airflow.providers.amazon.aws.sensors.batch

Module Contents

Classes

BatchSensor

Poll the state of the Batch Job until it reaches a terminal state; fails if the job fails.

BatchComputeEnvironmentSensor

Poll the state of the Batch environment until it reaches a terminal state; fails if the environment fails.

BatchJobQueueSensor

Poll the state of the Batch job queue until it reaches a terminal state; fails if the queue fails.
class airflow.providers.amazon.aws.sensors.batch.BatchSensor(*, job_id, aws_conn_id='aws_default', region_name=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), poke_interval=30, max_retries=4200, **kwargs)[source]

Bases: airflow.sensors.base.BaseSensorOperator

Poll the state of the Batch Job until it reaches a terminal state; fails if the job fails.

See also

For more information on how to use this sensor, take a look at the guide: Wait on an AWS Batch job state

Parameters

  • job_id (str) – Batch job_id to check the state for

  • aws_conn_id (str | None) – aws connection to use, defaults to ‘aws_default’ If this is None or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node).

  • region_name (str | None) – aws region name associated with the client

  • deferrable (bool) – Run sensor in the deferrable mode.

  • poke_interval (float) – polling period in seconds to check for the status of the job.

  • max_retries (int) – Number of times to poll for job state before returning the current state.

template_fields: collections.abc.Sequence[str] = ('job_id',)[source]
template_ext: collections.abc.Sequence[str] = ()[source]
ui_color = '#66c3ff'[source]
poke(context)[source]

Override when deriving this class.

execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event)[source]

Execute when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

hook()[source]
class airflow.providers.amazon.aws.sensors.batch.BatchComputeEnvironmentSensor(compute_environment, aws_conn_id='aws_default', region_name=None, **kwargs)[source]

Bases: airflow.sensors.base.BaseSensorOperator

Poll the state of the Batch environment until it reaches a terminal state; fails if the environment fails.

See also

For more information on how to use this sensor, take a look at the guide: Wait on an AWS Batch compute environment status

Parameters

  • compute_environment (str) – Batch compute environment name

  • aws_conn_id (str | None) – aws connection to use, defaults to ‘aws_default’ If this is None or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node).

  • region_name (str | None) – aws region name associated with the client

template_fields: collections.abc.Sequence[str] = ('compute_environment',)[source]
template_ext: collections.abc.Sequence[str] = ()[source]
ui_color = '#66c3ff'[source]
hook()[source]

Create and return a BatchClientHook.

poke(context)[source]

Override when deriving this class.

class airflow.providers.amazon.aws.sensors.batch.BatchJobQueueSensor(job_queue, treat_non_existing_as_deleted=False, aws_conn_id='aws_default', region_name=None, **kwargs)[source]

Bases: airflow.sensors.base.BaseSensorOperator

Poll the state of the Batch job queue until it reaches a terminal state; fails if the queue fails.

See also

For more information on how to use this sensor, take a look at the guide: Wait on an AWS Batch job queue status

Parameters

  • job_queue (str) – Batch job queue name

  • treat_non_existing_as_deleted (bool) – If True, a non-existing Batch job queue is considered as a deleted queue and as such a valid case.

  • aws_conn_id (str | None) – aws connection to use, defaults to ‘aws_default’ If this is None or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node).

  • region_name (str | None) – aws region name associated with the client

template_fields: collections.abc.Sequence[str] = ('job_queue',)[source]
template_ext: collections.abc.Sequence[str] = ()[source]
ui_color = '#66c3ff'[source]
hook()[source]

Create and return a BatchClientHook.

poke(context)[source]

Override when deriving this class.

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