Creating an interactive endpoint for your virtual cluster
This page describes how to create an interactive endpoint using the Amazon Command Line Interface (Amazon CLI).
Create an interactive endpoint with the
create-managed-endpoint
command
Specify the parameters in the create-managed-endpoint
command as follows.
Amazon EMR on EKS supports creating interactive endpoints with Amazon EMR releases 6.7.0 and
higher.
aws emr-containers create-managed-endpoint \ ‐‐type JUPYTER_ENTERPRISE_GATEWAY \ ‐‐virtual‐cluster‐id
1234567890abcdef0xxxxxxxx
\ ‐‐nameexample-endpoint-name
\ ‐‐execution-role-arn arn:aws:iam::444455556666:role/JobExecutionRole
\ ‐‐release-labelemr-6.9.0-latest
\ ‐‐configuration-overrides '{ "applicationConfiguration": [{ "classification": "spark-defaults", "properties": { "spark.driver.memory": "2G" } }], "monitoringConfiguration": { "cloudWatchMonitoringConfiguration": { "logGroupName": "log_group_name
", "logStreamNamePrefix": "log_stream_prefix
" }, "persistentAppUI": "ENABLED", "s3MonitoringConfiguration": { "logUri": "s3://my_s3_log_location
" } } }'
For more information, see Parameters for creating an interactive endpoint.
Create an interactive endpoint with specified parameters in a JSON file
-
Create a
create-managed-endpoint-request.json
file and specify the required parameters for your endpoint, as shown in the following JSON file:{ "name": "
MY_TEST_ENDPOINT
", "virtualClusterId": "MY_CLUSTER_ID
", "type": "JUPYTER_ENTERPRISE_GATEWAY", "releaseLabel": "emr-6.9.0-latest
", "executionRoleArn": "arn:aws:iam::444455556666:role/JobExecutionRole
", "configurationOverrides": { "applicationConfiguration": [ { "classification": "spark-defaults", "properties": { "spark.driver.memory": "8G" } } ], "monitoringConfiguration": { "persistentAppUI": "ENABLED", "cloudWatchMonitoringConfiguration": { "logGroupName": "my_log_group
", "logStreamNamePrefix": "log_stream_prefix
" }, "s3MonitoringConfiguration": { "logUri": "s3://my_s3_log_location
" } } } } -
Use the
create-managed-endpoint
command with a path to thecreate-managed-endpoint-request.json
file that is stored locally or in Amazon S3.aws emr-containers create-managed-endpoint \ ‐‐cli-input-json file://./create-managed-endpoint-request.json ‐‐region
AWS-Region
Output of create interactive endpoint
You should see the following output in the terminal. The output includes the name and identifier of your new interactive endpoint:
{
"id": "1234567890abcdef0
",
"name": "example-endpoint-name
",
"arn": "arn:aws:emr-containers:us-west-2:111122223333
:/virtualclusters/444455556666
/endpoints/444455556666
",
"virtualClusterId": "111122223333xxxxxxxx
"
}
Running aws emr-containers create-managed-endpoint
creates a self-signed
certificate that allows HTTPS communication between EMR Studio and the interactive
endpoint server.
If you run create-managed-endpoint
and haven't completed the prerequisites,
Amazon EMR returns an error message with the actions that you must take to continue.
Parameters for creating an interactive endpoint
Required parameters for interactive endpoints
You must specify the following parameters when you create an interactive endpoint:
‐‐type
-
Use
JUPYTER_ENTERPRISE_GATEWAY
. This is the only supported type. ‐‐virtual-cluster-id
-
The identifier of the virtual cluster that you registered with Amazon EMR on EKS.
‐‐name
-
A descriptive name for the interactive endpoint that helps EMR Studio users select it from the dropdown list.
‐‐execution-role-arn
-
The Amazon Resource Name (ARN) of your IAM job execution role for Amazon EMR on EKS that was created as part of the prerequisites.
‐‐release-label
-
The release label of the Amazon EMR release to use for the endpoint. For example,
emr-6.9.0-latest
. Amazon EMR on EKS supports interactive endpoints with Amazon EMR releases 6.7.0 and higher.
Optional parameters for interactive endpoints
Optionally, you can also specify the following parameters when you create an interactive endpoint:
‐‐configuration-overrides
-
To override the default configurations for applications, supply a coonfiguration object. You can use a shorthand syntax to provide the configuration, or you can reference the configuration object in a JSON file.
Configuration objects consist of a classification, properties, and optional
nested configurations. Properties consist of the settings that you want to override
in that file. You can specify multiple classifications for multiple applications in
a single JSON object. The configuration classifications that are available vary by
Amazon EMR on EKS release. For a list of configuration classifications that are available
for each release of Amazon EMR on EKS, see Amazon EMR on EKS releases. In addition to the configuration
classifications listed for each release, interactive endpoints bring in the
additional classification jeg-config
. For more information, see Jupyter Enterprise Gateway (JEG) configuration
options.