for which to list the job run.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "createdAfter": The date and time after which the job runs were submitted.
• "createdBefore": The date and time before which the job runs were submitted.
• "maxResults": The maximum number of job runs that can be listed.
• "name": The name of the job run.
• "nextToken": The token for the next set of job runs to return.
• "states": The states of the job run.

list_job_templates()
list_job_templates(params::Dict{String,<:Any})

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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "createdAfter": The date and time after which the job templates were created.
• "createdBefore": The date and time before which the job templates were created.
• "maxResults": The maximum number of job templates that can be listed.
• "nextToken": The token for the next set of job templates to return.

list_managed_endpoints(virtual_cluster_id)
list_managed_endpoints(virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• virtual_cluster_id: The ID of the virtual cluster.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "createdAfter": The date and time after which the endpoints are created.
• "createdBefore": The date and time before which the endpoints are created.
• "maxResults": The maximum number of managed endpoints that can be listed.
• "nextToken": The token for the next set of managed endpoints to return.
• "states": The states of the managed endpoints.
• "types": The types of the managed endpoints.

list_security_configurations()
list_security_configurations(params::Dict{String,<:Any})

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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "createdAfter": The date and time after which the security configuration was created.
• "createdBefore": The date and time before which the security configuration was created.
• "maxResults": The maximum number of security configurations the operation can list.
• "nextToken": The token for the next set of security configurations to return.

list_tags_for_resource(resource_arn)
list_tags_for_resource(resource_arn, params::Dict{String,<:Any})

Lists the tags assigned to the resources.

Arguments

• resource_arn: The ARN of tagged resources.

list_virtual_clusters()
list_virtual_clusters(params::Dict{String,<:Any})

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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "containerProviderId": The container provider ID of the virtual cluster.
• "containerProviderType": The container provider type of the virtual cluster. Amazon EKS is the only supported type as of now.
• "createdAfter": The date and time after which the virtual clusters are created.
• "createdBefore": The date and time before which the virtual clusters are created.
• "eksAccessEntryIntegrated": Optional Boolean that specifies whether the operation should return the virtual clusters that have the access entry integration enabled or disabled. If not specified, the operation returns all applicable virtual clusters.
• "maxResults": The maximum number of virtual clusters that can be listed.
• "nextToken": The token for the next set of virtual clusters to return.
• "states": The states of the requested virtual clusters.

start_job_run(client_token, virtual_cluster_id)
start_job_run(client_token, virtual_cluster_id, params::Dict{String,<:Any})

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.

Arguments

• client_token: The client idempotency token of the job run request.
• virtual_cluster_id: The virtual cluster ID for which the job run request is submitted.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

• "configurationOverrides": The configuration overrides for the job run.
• "executionRoleArn": The execution role ARN for the job run.
• "jobDriver": The job driver for the job run.
• "jobTemplateId": The job template ID to be used to start the job run.
• "jobTemplateParameters": The values of job template parameters to start a job run.
• "name": The name of the job run.
• "releaseLabel": The Amazon EMR release version to use for the job run.
• "retryPolicyConfiguration": The retry policy configuration for the job run.
• "tags": The tags assigned to job runs.

tag_resource(resource_arn, tags)
tag_resource(resource_arn, tags, params::Dict{String,<:Any})

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.

Arguments

• resource_arn: The ARN of resources.
• tags: The tags assigned to resources.

untag_resource(resource_arn, tag_keys)
untag_resource(resource_arn, tag_keys, params::Dict{String,<:Any})

Removes tags from resources.

Arguments

• resource_arn: The ARN of resources.
• tag_keys: The tag keys of the resources.