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#
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from __future__ import annotations
from typing import TYPE_CHECKING, Any, ClassVar, Sequence
from botocore.exceptions import ClientError
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.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.kinesis_analytics import (
KinesisAnalyticsV2ApplicationOperationCompleteTrigger,
)
from airflow.providers.amazon.aws.utils import validate_execute_complete_event
from airflow.providers.amazon.aws.utils.mixins import aws_template_fields
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class KinesisAnalyticsV2CreateApplicationOperator(AwsBaseOperator[KinesisAnalyticsV2Hook]):
"""
Creates an AWS Managed Service for Apache Flink application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:KinesisAnalyticsV2CreateApplicationOperator`
:param application_name: The name of application. (templated)
:param runtime_environment: The runtime environment for the application. (templated)
:param service_execution_role: The IAM role used by the application to access services. (templated)
:param create_application_kwargs: Create application extra properties. (templated)
:param application_description: A summary description of the application. (templated)
: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] aws_hook_class = KinesisAnalyticsV2Hook
[docs] template_fields: Sequence[str] = aws_template_fields(
"application_name",
"runtime_environment",
"service_execution_role",
"create_application_kwargs",
"application_description",
)
[docs] template_fields_renderers: ClassVar[dict] = {
"create_application_kwargs": "json",
}
def __init__(
self,
application_name: str,
runtime_environment: str,
service_execution_role: str,
create_application_kwargs: dict[str, Any] | None = None,
application_description: str = "Managed Service for Apache Flink application created from Airflow",
**kwargs,
):
super().__init__(**kwargs)
self.application_name = application_name
self.runtime_environment = runtime_environment
self.service_execution_role = service_execution_role
self.create_application_kwargs = create_application_kwargs or {}
self.application_description = application_description
[docs] def execute(self, context: Context) -> dict[str, str]:
self.log.info("Creating AWS Managed Service for Apache Flink application %s.", self.application_name)
try:
response = self.hook.conn.create_application(
ApplicationName=self.application_name,
ApplicationDescription=self.application_description,
RuntimeEnvironment=self.runtime_environment,
ServiceExecutionRole=self.service_execution_role,
**self.create_application_kwargs,
)
except ClientError as error:
raise AirflowException(
f"AWS Managed Service for Apache Flink application creation failed: {error.response['Error']['Message']}"
)
self.log.info(
"AWS Managed Service for Apache Flink application created successfully %s.",
self.application_name,
)
return {"ApplicationARN": response["ApplicationDetail"]["ApplicationARN"]}
[docs]class KinesisAnalyticsV2StartApplicationOperator(AwsBaseOperator[KinesisAnalyticsV2Hook]):
"""
Starts an AWS Managed Service for Apache Flink application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:KinesisAnalyticsV2StartApplicationOperator`
:param application_name: The name of application. (templated)
:param run_configuration: Application properties to start Apache Flink Job. (templated)
:param wait_for_completion: Whether to wait for job to stop. (default: True)
:param waiter_delay: Time in seconds to wait between status checks. (default: 60)
:param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20)
:param deferrable: If True, the operator will wait asynchronously for the job to stop.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
: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] aws_hook_class = KinesisAnalyticsV2Hook
[docs] template_fields: Sequence[str] = aws_template_fields(
"application_name",
"run_configuration",
)
[docs] template_fields_renderers: ClassVar[dict] = {
"run_configuration": "json",
}
def __init__(
self,
application_name: str,
run_configuration: dict[str, Any] | None = None,
wait_for_completion: bool = True,
waiter_delay: int = 60,
waiter_max_attempts: int = 20,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
self.application_name = application_name
self.run_configuration = run_configuration or {}
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context) -> dict[str, Any]:
msg = "AWS Managed Service for Apache Flink application"
try:
self.log.info("Starting %s %s.", msg, self.application_name)
self.hook.conn.start_application(
ApplicationName=self.application_name, RunConfiguration=self.run_configuration
)
except ClientError as error:
raise AirflowException(
f"Failed to start {msg} {self.application_name}: {error.response['Error']['Message']}"
)
