# Creating an interactive endpoint for your virtual cluster
<a name="create-managed-endpoint"></a>

This topic describes a couple ways to create an interactive endpoint using the AWS Command Line Interface (AWS CLI) and includes details on available configuration parameters.

## Create an interactive endpoint with the `create-managed-endpoint` command
<a name="create-using-json-file"></a>

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 \
‐‐name example-endpoint-name \
‐‐execution-role-arn arn:aws:iam::444455556666:role/JobExecutionRole \
‐‐release-label emr-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](#parameters-for-creating).

## Create an interactive endpoint with specified parameters in a JSON file
<a name="create-using-json-file-B"></a>

1. 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"
               }
           }
       }
   }
   ```

1. Use the `create-managed-endpoint` command with a path to the `create-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
<a name="create-managed-endpoint-output"></a>

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
<a name="parameters-for-creating"></a>

**Topics**
+ [Required parameters for interactive endpoints](#parameters-for-creating-required)
+ [Optional parameters for interactive endpoints](#parameters-for-creating-optional)

### Required parameters for interactive endpoints
<a name="parameters-for-creating-required"></a>

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
<a name="parameters-for-creating-optional"></a>

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](emr-eks-releases.md). 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](jeg-config-options.md).