Source code for airflow.providers.amazon.aws.sensors.batch

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
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] = ()
[docs] ui_color = "#66c3ff"
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] = ()
[docs] ui_color = "#66c3ff"
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] = ()
[docs] ui_color = "#66c3ff"
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}")

Was this entry helpful?