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.. _howto/operator:EMRContainersOperators:

Amazon EMR on EKS Operators
===========================

`Amazon EMR on EKS <https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks.html>`__
provides a deployment option for Amazon EMR that allows you to run open-source big data frameworks on
Amazon EKS.

Airflow provides the :class:`~airflow.providers.amazon.aws.operators.emr.EmrContainerOperator`
to submit Apache Spark jobs to your EMR on EKS virtual cluster.

Prerequisite Tasks
------------------

.. include:: _partials/prerequisite_tasks.rst

This example assumes that you already have an EMR on EKS virtual cluster configured. See the
`EMR on EKS Getting Started guide <https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/getting-started.html>`__
for more information.


Run a Spark job on EMR on EKS
-----------------------------

Purpose
"""""""

The ``EMRContainerOperator`` will submit a new job to an EMR on EKS virtual cluster and wait for
the job to complete. The example job below calculates the mathematical constant ``Pi``. In a
production job, you would usually refer to a Spark script on Amazon Simple Storage Service (S3).

Job configuration
"""""""""""""""""

To create a job for EMR on EKS, you need to specify your virtual cluster ID, the release of EMR you
want to use, your IAM execution role, and Spark submit parameters.

You can also optionally provide configuration overrides such as Spark, Hive, or Log4j properties as
well as monitoring configuration that sends Spark logs to S3 or Amazon Cloudwatch.

In the example, we show how to add an ``applicationConfiguration`` to use the AWS Glue data catalog
and ``monitoringConfiguration`` to send logs to the ``/aws/emr-eks-spark`` log group in CloudWatch.
Refer to the `EMR on EKS guide <https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks-jobs-CLI.html#emr-eks-jobs-parameters>`__
for more details on job configuration.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_emr_eks_job.py
    :language: python
    :start-after: [START howto_operator_emr_eks_config]
    :end-before: [END howto_operator_emr_eks_config]


We pass the ``virtual_cluster_id`` and ``execution_role_arn`` values as operator parameters, but you
can store them in a connection or provide them in the DAG. Your AWS region should be defined either
in the ``aws_default`` connection as ``{"region_name": "us-east-1"}`` or a custom connection name
that gets passed to the operator with the ``aws_conn_id`` parameter.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_emr_eks_job.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_emr_eks_job]
    :end-before: [END howto_operator_emr_eks_job]

With the EmrContainerOperator, it will wait until the successful completion of the job or raise
an ``AirflowException`` if there is an error. The operator returns the Job ID of the job run.

Reference
---------

For further information, look at:

* `Amazon EMR on EKS Job runs <https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/job-runs.html>`__
* `EMR on EKS Best Practices <https://aws.github.io/aws-emr-containers-best-practices/>`__