Amazon EMR on EKS 7.0.0 releases - Amazon EMR
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Amazon EMR on EKS 7.0.0 releases

This page describes the new and updated functionality for Amazon EMR that is specific to the Amazon EMR on EKS deployment. For details about Amazon EMR running on Amazon EC2 and about the Amazon EMR 7.0.0 release in general, see Amazon EMR 7.0.0 in the Amazon EMR Release Guide.

Amazon EMR on EKS 7.0 releases

The following Amazon EMR 7.0.0 releases are available for Amazon EMR on EKS. Select a specific emr-7.0.0-XXXX release to view more details such as the related container image tag.

Flink releases

The following Amazon EMR 7.0.0 releases are available for Amazon EMR on EKS when you run Flink applications.

Spark releases

The following Amazon EMR 7.0.0 releases are available for Amazon EMR on EKS when you run Spark applications.

  • emr-7.0.0-latest

  • emr-7.0.0-20231211

  • emr-7.0.0-spark-rapids-latest

  • emr-7.0.0-spark-rapids-20231211

  • emr-7.0.0-java11-latest

  • emr-7.0.0-java11-20231211

  • emr-7.0.0-java8-latest

  • emr-7.0.0-java8-20231211

  • emr-7.0.0-spark-rapids-java8-latest

  • emr-7.0.0-spark-rapids-java8-20231211

  • notebook-spark/emr-7.0.0-latest

  • notebook-spark/emr-7.0.0-20231211

  • notebook-spark/emr-7.0.0-spark-rapids-latest

  • notebook-spark/emr-7.0.0-spark-rapids-20231211

  • notebook-spark/emr-7.0.0-java11-latest

  • notebook-spark/emr-7.0.0-java11-20231211

  • notebook-spark/emr-7.0.0-java8-latest

  • notebook-spark/emr-7.0.0-java8-20231211

  • notebook-spark/emr-7.0.0-spark-rapids-java8-latest

  • notebook-spark/emr-7.0.0-spark-rapids-java8-20231211

  • notebook-python/emr-7.0.0-latest

  • notebook-python/emr-7.0.0-20231211

  • notebook-python/emr-7.0.0-spark-rapids-latest

  • notebook-python/emr-7.0.0-spark-rapids-20231211

  • notebook-python/emr-7.0.0-java11-latest

  • notebook-python/emr-7.0.0-java11-20231211

  • notebook-python/emr-7.0.0-java8-latest

  • notebook-python/emr-7.0.0-java8-20231211

  • notebook-python/emr-7.0.0-spark-rapids-java8-latest

  • notebook-python/emr-7.0.0-spark-rapids-java8-20231211

Release notes

Release notes for Amazon EMR on EKS 7.0.0

  • Supported applications ‐ AWS SDK for Java 2.20.160-amzn-0 and 1.12.595, Apache Spark 3.5.0-amzn-0, Apache Flink 1.18.0-amzn-0, Flink Operator 1.6.1, Apache Hudi 0.14.0-amzn-1, Apache Iceberg 1.4.2-amzn-0, Delta 3.0.0, Apache Spark RAPIDS 23.10.0-amzn-0, Jupyter Enterprise Gateway 2.6.0

  • Supported components ‐ aws-sagemaker-spark-sdk, emr-ddb, emr-goodies, emr-s3-select, emrfs, hadoop-client, hudi, hudi-spark, iceberg, spark-kubernetes.

  • Supported configuration classifications

    For use with StartJobRun and CreateManagedEndpoint APIs:

    Classifications Descriptions

    core-site

    Change values in the core-site.xml Hadoop file.

    emrfs-site

    Change EMRFS settings.

    spark-metrics

    Change values in the metrics.properties Spark file.

    spark-defaults

    Change values in the spark-defaults.conf Spark file.

    spark-env

    Change values in the Spark environment.

    spark-hive-site

    Change values in the hive-site.xml Spark file.

    spark-log4j

    Change values in the log4j2.properties Spark file.

    emr-job-submitter

    Configuration for job submitter pod.

    For use specifically with CreateManagedEndpoint APIs:

    Classifications Descriptions

    jeg-config

    Change values in Jupyter Enterprise Gateway jupyter_enterprise_gateway_config.py file.

    jupyter-kernel-overrides

    Change value for the Kernel Image in Jupyter Kernel Spec file.

    Configuration classifications allow you to customize applications. These often correspond to a configuration XML file for the application, such as spark-hive-site.xml. For more information, see Configure Applications.

Notable features

The following features are included with the 7.0 release of Amazon EMR on EKS.

  • Application upgrades – Amazon EMR on EKS 7.0.0 application upgrades include Spark 3.5, Flink 1.18, and Flink Operator 1.6.1.

  • Flink Autoscaler parameter auto-tuning – The default parameters that Flink Autoscaler uses for its scaling calculations might not be the optimal value for a given job. Amazon EMR on EKS 7.0.0 uses historical trends of specific captured metrics to calculate the optimal parameter tailored for the job.

Changes

The following changes are included with the 7.0 release of Amazon EMR on EKS.

  • Amazon Linux 2023 – With Amazon EMR on EKS 7.0.0 and higher, all container images are based on Amazon Linux 2023.

  • Spark uses Java 17 as default runtime – Amazon EMR on EKS 7.0.0 Spark uses Java 17 as default runtime. If you need to, you can switch to use Java 8 or Java 11 with the corresponding release label as provided in the Amazon EMR on EKS 7.0 releases list.