docs-archive/apache-airflow-providers-amazon/5.0.0/_sources/_api/airflow/providers/amazon/aws/operators/batch/index.rst.txt

:py:mod:`airflow.providers.amazon.aws.operators.batch`
======================================================

.. py:module:: airflow.providers.amazon.aws.operators.batch

.. autoapi-nested-parse::

   An Airflow operator for AWS Batch services

   .. seealso::

       - http://boto3.readthedocs.io/en/latest/guide/configuration.html
       - http://boto3.readthedocs.io/en/latest/reference/services/batch.html
       - https://docs.aws.amazon.com/batch/latest/APIReference/Welcome.html



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.amazon.aws.operators.batch.BatchOperator
   airflow.providers.amazon.aws.operators.batch.BatchCreateComputeEnvironmentOperator




.. py:class:: BatchOperator(*, job_name, job_definition, job_queue, overrides, array_properties = None, parameters = None, job_id = None, waiters = None, max_retries = None, status_retries = None, aws_conn_id = None, region_name = None, tags = None, wait_for_completion = True, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Execute a job on AWS Batch

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BatchOperator`

   :param job_name: the name for the job that will run on AWS Batch (templated)

   :param job_definition: the job definition name on AWS Batch

   :param job_queue: the queue name on AWS Batch

   :param overrides: the `containerOverrides` parameter for boto3 (templated)

   :param array_properties: the `arrayProperties` parameter for boto3

   :param parameters: the `parameters` for boto3 (templated)

   :param job_id: the job ID, usually unknown (None) until the
       submit_job operation gets the jobId defined by AWS Batch

   :param waiters: an :py:class:`.BatchWaiters` object (see note below);
       if None, polling is used with max_retries and status_retries.

   :param max_retries: exponential back-off retries, 4200 = 48 hours;
       polling is only used when waiters is None

   :param status_retries: number of HTTP retries to get job status, 10;
       polling is only used when waiters is None

   :param aws_conn_id: connection id of AWS credentials / region name. If None,
       credential boto3 strategy will be used.

   :param region_name: region name to use in AWS Hook.
       Override the region_name in connection (if provided)

   :param tags: collection of tags to apply to the AWS Batch job submission
       if None, no tags are submitted

   .. note::
       Any custom waiters must return a waiter for these calls:
       .. code-block:: python

           waiter = waiters.get_waiter("JobExists")
           waiter = waiters.get_waiter("JobRunning")
           waiter = waiters.get_waiter("JobComplete")

   .. py:attribute:: ui_color
      :annotation: = #c3dae0

      

   .. py:attribute:: arn
      :annotation: :Optional[str]

      

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['job_name', 'job_queue', 'job_definition', 'overrides', 'parameters']

      

   .. py:attribute:: template_fields_renderers
      

      

   .. py:method:: operator_extra_links()
      :property:


   .. py:method:: execute(context)

      Submit and monitor an AWS Batch job

      :raises: AirflowException


   .. py:method:: on_kill()

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.


   .. py:method:: submit_job(context)

      Submit an AWS Batch job

      :raises: AirflowException


   .. py:method:: monitor_job(context)

      Monitor an AWS Batch job
      monitor_job can raise an exception or an AirflowTaskTimeout can be raised if execution_timeout
      is given while creating the task. These exceptions should be handled in taskinstance.py
      instead of here like it was previously done

      :raises: AirflowException



.. py:class:: BatchCreateComputeEnvironmentOperator(compute_environment_name, environment_type, state, compute_resources, unmanaged_v_cpus = None, service_role = None, tags = None, max_retries = None, status_retries = None, aws_conn_id = None, region_name = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Create an AWS Batch compute environment

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BatchCreateComputeEnvironmentOperator`

   :param compute_environment_name: the name of the AWS batch compute environment (templated)

   :param environment_type: the type of the compute-environment

   :param state: the state of the compute-environment

   :param compute_resources: details about the resources managed by the compute-environment (templated).
       See more details here
       https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html#Batch.Client.create_compute_environment

   :param unmanaged_v_cpus: the maximum number of vCPU for an unmanaged compute environment.
       This parameter is only supported when the ``type`` parameter is set to ``UNMANAGED``.

   :param service_role: the IAM role that allows Batch to make calls to other AWS services on your behalf
       (templated)

   :param tags: the tags that you apply to the compute-environment to help you categorize and organize your
       resources

   :param max_retries: exponential back-off retries, 4200 = 48 hours;
       polling is only used when waiters is None

   :param status_retries: number of HTTP retries to get job status, 10;
       polling is only used when waiters is None

   :param aws_conn_id: connection id of AWS credentials / region name. If None,
       credential boto3 strategy will be used.

   :param region_name: region name to use in AWS Hook.
       Override the region_name in connection (if provided)

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['compute_environment_name', 'compute_resources', 'service_role']

      

   .. py:attribute:: template_fields_renderers
      

      

   .. py:method:: hook()

      Create and return a BatchClientHook


   .. py:method:: execute(context)

      Create an AWS batch compute environment