Spark jobs

You can run Spark jobs on an application with the type parameter set to 'SPARK'. Jobs must be compatible with the Spark version referenced in the Amazon EMR release version. For example, when you run jobs on an application with Amazon EMR release 6.6.0, your job must be compatible with Apache Spark 3.2.0.

To run a Spark job, specify the following parameters when you use the start-job-run API.

Job runtime role (executionRoleArn)

This role is an IAM role ARN that your application uses to execute Spark jobs. This role must contain the following permissions:

Read from S3 buckets or other data sources where your data resides
Read from S3 buckets or prefixes where your PySpark script or JAR file resides
Write to S3 buckets where you intend to write your final output
Write logs to a S3 bucket or prefix that S3MonitoringConfigurationspecifies
Access to KMS keys if you use KMS keys to encrypt data in your S3 bucket
Access to the AWS Glue Data Catalog if you use SparkSQL

If your Spark job reads or writes data to or from other data sources, specify the appropriate permissions in this IAM role. If you don't provide these permissions to the IAM role, the job might fail. For more information, see Job runtime roles and Storing logs.

Job driver (jobDriver)

Use a job driver to provide input to the job. This parameter accepts only one value for the job type that you want to run. For a Spark job, the parameter value is sparkSubmit. You can use this job type to run Scala, Java, PySpark, SparkR, and any other supported jobs through Spark submit. Spark jobs have the following parameters:

sparkSubmitParameters – These are the additional Spark parameters that you want to send to the job. Use this parameter to override default Spark properties such as driver memory or number of executors, like those defined in the --conf or --class parameters.
entryPointArguments – This is an array of arguments that you want to pass to your main JAR or Python file. You should handle reading these parameters using your entrypoint code. Separate each argument in the array by a comma.
entryPoint – This is the reference in Amazon S3 to the main JAR or Python file that you want to run. If you are running a Scala or Java JAR, specify the main entry class in the SparkSubmitParameters using the --class argument.

For additional information, see Launching Applications with spark-submit.

Configuration overrides (configurationOverrides)

Use this parameter to override application and monitoring level configuration properties. This parameter accepts a JSON object with the following two fields:

applicationConfiguration – To override the default configurations for applications, you can provide a configuration object in this field. 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 you can use vary by specific release version for Amazon EMR. For a list of configuration classifications that are available for each release version of Amazon EMR, see Release versions.

If you use the same configuration in an application override and in Spark submit parameters, the Spark submit parameters take priority. Configurations rank in priority as follows, from highest to lowest:
- Configuration that EMR Serverless provides when it creates SparkSession.
- Configuration that you provide as part of sparkSubmitParameters with the --conf argument.
- Configuration that you provide as part of your application overrides.
- Optimized configurations that Amazon EMR uses for the release.
- Default open source configurations for the application.
monitoringConfiguration ‐ Use this field to specify the Amazon S3 URL (s3MonitoringConfiguration) where you want the EMR Serverless job to store logs of your Spark job. Make sure you've created this bucket with the same AWS account that hosts your application, and in the same AWS Region where your job is running.

Spark job properties

The following table lists optional Spark properties and their default values that you can override when you submit a Spark job.

