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from __future__ import annotations
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.kinesis_analytics import KinesisAnalyticsV2Hook
from airflow.providers.amazon.aws.sensors.base_aws import AwsBaseSensor
from airflow.providers.amazon.aws.triggers.kinesis_analytics import (
KinesisAnalyticsV2ApplicationOperationCompleteTrigger,
)
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class KinesisAnalyticsV2BaseSensor(AwsBaseSensor[KinesisAnalyticsV2Hook]):
"""
General sensor behaviour for AWS Managed Service for Apache Flink.
Subclasses must set the following fields:
- ``INTERMEDIATE_STATES``
- ``FAILURE_STATES``
- ``SUCCESS_STATES``
- ``FAILURE_MESSAGE``
- ``SUCCESS_MESSAGE``
:param application_name: Application name.
:param deferrable: If True, the sensor will operate in deferrable mode. This mode requires aiobotocore
module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
"""
[docs] aws_hook_class = KinesisAnalyticsV2Hook
[docs] FAILURE_STATES: tuple[str, ...] = ()
[docs] SUCCESS_STATES: tuple[str, ...] = ()
def __init__(
self,
application_name: str,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs: Any,
):
super().__init__(**kwargs)
self.application_name = application_name
self.deferrable = deferrable
[docs] def poke(self, context: Context, **kwargs) -> bool:
status = self.hook.conn.describe_application(ApplicationName=self.application_name)[
"ApplicationDetail"
]["ApplicationStatus"]
self.log.info(
"Poking for AWS Managed Service for Apache Flink application: %s status: %s",
self.application_name,
status,
)
if status in self.FAILURE_STATES:
raise AirflowException(self.FAILURE_MESSAGE)
if status in self.SUCCESS_STATES:
self.log.info(
"%s `%s`.",
self.SUCCESS_MESSAGE,
self.application_name,
)
return True
return False
[docs]class KinesisAnalyticsV2StartApplicationCompletedSensor(KinesisAnalyticsV2BaseSensor):
"""
Waits for AWS Managed Service for Apache Flink application to start.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:KinesisAnalyticsV2StartApplicationCompletedSensor`
:param application_name: Application name.
:param deferrable: If True, the sensor will operate in deferrable mode. This mode requires aiobotocore
module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
:param poke_interval: Polling period in seconds to check for the status of the job. (default: 120)
:param max_retries: Number of times before returning the current state. (default: 75)
:param aws_conn_id: The Airflow connection used for AWS credentials.
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. If not specified then the default boto3 behaviour is used.
:param verify: Whether to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param botocore_config: Configuration dictionary (key-values) for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs] FAILURE_STATES: tuple[str, ...] = KinesisAnalyticsV2Hook.APPLICATION_START_FAILURE_STATES
[docs] SUCCESS_STATES: tuple[str, ...] = KinesisAnalyticsV2Hook.APPLICATION_START_SUCCESS_STATES
[docs] FAILURE_MESSAGE = "AWS Managed Service for Apache Flink application start failed."
[docs] SUCCESS_MESSAGE = "AWS Managed Service for Apache Flink application started successfully"
[docs] template_fields: Sequence[str] = aws_template_fields("application_name")
def __init__(
self,
*,
application_name: str,
max_retries: int = 75,
poke_interval: int = 120,
**kwargs: Any,
) -> None:
super().__init__(application_name=application_name, **kwargs)
self.application_name = application_name
self.max_retries = max_retries
self.poke_interval = poke_interval
[docs] def execute(self, context: Context) -> Any:
if self.deferrable:
self.defer(
trigger=KinesisAnalyticsV2ApplicationOperationCompleteTrigger(
application_name=self.application_name,
waiter_name="application_start_complete",
aws_conn_id=self.aws_conn_id,
waiter_delay=int(self.poke_interval),
waiter_max_attempts=self.max_retries,
region_name=self.region_name,
verify=self.verify,
botocore_config=self.botocore_config,
),
method_name="poke",
)
else:
super().execute(context=context)
[docs]class KinesisAnalyticsV2StopApplicationCompletedSensor(KinesisAnalyticsV2BaseSensor):
"""
Waits for AWS Managed Service for Apache Flink application to stop.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:KinesisAnalyticsV2StopApplicationCompletedSensor`
:param application_name: Application name.
:param deferrable: If True, the sensor will operate in deferrable mode. This mode requires aiobotocore
module to be installed.
(default: False, but can be overridden in config file by setting default_deferrable to True)
:param poke_interval: Polling period in seconds to check for the status of the job. (default: 120)
:param max_retries: Number of times before returning the current state. (default: 75)
:param aws_conn_id: The Airflow connection used for AWS credentials.
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. If not specified then the default boto3 behaviour is used.
:param verify: Whether to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param botocore_config: Configuration dictionary (key-values) for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs] FAILURE_STATES: tuple[str, ...] = KinesisAnalyticsV2Hook.APPLICATION_STOP_FAILURE_STATES
[docs] SUCCESS_STATES: tuple[str, ...] = KinesisAnalyticsV2Hook.APPLICATION_STOP_SUCCESS_STATES
[docs] FAILURE_MESSAGE = "AWS Managed Service for Apache Flink application stop failed."
[docs] SUCCESS_MESSAGE = "AWS Managed Service for Apache Flink application stopped successfully"
[docs] template_fields: Sequence[str] = aws_template_fields("application_name")
def __init__(
self,
*,
application_name: str,
max_retries: int = 75,
poke_interval: int = 120,
**kwargs: Any,
) -> None:
super().__init__(application_name=application_name, **kwargs)
self.application_name = application_name
self.max_retries = max_retries
self.poke_interval = poke_interval
[docs] def execute(self, context: Context) -> Any:
if self.deferrable:
self.defer(
trigger=KinesisAnalyticsV2ApplicationOperationCompleteTrigger(
application_name=self.application_name,
waiter_name="application_stop_complete",
aws_conn_id=self.aws_conn_id,
waiter_delay=int(self.poke_interval),
waiter_max_attempts=self.max_retries,
region_name=self.region_name,
verify=self.verify,
botocore_config=self.botocore_config,
),
method_name="poke",
)
else:
super().execute(context=context)