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

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from __future__ import annotations

from enum import Enum
from typing import TYPE_CHECKING, Any, Sequence

from airflow.compat.functools import cached_property
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.glacier import GlacierHook
from airflow.sensors.base import BaseSensorOperator

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class JobStatus(Enum): """Glacier jobs description"""
[docs] IN_PROGRESS = "InProgress"
[docs] SUCCEEDED = "Succeeded"
[docs]class GlacierJobOperationSensor(BaseSensorOperator): """ Glacier sensor for checking job state. This operator runs only in reschedule mode. .. seealso:: For more information on how to use this sensor, take a look at the guide: :ref:`howto/sensor:GlacierJobOperationSensor` :param aws_conn_id: The reference to the AWS connection details :param vault_name: name of Glacier vault on which job is executed :param job_id: the job ID was returned by retrieve_inventory() :param poke_interval: Time in seconds that the job should wait in between each tries :param mode: How the sensor operates. Options are: ``{ poke | reschedule }``, default is ``poke``. When set to ``poke`` the sensor is taking up a worker slot for its whole execution time and sleeps between pokes. Use this mode if the expected runtime of the sensor is short or if a short poke interval is required. Note that the sensor will hold onto a worker slot and a pool slot for the duration of the sensor's runtime in this mode. When set to ``reschedule`` the sensor task frees the worker slot when the criteria is not yet met and it's rescheduled at a later time. Use this mode if the time before the criteria is met is expected to be quite long. The poke interval should be more than one minute to prevent too much load on the scheduler. """
[docs] template_fields: Sequence[str] = ("vault_name", "job_id")
def __init__( self, *, aws_conn_id: str = "aws_default", vault_name: str, job_id: str, poke_interval: int = 60 * 20, mode: str = "reschedule", **kwargs: Any, ) -> None: super().__init__(**kwargs) self.aws_conn_id = aws_conn_id self.vault_name = vault_name self.job_id = job_id self.poke_interval = poke_interval self.mode = mode @cached_property
[docs] def hook(self): return GlacierHook(aws_conn_id=self.aws_conn_id)
[docs] def poke(self, context: Context) -> bool: response = self.hook.describe_job(vault_name=self.vault_name, job_id=self.job_id) if response["StatusCode"] == JobStatus.SUCCEEDED.value: self.log.info("Job status: %s, code status: %s", response["Action"], response["StatusCode"]) self.log.info("Job finished successfully") return True elif response["StatusCode"] == JobStatus.IN_PROGRESS.value: self.log.info("Processing...") self.log.warning("Code status: %s", response["StatusCode"]) return False else: raise AirflowException( f'Sensor failed. Job status: {response["Action"]}, code status: {response["StatusCode"]}'
)

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