Key	Description	Default value
`spark.archives`	A comma-separated list of archives that Spark extracts into each executor's working directory. Supported file types include `.jar`,`.tar.gz`, `.tgz` and `.zip`. To specify the directory name to extract, add `#` after the file name that you want to extract. For example, `file.zip#directory`. This configuration is experimental.	`NULL`
`spark.authenticate`	Option that turns on authentication of Spark's internal connections.	`TRUE`
`spark.driver.cores`	The number of cores that the driver uses.	4
`spark.driver.extraJavaOptions`	Extra Java options for the Spark driver.	`NULL`
`spark.driver.memory`	The amount of memory that the driver uses.	14G
`spark.dynamicAllocation.enabled`	Option that turns on dynamic resource allocation. This option scales up or down the number of executors registered with the application, based on the workload.	`TRUE`
`spark.dynamicAllocation.executorIdleTimeout`	The length of time that an executor can remain idle before Spark removes it. This only applies if you turn on dynamic allocation.	60s
`spark.dynamicAllocation.initialExecutors`	The initial number of executors to run if you turn on dynamic allocation.	3
`spark.dynamicAllocation.maxExecutors`	The upper bound for the number of executors if you turn on dynamic allocation.	100
`spark.dynamicAllocation.minExecutors`	The lower bound for the number of executors if you turn on dynamic allocation.	0
`spark.emr-serverless.allocation.batch.size`	The number of containers to request in each cycle of executor allocation. There is a one-second gap between each allocation cycle.	20
`spark.emr-serverless.allocation.executor.timeout`	The time to wait for a newly-created executor container to reach the running state before EMR Serverless cancels the request.	300s
`spark.emr-serverless.driver.disk`	The Spark driver disk.	20G
`spark.emr-serverless.driverEnv.[KEY]`	Option that adds environment variables to the Spark driver.	`NULL`
`spark.emr-serverless.executor.disk`	The Spark executor disk.	20G
`spark.emr-serverless.memoryOverheadFactor`	Sets the memory overhead to add to the driver and executor container memory.	0.1
`spark.executor.cores`	The number of cores that each executor uses.	4
`spark.executor.extraJavaOptions`	Extra Java options for the Spark executor.	`NULL`
`spark.executor.instances`	The number of Spark executor containers to allocate.	3
`spark.executor.memory`	The amount of memory that each executor uses.	14G
`spark.executorEnv.[KEY]`	Option that adds environment variables to the Spark executors.	`NULL`
`spark.files`	A comma-separated list of files to go in the working directory of each executor. You can access the file paths of these files in the executor with `SparkFiles.get(fileName)`.	`NULL`
`spark.hadoop.hive.metastore.client.factory.class`	The Hive metastore implementation class.	`NULL`
`spark.jars`	Additional jars to add to the runtime classpath of the driver and executors.	`NULL`
`spark.network.crypto.enabled`	Option that turns on AES-based RPC encryption. This includes the authentication protocol added in Spark 2.2.0.	`FALSE`
`spark.sql.warehouse.dir`	The default location for managed databases and tables.	The value of `$PWD/spark-warehouse`
`spark.submit.pyFiles`	A comma-separated list of `.zip`, `.egg`, or`.py` files to place in the `PYTHONPATH` for Python apps.	`NULL`

The following table lists the default Spark submit parameters.

Key	Description	Default value
`archives`	A comma-separated list of archives that Spark extracts into each executor's working directory.	`NULL`
`class`	The application's main class (for Java and Scala apps).	`NULL`
`conf`	An arbitrary Spark configuration property.	`NULL`
`driver-cores`	The number of cores that the driver uses.	4
`driver-memory`	The amount of memory that the driver uses.	14G
`executor-cores`	The number of cores that each executor uses.	4
`executor-memory`	The amount of memory that the executor uses.	14G
`files`	A comma-separated list of files to place in the working directory of each executor. You can access the file paths of these files in the executor with `SparkFiles.get(fileName)`.	`NULL`
`jars`	A comma-separated list of jars to include on the driver and executor classpaths.	`NULL`
`num-executors`	The number of executors to launch.	3
`py-files`	A comma-separated list of `.zip`, `.egg`, or `.py` files to place on the `PYTHONPATH` for Python apps.	`NULL`
`verbose`	Option that turns on additional debug output.	`NULL`

Spark examples

The following example shows how to use the StartJobRun API to run a Python script. For an end-to-end tutorial that uses this example, see Getting started with Amazon EMR Serverless. You can find additional examples of how to run PySpark jobs and add Python dependencies in the EMR Serverless Samples GitHub repository.


aws emr-serverless start-job-run \
    --application-id application-id \
    --execution-role-arn job-role-arn \
    --job-driver '{
        "sparkSubmit": {
            "entryPoint": "s3://us-east-1.elasticmapreduce/emr-containers/samples/wordcount/scripts/wordcount.py",
            "entryPointArguments": ["s3://DOC-EXAMPLE-BUCKET-OUTPUT/wordcount_output"],
            "sparkSubmitParameters": "--conf spark.executor.cores=1 --conf spark.executor.memory=4g --conf spark.driver.cores=1 --conf spark.driver.memory=4g --conf spark.executor.instances=1"
        }
    }'

The following example shows how to use the StartJobRun API to run a Spark JAR.


aws emr-serverless start-job-run \
    --application-id application-id \
    --execution-role-arn job-role-arn \
    --job-driver '{
        "sparkSubmit": {
            "entryPoint": "/usr/lib/spark/examples/jars/spark-examples.jar",
            "entryPointArguments": ["1"],
            "sparkSubmitParameters": "--class org.apache.spark.examples.SparkPi --conf spark.executor.cores=4 --conf spark.executor.memory=20g --conf spark.driver.cores=4 --conf spark.driver.memory=8g --conf spark.executor.instances=1"
        }
    }'

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