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Default application configuration for EMR Serverless

You can specify a common set of runtime and monitoring configurations at the application level for all the jobs that you submit under the same application. This reduces the additional overhead that is associated with the need to submit the same configurations for each job.

You can modify the configurations at the following points in time:

The following sections provide more details and an example for further context.

Declaring configurations at the application level

You can specify application-level logging and runtime configuration properties for the jobs that you submit under the application.

Example declaration

The following example shows how to declare default configurations with create-application.

aws emr-serverless create-application \
    --release-label release-version  \
    --type SPARK \
    --name my-application-name \
    --runtime-configuration '[
        {
            "classification": "spark-defaults",
            "properties": {
                "spark.driver.cores": "4",
                "spark.executor.cores": "2",
                "spark.driver.memory": "8G",
                "spark.executor.memory": "8G",
                "spark.executor.instances": "2",
                "spark.hadoop.javax.jdo.option.ConnectionDriverName":"org.mariadb.jdbc.Driver",
                "spark.hadoop.javax.jdo.option.ConnectionURL":"jdbc:mysql://db-host:db-port/db-name",
                "spark.hadoop.javax.jdo.option.ConnectionUserName":"connection-user-name",
                "spark.hadoop.javax.jdo.option.ConnectionPassword": "EMR.secret@SecretID"
            }
        },
        {
            "classification": "spark-driver-log4j2",
            "properties": {
                "rootLogger.level":"error", 
                "logger.IdentifierForClass.name": "classpathForSettingLogger",
                "logger.IdentifierForClass.level": "info"
            }
        }
    ]' \
    --monitoring-configuration '{
        "s3MonitoringConfiguration": {
            "logUri": "s3://DOC-EXAMPLE-BUCKET-LOGGING/logs/app-level"
        },
        "managedPersistenceMonitoringConfiguration": {
            "enabled": false
        }
    }'

Overriding configurations during a job run

You can specify configuration overrides for the application configuration and monitoring configuration with the StartJobRun API. EMR Serverless then merges the configurations that you specify at the application level and the job level to determine the configurations for the job execution.

The granularity level when the merge occurs is as follows:

For more information priority rankings, see Hive configuration override parameter and Spark configuration override parameter.

When you start a job, if you don’t specify a particular configuration, it will be inherited from the application. If you declare the configurations at job level, you can perform the following operations:

Example override

The following example shows different operations you can perform during job submission at start-job-run.

aws emr-serverless start-job-run \
    --application-id your-application-id \
    --execution-role-arn your-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"]
        }
    }' \
    --configuration-overrides '{
        "applicationConfiguration": [ 
            {
                // Override existing configuration for spark-defaults in the application
                "classification": "spark-defaults", 
                "properties": {
                    "spark.driver.cores": "2",
                    "spark.executor.cores": "1",
                    "spark.driver.memory": "4G",
                    "spark.executor.memory": "4G"
                }
            },
            {
                // Add configuration for spark-executor-log4j2
                "classification": "spark-executor-log4j2",
                "properties": {
                    "rootLogger.level": "error", 
                    "logger.IdentifierForClass.name": "classpathForSettingLogger",
                    "logger.IdentifierForClass.level": "info"
                }
            },
            {
                // Remove existing configuration for spark-driver-log4j2 from the application
                "classification": "spark-driver-log4j2",
                "properties": {}
            }
        ],
        "monitoringConfiguration": {
            "managedPersistenceMonitoringConfiguration": {
                // Override existing configuration for managed persistence
                "enabled": true
            },
            "s3MonitoringConfiguration": {
                // Remove configuration of S3 monitoring
            },
            "cloudWatchLoggingConfiguration": {
                // Add configuration for CloudWatch logging
                "enabled": true
            }
        }
    }'

At the time of job execution, the following classifications and configurations will apply based on the priority override ranking described in Hive configuration override parameter and Spark configuration override parameter.