# 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.
"""This module contains AWS MWAA operators."""
from __future__ import annotations
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.mwaa import MwaaHook
from airflow.providers.amazon.aws.operators.base_aws import AwsBaseOperator
from airflow.providers.amazon.aws.triggers.mwaa import MwaaDagRunCompletedTrigger
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 MwaaTriggerDagRunOperator(AwsBaseOperator[MwaaHook]):
"""
Trigger a Dag Run for a Dag in an Amazon MWAA environment.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:MwaaTriggerDagRunOperator`
:param env_name: The MWAA environment name (templated)
:param trigger_dag_id: The ID of the DAG to be triggered (templated)
:param trigger_run_id: The Run ID. This together with trigger_dag_id are a unique key. (templated)
:param logical_date: The logical date (previously called execution date). This is the time or interval
covered by this DAG run, according to the DAG definition. This together with trigger_dag_id are a
unique key. (templated)
:param data_interval_start: The beginning of the interval the DAG run covers
:param data_interval_end: The end of the interval the DAG run covers
:param conf: Additional configuration parameters. The value of this field can be set only when creating
the object. (templated)
:param note: Contains manually entered notes by the user about the DagRun. (templated)
:param wait_for_completion: Whether to wait for DAG run to stop. (default: False)
:param waiter_delay: Time in seconds to wait between status checks. (default: 120)
:param waiter_max_attempts: Maximum number of attempts to check for DAG run completion. (default: 720)
:param deferrable: If True, the operator will wait asynchronously for the DAG run 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 or not 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 = MwaaHook
[docs]
template_fields: Sequence[str] = aws_template_fields(
"env_name",
"trigger_dag_id",
"trigger_run_id",
"logical_date",
"data_interval_start",
"data_interval_end",
"conf",
"note",
)
[docs]
template_fields_renderers = {"conf": "json"}
def __init__(
self,
*,
env_name: str,
trigger_dag_id: str,
trigger_run_id: str | None = None,
logical_date: str | None = None,
data_interval_start: str | None = None,
data_interval_end: str | None = None,
conf: dict | None = None,
note: str | None = None,
wait_for_completion: bool = False,
waiter_delay: int = 60,
waiter_max_attempts: int = 720,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
):
super().__init__(**kwargs)
[docs]
self.env_name = env_name
[docs]
self.trigger_dag_id = trigger_dag_id
[docs]
self.trigger_run_id = trigger_run_id
[docs]
self.logical_date = logical_date
[docs]
self.data_interval_start = data_interval_start
[docs]
self.data_interval_end = data_interval_end
[docs]
self.conf = conf if conf else {}
[docs]
self.wait_for_completion = wait_for_completion
[docs]
self.waiter_delay = waiter_delay
[docs]
self.waiter_max_attempts = waiter_max_attempts
[docs]
self.deferrable = deferrable
[docs]
def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> dict:
validated_event = validate_execute_complete_event(event)
if validated_event["status"] != "success":
raise AirflowException(f"DAG run failed: {validated_event}")
dag_run_id = validated_event["dag_run_id"]
self.log.info("DAG run %s of DAG %s completed", dag_run_id, self.trigger_dag_id)
return self.hook.invoke_rest_api(
env_name=self.env_name, path=f"/dags/{self.trigger_dag_id}/dagRuns/{dag_run_id}", method="GET"
)
[docs]
def execute(self, context: Context) -> dict:
"""
Trigger a Dag Run for the Dag in the Amazon MWAA environment.
:param context: the Context object
:return: dict with information about the Dag run
For details of the returned dict, see :py:meth:`botocore.client.MWAA.invoke_rest_api`
"""
response = self.hook.invoke_rest_api(
env_name=self.env_name,
path=f"/dags/{self.trigger_dag_id}/dagRuns",
method="POST",
body={
"dag_run_id": self.trigger_run_id,
"logical_date": self.logical_date,
"data_interval_start": self.data_interval_start,
"data_interval_end": self.data_interval_end,
"conf": self.conf,
"note": self.note,
},
)
dag_run_id = response["RestApiResponse"]["dag_run_id"]
self.log.info("DAG run %s of DAG %s created", dag_run_id, self.trigger_dag_id)
task_description = f"DAG run {dag_run_id} of DAG {self.trigger_dag_id} to complete"
if self.deferrable:
self.log.info("Deferring for %s", task_description)
self.defer(
trigger=MwaaDagRunCompletedTrigger(
external_env_name=self.env_name,
external_dag_id=self.trigger_dag_id,
external_dag_run_id=dag_run_id,
waiter_delay=self.waiter_delay,
waiter_max_attempts=self.waiter_max_attempts,
aws_conn_id=self.aws_conn_id,
),
method_name="execute_complete",
)
elif self.wait_for_completion:
self.log.info("Waiting for %s", task_description)
api_kwargs = {
"Name": self.env_name,
"Path": f"/dags/{self.trigger_dag_id}/dagRuns/{dag_run_id}",
"Method": "GET",
}
self.hook.get_waiter("mwaa_dag_run_complete").wait(
**api_kwargs,
WaiterConfig={"Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts},
)
return response