Introduce Amazon Kinesis Analytics V2 (Managed Service for Apache Flink application)

airflow/providers/amazon/aws/hooks/kinesis_analytics.py

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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from __future__ import annotations
+
+from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
+
+
+class KinesisAnalyticsV2Hook(AwsBaseHook):
+    """
+    Interact with Amazon Kinesis Analytics V2.
+
+    Provide thin wrapper around :external+boto3:py:class:`boto3.client("kinesisanalyticsv2") <KinesisAnalyticsV2.Client>`.
+
+    Additional arguments (such as ``aws_conn_id``) may be specified and
+    are passed down to the underlying AwsBaseHook.
+
+    .. seealso::
+        - :class:`airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook`
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        kwargs["client_type"] = "kinesisanalyticsv2"
+        super().__init__(*args, **kwargs)

airflow/providers/amazon/aws/sensors/kinesis_analytics.py

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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Sequence
+
+from airflow.configuration import conf
+from airflow.exceptions import AirflowException, AirflowSkipException
+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
+
+
+class KinesisAnalyticsV2StartApplicationCompletedSensor(AwsBaseSensor[KinesisAnalyticsV2Hook]):
+    """
+    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
+
+    """
+
+    aws_hook_class = KinesisAnalyticsV2Hook
+    ui_color = "#66c3ff"
+
+    INTERMEDIATE_STATES: tuple[str, ...] = ("STARTING", "UPDATING", "AUTOSCALING")
+    FAILURE_STATES: tuple[str, ...] = (
+        "DELETING",
+        "STOPPING",
+        "READY",
+        "FORCE_STOPPING",
+        "ROLLING_BACK",
+        "MAINTENANCE",
+        "ROLLED_BACK",
+    )
+    SUCCESS_STATES: tuple[str, ...] = ("RUNNING",)
+    FAILURE_MESSAGE = "AWS Managed Service for Apache Flink application start failed."
+
+    template_fields: Sequence[str] = aws_template_fields("application_name")
+
+    def __init__(
+        self,
+        *,
+        application_name: str,
+        max_retries: int = 75,
+        poke_interval: int = 120,
+        deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.application_name = application_name
+        self.max_retries = max_retries
+        self.poke_interval = poke_interval
+        self.deferrable = deferrable
+
+    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)
+
+    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:
+            # TODO: remove this if block when min_airflow_version is set to higher than 2.7.1
+            if self.soft_fail:
+                raise AirflowSkipException(self.FAILURE_MESSAGE)
+            raise AirflowException(self.FAILURE_MESSAGE)
+
+        if status in self.SUCCESS_STATES:
+            self.log.info(
+                "AWS Managed Service for Apache Flink application started successfully `%s`.",
+                self.application_name,
+            )
+            return True
+
+        return False
+
+
+class KinesisAnalyticsV2StopApplicationCompletedSensor(AwsBaseSensor[KinesisAnalyticsV2Hook]):
+    """
+    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
+
+    """
+
+    aws_hook_class = KinesisAnalyticsV2Hook
+    ui_color = "#66c3ff"
+
+    INTERMEDIATE_STATES: tuple[str, ...] = (
+        "STARTING",
+        "UPDATING",
+        "AUTOSCALING",
+        "RUNNING",
+        "STOPPING",
+        "FORCE_STOPPING",
+    )
+    FAILURE_STATES: tuple[str, ...] = ("DELETING", "ROLLING_BACK", "MAINTENANCE", "ROLLED_BACK")
+    SUCCESS_STATES: tuple[str, ...] = ("READY",)
+    FAILURE_MESSAGE = "AWS Managed Service for Apache Flink application stop failed."
+
+    template_fields: Sequence[str] = aws_template_fields("application_name")
+
+    def __init__(
+        self,
+        *,
+        application_name: str,
+        max_retries: int = 75,
+        poke_interval: int = 120,
+        deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.application_name = application_name
+        self.max_retries = max_retries
+        self.poke_interval = poke_interval
+        self.deferrable = deferrable
+
+    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)
+
+    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:
+            # TODO: remove this if block when min_airflow_version is set to higher than 2.7.1
+            if self.soft_fail:
+                raise AirflowSkipException(self.FAILURE_MESSAGE)
+            raise AirflowException(self.FAILURE_MESSAGE)
+
+        if status in self.SUCCESS_STATES:
+            self.log.info(
+                "AWS Managed Service for Apache Flink application stopped successfully `%s`.",
+                self.application_name,
+            )
+            return True
+
+        return False

airflow/providers/amazon/aws/triggers/kinesis_analytics.py

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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from airflow.providers.amazon.aws.hooks.kinesis_analytics import KinesisAnalyticsV2Hook
+from airflow.providers.amazon.aws.triggers.base import AwsBaseWaiterTrigger
+
+if TYPE_CHECKING:
+    from airflow.providers.amazon.aws.hooks.base_aws import AwsGenericHook
+
+
+class KinesisAnalyticsV2ApplicationOperationCompleteTrigger(AwsBaseWaiterTrigger):
+    """
+    Trigger when a Managed Service for Apache Flink application Start or Stop is complete.
+
+    :param application_name: Application name.
+    :param waiter_name: The name of the waiter for stop or start application.
+    :param waiter_delay: The amount of time in seconds to wait between attempts. (default: 120)
+    :param waiter_max_attempts: The maximum number of attempts to be made. (default: 75)
+    :param aws_conn_id: The Airflow connection used for AWS credentials.
+    """
+
+    def __init__(
+        self,
+        application_name: str,
+        waiter_name: str,
+        waiter_delay: int = 120,
+        waiter_max_attempts: int = 75,
+        aws_conn_id: str | None = "aws_default",
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            serialized_fields={"application_name": application_name, "waiter_name": waiter_name},
+            waiter_name=waiter_name,
+            waiter_args={"ApplicationName": application_name},
+            failure_message=f"AWS Managed Service for Apache Flink Application {application_name} failed.",
+            status_message=f"Status of AWS Managed Service for Apache Flink Application {application_name} is",
+            status_queries=["ApplicationDetail.ApplicationStatus"],
+            return_key="application_name",
+            return_value=application_name,
+            waiter_delay=waiter_delay,
+            waiter_max_attempts=waiter_max_attempts,
+            aws_conn_id=aws_conn_id,
+            **kwargs,
+        )
+
+    def hook(self) -> AwsGenericHook:
+        return KinesisAnalyticsV2Hook(
+            aws_conn_id=self.aws_conn_id,
+            region_name=self.region_name,
+            verify=self.verify,
+            config=self.botocore_config,
+        )