describe_response = self.hook.conn.describe_application(ApplicationName=self.application_name)
if self.deferrable:
self.log.info("Deferring for %s to start: %s.", msg, self.application_name)
self.defer(
trigger=KinesisAnalyticsV2ApplicationOperationCompleteTrigger(
application_name=self.application_name,
waiter_name="application_start_complete",
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
region_name=self.region_name,
verify=self.verify,
botocore_config=self.botocore_config,
),
method_name="execute_complete",
)
if self.wait_for_completion:
self.log.info("Waiting for %s to start: %s.", msg, self.application_name)
self.hook.get_waiter("application_start_complete").wait(
ApplicationName=self.application_name,
WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts},
)
self.log.info("%s started successfully %s.", msg, self.application_name)
return {"ApplicationARN": describe_response["ApplicationDetail"]["ApplicationARN"]}
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> dict[str, Any]:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException(
"Error while starting AWS Managed Service for Apache Flink application: %s", event
)
response = self.hook.conn.describe_application(
ApplicationName=event["application_name"],
)
self.log.info(
"AWS Managed Service for Apache Flink application %s started successfully.",
event["application_name"],
)
return {"ApplicationARN": response["ApplicationDetail"]["ApplicationARN"]}
[docs]class KinesisAnalyticsV2StopApplicationOperator(AwsBaseOperator[KinesisAnalyticsV2Hook]):
"""
Stop an AWS Managed Service for Apache Flink application.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:KinesisAnalyticsV2StopApplicationOperator`
:param application_name: The name of your application. (templated)
:param force: Set to true to force the application to stop. If you set Force to true, Managed Service for
Apache Flink stops the application without taking a snapshot. (templated)
:param wait_for_completion: Whether to wait for job to stop. (default: True)
:param waiter_delay: Time in seconds to wait between status checks. (default: 60)
:param waiter_max_attempts: Maximum number of attempts to check for job completion. (default: 20)
:param deferrable: If True, the operator will wait asynchronously for the job to stop.
This implies waiting for completion. This mode requires aiobotocore module to be installed.
(default: False)
: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] aws_hook_class = KinesisAnalyticsV2Hook
[docs] template_fields: Sequence[str] = aws_template_fields(
"application_name",
"force",
)
def __init__(
self,
application_name: str,
force: bool = False,
wait_for_completion: bool = True,
waiter_delay: int = 60,
waiter_max_attempts: int = 20,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
self.application_name = application_name
self.force = force
self.wait_for_completion = wait_for_completion
self.waiter_delay = waiter_delay
self.waiter_max_attempts = waiter_max_attempts
self.deferrable = deferrable
[docs] def execute(self, context: Context) -> dict[str, Any]:
msg = "AWS Managed Service for Apache Flink application"
try:
self.log.info("Stopping %s %s.", msg, self.application_name)
self.hook.conn.stop_application(ApplicationName=self.application_name, Force=self.force)
except ClientError as error:
raise AirflowException(
f"Failed to stop {msg} {self.application_name}: {error.response['Error']['Message']}"
)
describe_response = self.hook.conn.describe_application(ApplicationName=self.application_name)
if self.deferrable:
self.log.info("Deferring for %s to stop: %s.", msg, self.application_name)
self.defer(
trigger=KinesisAnalyticsV2ApplicationOperationCompleteTrigger(
application_name=self.application_name,
waiter_name="application_stop_complete",
aws_conn_id=self.aws_conn_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
region_name=self.region_name,
verify=self.verify,
botocore_config=self.botocore_config,
),
method_name="execute_complete",
)
if self.wait_for_completion:
self.log.info("Waiting for %s to stop: %s.", msg, self.application_name)
self.hook.get_waiter("application_stop_complete").wait(
ApplicationName=self.application_name,
WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts},
)
self.log.info("%s stopped successfully %s.", msg, self.application_name)
return {"ApplicationARN": describe_response["ApplicationDetail"]["ApplicationARN"]}
[docs] def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> dict[str, Any]:
event = validate_execute_complete_event(event)
if event["status"] != "success":
raise AirflowException("Error while stopping AWS Managed Service for Apache Flink application")
response = self.hook.conn.describe_application(
ApplicationName=event["application_name"],
)
self.log.info(
"AWS Managed Service for Apache Flink application %s stopped successfully.",
event["application_name"],
)
return {"ApplicationARN": response["ApplicationDetail"]["ApplicationARN"]}