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

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from __future__ import annotations

from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence

from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.providers.amazon.aws.triggers.batch import BatchJobTrigger
from airflow.sensors.base import BaseSensorOperator

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class BatchSensor(BaseSensorOperator): """ Poll the state of the Batch Job until it reaches a terminal state; fails if the job fails. .. 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' 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). :param region_name: aws region name associated with the client :param deferrable: Run sensor in the deferrable mode. :param poke_interval: polling period in seconds to check for the status of the job. :param max_retries: Number of times to poll for job state before returning the current state. """
[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 | None = "aws_default", region_name: str | None = None, deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False), poke_interval: float = 30, max_retries: int = 4200, **kwargs, ): super().__init__(**kwargs) self.job_id = job_id self.aws_conn_id = aws_conn_id self.region_name = region_name self.deferrable = deferrable self.poke_interval = poke_interval self.max_retries = max_retries
[docs] def poke(self, context: Context) -> bool: job_description = self.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 raise AirflowException(f"Batch sensor failed. AWS Batch job status: {state}")
[docs] def execute(self, context: Context) -> None: if not self.deferrable: super().execute(context=context) else: timeout = ( timedelta(seconds=self.max_retries * self.poke_interval + 60) if self.max_retries else self.execution_timeout ) self.defer( timeout=timeout, trigger=BatchJobTrigger( job_id=self.job_id, aws_conn_id=self.aws_conn_id, region_name=self.region_name, waiter_delay=int(self.poke_interval), waiter_max_attempts=self.max_retries, ), method_name="execute_complete", )
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> None: """ Execute when the trigger fires - returns immediately. Relies on trigger to throw an exception, otherwise it assumes execution was successful. """ if event["status"] != "success": raise AirflowException(f"Error while running job: {event}") job_id = event["job_id"] self.log.info("Batch Job %s complete", job_id)
@cached_property
[docs] def hook(self) -> BatchClientHook: return BatchClientHook( aws_conn_id=self.aws_conn_id, region_name=self.region_name, )
[docs]class BatchComputeEnvironmentSensor(BaseSensorOperator): """ Poll the state of the Batch environment until it reaches a terminal state; fails if the environment fails. .. 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' 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). :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 | None = "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( # type: ignore[union-attr] computeEnvironments=[self.compute_environment] ) if not response["computeEnvironments"]: 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): """ Poll the state of the Batch job queue until it reaches a terminal state; fails if the queue fails. .. 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' 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). :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 | None = "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( # type: ignore[union-attr] jobQueues=[self.job_queue] ) if not response["jobQueues"]: 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 message = f"AWS Batch job queue failed. AWS Batch job queue status: {status}" raise AirflowException(message)

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