airflow.providers.amazon.aws.operators.batch

An Airflow operator for AWS Batch services

See also

Module Contents

class airflow.providers.amazon.aws.operators.batch.AwsBatchOperator(*, job_name: str, job_definition: str, job_queue: str, overrides: dict, array_properties: Optional[dict] = None, parameters: Optional[dict] = None, job_id: Optional[str] = None, waiters: Optional[Any] = None, max_retries: Optional[int] = None, status_retries: Optional[int] = None, aws_conn_id: Optional[str] = None, region_name: Optional[str] = None, tags: Optional[dict] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

Execute a job on AWS Batch

Parameters

  • job_name (str) -- the name for the job that will run on AWS Batch (templated)

  • job_definition (str) -- the job definition name on AWS Batch

  • job_queue (str) -- the queue name on AWS Batch

  • overrides (Optional[dict]) -- the containerOverrides parameter for boto3 (templated)

  • array_properties (Optional[dict]) -- the arrayProperties parameter for boto3

  • parameters (Optional[dict]) -- the parameters for boto3 (templated)

  • job_id (Optional[str]) -- the job ID, usually unknown (None) until the submit_job operation gets the jobId defined by AWS Batch

  • waiters (Optional[AwsBatchWaiters]) -- an AwsBatchWaiters object (see note below); if None, polling is used with max_retries and status_retries.

  • max_retries (int) -- exponential back-off retries, 4200 = 48 hours; polling is only used when waiters is None

  • status_retries (int) -- number of HTTP retries to get job status, 10; polling is only used when waiters is None

  • aws_conn_id (str) -- connection id of AWS credentials / region name. If None, credential boto3 strategy will be used.

  • region_name (str) -- region name to use in AWS Hook. Override the region_name in connection (if provided)

  • tags (dict) -- 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")

ui_color = #c3dae0[source]
arn :Optional[str][source]
template_fields = ['job_name', 'overrides', 'parameters'][source]
template_fields_renderers[source]
execute(self, context: Dict)[source]

Submit and monitor an AWS Batch job

Raises

AirflowException

on_kill(self)[source]
submit_job(self, context: Dict)[source]

Submit an AWS Batch job

Raises

AirflowException

monitor_job(self, context: Dict)[source]

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

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