Class: AWS.EMRcontainers
- Inherits:
-
AWS.Service
- Object
- AWS.Service
- AWS.EMRcontainers
- Identifier:
- emrcontainers
- API Version:
- 2020-10-01
- Defined in:
- (unknown)
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:
-
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 EKS Service Endpoints.
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 Summary collapse
-
new AWS.EMRcontainers(options = {}) ⇒ Object
constructor
Constructs a service object.
Property Summary collapse
-
endpoint ⇒ AWS.Endpoint
readwrite
An Endpoint object representing the endpoint URL for service requests.
Properties inherited from AWS.Service
Method Summary collapse
-
cancelJobRun(params = {}, callback) ⇒ AWS.Request
Cancels a job run.
-
createManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Creates a managed endpoint.
-
createVirtualCluster(params = {}, callback) ⇒ AWS.Request
Creates a virtual cluster.
-
deleteManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Deletes a managed endpoint.
-
deleteVirtualCluster(params = {}, callback) ⇒ AWS.Request
Deletes a virtual cluster.
-
describeJobRun(params = {}, callback) ⇒ AWS.Request
Displays detailed information about a job run.
-
describeManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Displays detailed information about a managed endpoint.
-
describeVirtualCluster(params = {}, callback) ⇒ AWS.Request
Displays detailed information about a specified virtual cluster.
-
listJobRuns(params = {}, callback) ⇒ AWS.Request
Lists job runs based on a set of parameters.
-
listManagedEndpoints(params = {}, callback) ⇒ AWS.Request
Lists managed endpoints based on a set of parameters.
-
listTagsForResource(params = {}, callback) ⇒ AWS.Request
Lists the tags assigned to the resources.
.
-
listVirtualClusters(params = {}, callback) ⇒ AWS.Request
Lists information about the specified virtual cluster.
-
startJobRun(params = {}, callback) ⇒ AWS.Request
Starts a job run.
-
tagResource(params = {}, callback) ⇒ AWS.Request
Assigns tags to resources.
-
untagResource(params = {}, callback) ⇒ AWS.Request
Removes tags from resources.
.
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
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.
createManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Creates a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your 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.
deleteManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Deletes a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that 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.
describeManagedEndpoint(params = {}, callback) ⇒ AWS.Request
Displays detailed information about a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your 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.
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.
listManagedEndpoints(params = {}, callback) ⇒ AWS.Request
Lists managed endpoints based on a set of parameters. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
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 AWS resource. Each tag consists of a key and an optional value, both of which you define. Tags enable you to categorize your AWS 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.