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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