# Running jobs from the EMR Studio console
You can submit job runs to EMR Serverless applications and access the jobs from the EMR Studio console. To create or navigate to your EMR Serverless application on the EMR Studio console, follow the instructions in [Getting started from the console](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/getting-started.html#gs-console).
## Submit a job
On the **Submit job** page, submit a job to an EMR Serverless application as follows.
------
#### [ Spark ]
1. In the **Name** field, enter a name for your job run.
1. In the **Runtime role** field, enter the name of the IAM role that your EMR Serverless application can assume for the job run. To learn more about runtime roles, refer to [Job runtime roles for Amazon EMR Serverless](security-iam-runtime-role.md).
1. In the **Script location** field, enter the Amazon S3 location for the script or JAR that you want to run. For Spark jobs, the script can be a Python (`.py`) file or a JAR (`.jar`) file.
1. If your script location is a JAR file, enter the class name that is the entry point for the job in the **Main class** field.
1. (Optional) Enter values for the remaining fields.
+ **Script arguments** — Enter any arguments that you want to pass to your main JAR or Python script. Your code reads these parameters. Separate each argument in the array by a comma.
+ **Spark properties** — Expand the Spark properties section and enter any Spark configuration parameters in this field.
**Note**
If you specify Spark driver and executor sizes, take memory overhead into account. Specify memory overhead values in the properties `spark.driver.memoryOverhead` and `spark.executor.memoryOverhead`. Memory overhead has a default value of 10% of container memory, with a minimum of 384 MB. The executor memory and the memory overhead together can't exceed the worker memory. For example, the maximum `spark.executor.memory` on a 30 GB worker must be 27 GB.
+ **Job configuration** — Specify any job configuration in this field. You can use these job configurations to override the default configurations for applications. The following example shows how to override Spark default settings such as executor and driver memory.
```
{
"applicationConfiguration": [
{
"classification": "spark-defaults",
"configurations": [],
"properties": {
"spark.executor.memory": "8G",
"spark.driver.memory": "6G",
"spark.driver.cores": "2",
"spark.executor.cores": "4"
}
}
]
}
```
+ **Additional settings** — Activate or deactivate the AWS Glue Data Catalog as a metastore and modify application log settings. To learn more about metastore configurations, refer to [Metastore configuration for EMR Serverless](metastore-config.md). To learn more about application logging options, refer to [Storing logs](logging.md).
+ **Tags** — Assign custom tags to the application.
1. Choose **Submit job**.
------
#### [ Hive ]
1. In the **Name** field, enter a name for your job run.
1. In the **Runtime role** field, enter the name of the IAM role that your EMR Serverless application can assume for the job run.
1. In the **Script location** field, enter the Amazon S3 location for the script or JAR that you want to run. For Hive jobs, the script must be a Hive (`.sql`) file.
1. (Optional) Enter values for the remaining fields.
+ **Initialization script location** – Enter the location of the script that initializes tables before the Hive script runs.
+ **Hive properties** – Expand the Hive properties section and enter any Hive configuration parameters in this field.
+ **Job configuration** – Specify any job configuration. You can use these job configurations to override the default configurations for applications. For Hive jobs, `hive.exec.scratchdir` and `hive.metastore.warehouse.dir` are required properties in the `hive-site` configuration.
```
{
"applicationConfiguration": [
{
"classification": "hive-site",
"configurations": [],
"properties": {
"hive.exec.scratchdir": "s3://{{DOC-EXAMPLE_BUCKET}}/hive/scratch",
"hive.metastore.warehouse.dir": "s3://{{DOC-EXAMPLE_BUCKET}}/hive/warehouse"
}
}
],
"monitoringConfiguration": {}
}
```
+ **Additional settings** — Activate or deactivate the AWS Glue Data Catalog as a metastore and modify application log settings. To learn more about metastore configurations, refer to [Metastore configuration for EMR Serverless](metastore-config.md). To learn more about application logging options, refer to [Storing logs](logging.md).
+ **Tags** — Assign any custom tags to the application.
1. Choose **Submit job**.
------
## Access job runs
From the **Job runs** tab on an application’s **Details** page, access job runs and perform the following actions for job runs.
**Cancel job** — To cancel a job run that is in the `RUNNING` state, choose this option. To learn more about job run transitions, refer to [Job run states](job-states.md).
**Clone job** — To clone a previous job run and resubmit it, choose this option.