AWS.EMRcontainers — AWS SDK for JavaScript (original) (raw)

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Overview

Constructs a service interface object. Each API operation is exposed as a function on service.

Service Description

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.

Amazon EMR containers is the API name for Amazon EMR on EKS. The emr-containers prefix is used in the following scenarios:

Sending a Request Using EMRcontainers

var emrcontainers = new AWS.EMRcontainers();
emrcontainers.cancelJobRun(params, function (err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Locking the API Version

In order to ensure that the EMRcontainers object uses this specific API, you can construct the object by passing the apiVersion option to the constructor:

var emrcontainers = new AWS.EMRcontainers({apiVersion: '2020-10-01'});

You can also set the API version globally in AWS.config.apiVersions using the emrcontainers service identifier:

AWS.config.apiVersions = {
  emrcontainers: '2020-10-01',
  // other service API versions
};

var emrcontainers = new AWS.EMRcontainers();

Constructor Summarycollapse

Property Summarycollapse

Properties inherited from AWS.Service

apiVersions

Method Summarycollapse

Methods inherited from AWS.Service

makeRequest, makeUnauthenticatedRequest, waitFor, setupRequestListeners, defineService

Constructor Details

new AWS.EMRcontainers(options = {}) ⇒ Object

Constructs a service object. This object has one method for each API operation.

Property Details

endpoint ⇒ AWS.Endpoint

Returns an Endpoint object representing the endpoint URL for service requests.

Method Details

cancelJobRun(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

Generate a session token to connect to a managed endpoint.

listJobRuns(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

Lists the tags assigned to the resources.

listVirtualClusters(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

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(params = {}, callback) ⇒ AWS.Request

Removes tags from resources.