Source code for airflow.providers.amazon.aws.sensors.batch
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from __future__ import annotations
import sys
from typing import TYPE_CHECKING, Sequence
if sys.version_info >= (3, 8):
from functools import cached_property
else:
from cached_property import cached_property
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class BatchSensor(BaseSensorOperator):
"""
Asks for the state of the Batch Job execution until it reaches a failure state or success state.
If the job fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchSensor`
:param job_id: Batch job_id to check the state for
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
"""
[docs] template_fields: Sequence[str] = ("job_id",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
job_id: str,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.job_id = job_id
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.hook: BatchClientHook | None = None
[docs] def poke(self, context: Context) -> bool:
job_description = self.get_hook().get_job_description(self.job_id)
state = job_description["status"]
if state == BatchClientHook.SUCCESS_STATE:
return True
if state in BatchClientHook.INTERMEDIATE_STATES:
return False
if state == BatchClientHook.FAILURE_STATE:
raise AirflowException(f"Batch sensor failed. AWS Batch job status: {state}")
raise AirflowException(f"Batch sensor failed. Unknown AWS Batch job status: {state}")
[docs] def get_hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook"""
if self.hook:
return self.hook
self.hook = BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
return self.hook
[docs]class BatchComputeEnvironmentSensor(BaseSensorOperator):
"""
Asks for the state of the Batch compute environment until it reaches a failure state or success state.
If the environment fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchComputeEnvironmentSensor`
:param compute_environment: Batch compute environment name
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
"""
[docs] template_fields: Sequence[str] = ("compute_environment",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
compute_environment: str,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.compute_environment = compute_environment
self.aws_conn_id = aws_conn_id
self.region_name = region_name
@cached_property
[docs] def hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook"""
return BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.client.describe_compute_environments(
computeEnvironments=[self.compute_environment]
)
if len(response["computeEnvironments"]) == 0:
raise AirflowException(f"AWS Batch compute environment {self.compute_environment} not found")
status = response["computeEnvironments"][0]["status"]
if status in BatchClientHook.COMPUTE_ENVIRONMENT_TERMINAL_STATUS:
return True
if status in BatchClientHook.COMPUTE_ENVIRONMENT_INTERMEDIATE_STATUS:
return False
raise AirflowException(
f"AWS Batch compute environment failed. AWS Batch compute environment status: {status}"
)
[docs]class BatchJobQueueSensor(BaseSensorOperator):
"""
Asks for the state of the Batch job queue until it reaches a failure state or success state.
If the queue fails, the task will fail.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchJobQueueSensor`
:param job_queue: Batch job queue name
:param treat_non_existing_as_deleted: If True, a non-existing Batch job queue is considered as a deleted
queue and as such a valid case.
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
"""
[docs] template_fields: Sequence[str] = ("job_queue",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
job_queue: str,
treat_non_existing_as_deleted: bool = False,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.job_queue = job_queue
self.treat_non_existing_as_deleted = treat_non_existing_as_deleted
self.aws_conn_id = aws_conn_id
self.region_name = region_name
@cached_property
[docs] def hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook"""
return BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.client.describe_job_queues(jobQueues=[self.job_queue])
if len(response["jobQueues"]) == 0:
if self.treat_non_existing_as_deleted:
return True
else:
raise AirflowException(f"AWS Batch job queue {self.job_queue} not found")
status = response["jobQueues"][0]["status"]
if status in BatchClientHook.JOB_QUEUE_TERMINAL_STATUS:
return True
if status in BatchClientHook.JOB_QUEUE_INTERMEDIATE_STATUS:
return False
raise AirflowException(f"AWS Batch job queue failed. AWS Batch job queue status: {status}")
