# 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
import time
import warnings
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.models import BaseOperator, BaseOperatorLink, XCom
from airflow.providers.microsoft.azure.hooks.data_factory import (
AzureDataFactoryHook,
AzureDataFactoryPipelineRunException,
AzureDataFactoryPipelineRunStatus,
get_field,
)
from airflow.providers.microsoft.azure.triggers.data_factory import AzureDataFactoryTrigger
from airflow.utils.log.logging_mixin import LoggingMixin
if TYPE_CHECKING:
from airflow.models.taskinstancekey import TaskInstanceKey
from airflow.utils.context import Context
[docs]class AzureDataFactoryPipelineRunLink(LoggingMixin, BaseOperatorLink):
"""Construct a link to monitor a pipeline run in Azure Data Factory."""
[docs] name = "Monitor Pipeline Run"
[docs] def get_link(
self,
operator: BaseOperator,
*,
ti_key: TaskInstanceKey,
) -> str:
run_id = XCom.get_value(key="run_id", ti_key=ti_key)
conn_id = operator.azure_data_factory_conn_id # type: ignore
conn = BaseHook.get_connection(conn_id)
extras = conn.extra_dejson
subscription_id = get_field(extras, "subscriptionId") or get_field(
extras, "extra__azure__subscriptionId"
)
if not subscription_id:
raise KeyError(f"Param subscriptionId not found in conn_id '{conn_id}'")
# Both Resource Group Name and Factory Name can either be declared in the Azure Data Factory
# connection or passed directly to the operator.
resource_group_name = operator.resource_group_name or get_field( # type: ignore
extras, "resource_group_name"
)
factory_name = operator.factory_name or get_field(extras, "factory_name") # type: ignore
url = (
f"https://adf.azure.com/en-us/monitoring/pipelineruns/{run_id}"
f"?factory=/subscriptions/{subscription_id}/"
f"resourceGroups/{resource_group_name}/providers/Microsoft.DataFactory/"
f"factories/{factory_name}"
)
return url
[docs]class AzureDataFactoryRunPipelineOperator(BaseOperator):
"""
Execute a data factory pipeline.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AzureDataFactoryRunPipelineOperator`
:param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory.
:param pipeline_name: The name of the pipeline to execute.
:param wait_for_termination: Flag to wait on a pipeline run's termination. By default, this feature is
enabled but could be disabled to perform an asynchronous wait for a long-running pipeline execution
using the ``AzureDataFactoryPipelineRunSensor``.
:param resource_group_name: The resource group name. If a value is not passed in to the operator, the
``AzureDataFactoryHook`` will attempt to use the resource group name provided in the corresponding
connection.
:param factory_name: The data factory name. If a value is not passed in to the operator, the
``AzureDataFactoryHook`` will attempt to use the factory name provided in the corresponding
connection.
:param reference_pipeline_run_id: The pipeline run identifier. If this run ID is specified the parameters
of the specified run will be used to create a new run.
:param is_recovery: Recovery mode flag. If recovery mode is set to `True`, the specified referenced
pipeline run and the new run will be grouped under the same ``groupId``.
:param start_activity_name: In recovery mode, the rerun will start from this activity. If not specified,
all activities will run.
:param start_from_failure: In recovery mode, if set to true, the rerun will start from failed activities.
The property will be used only if ``start_activity_name`` is not specified.
:param parameters: Parameters of the pipeline run. These parameters are referenced in a pipeline via
``@pipeline().parameters.parameterName`` and will be used only if the ``reference_pipeline_run_id`` is
not specified.
:param timeout: Time in seconds to wait for a pipeline to reach a terminal status for non-asynchronous
waits. Used only if ``wait_for_termination`` is True.
:param check_interval: Time in seconds to check on a pipeline run's status for non-asynchronous waits.
Used only if ``wait_for_termination`` is True.
:param deferrable: Run operator in deferrable mode.
"""
[docs] template_fields: Sequence[str] = (
"azure_data_factory_conn_id",
"resource_group_name",
"factory_name",
"pipeline_name",
"reference_pipeline_run_id",
"parameters",
)
[docs] template_fields_renderers = {"parameters": "json"}
def __init__(
self,
*,
pipeline_name: str,
azure_data_factory_conn_id: str = AzureDataFactoryHook.default_conn_name,
resource_group_name: str,
factory_name: str,
wait_for_termination: bool = True,
reference_pipeline_run_id: str | None = None,
is_recovery: bool | None = None,
start_activity_name: str | None = None,
start_from_failure: bool | None = None,
parameters: dict[str, Any] | None = None,
timeout: int = 60 * 60 * 24 * 7,
check_interval: int = 60,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.azure_data_factory_conn_id = azure_data_factory_conn_id
self.pipeline_name = pipeline_name
self.wait_for_termination = wait_for_termination
self.resource_group_name = resource_group_name
self.factory_name = factory_name
self.reference_pipeline_run_id = reference_pipeline_run_id
self.is_recovery = is_recovery
self.start_activity_name = start_activity_name
self.start_from_failure = start_from_failure
self.parameters = parameters
self.timeout = timeout
self.check_interval = check_interval
self.deferrable = deferrable
@cached_property
[docs] def hook(self) -> AzureDataFactoryHook:
"""Create and return an AzureDataFactoryHook (cached)."""
return AzureDataFactoryHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
[docs] def execute(self, context: Context) -> None:
self.log.info("Executing the %s pipeline.", self.pipeline_name)
response = self.hook.run_pipeline(
self.pipeline_name,
self.resource_group_name,
self.factory_name,
reference_pipeline_run_id=self.reference_pipeline_run_id,
is_recovery=self.is_recovery,
start_activity_name=self.start_activity_name,
start_from_failure=self.start_from_failure,
parameters=self.parameters,
)
self.run_id = vars(response)["run_id"]
# Push the ``run_id`` value to XCom regardless of what happens during execution. This allows for
# retrieval the executed pipeline's ``run_id`` for downstream tasks especially if performing an
# asynchronous wait.
context["ti"].xcom_push(key="run_id", value=self.run_id)
if self.wait_for_termination:
if self.deferrable is False:
self.log.info("Waiting for pipeline run %s to terminate.", self.run_id)
if self.hook.wait_for_pipeline_run_status(
self.run_id,
AzureDataFactoryPipelineRunStatus.SUCCEEDED,
self.resource_group_name,
self.factory_name,
check_interval=self.check_interval,
timeout=self.timeout,
):
self.log.info("Pipeline run %s has completed successfully.", self.run_id)
else:
raise AzureDataFactoryPipelineRunException(
f"Pipeline run {self.run_id} has failed or has been cancelled."
)
else:
end_time = time.time() + self.timeout
pipeline_run_status = self.hook.get_pipeline_run_status(
self.run_id, self.resource_group_name, self.factory_name
)
if pipeline_run_status not in AzureDataFactoryPipelineRunStatus.TERMINAL_STATUSES:
self.defer(
timeout=self.execution_timeout,
trigger=AzureDataFactoryTrigger(
azure_data_factory_conn_id=self.azure_data_factory_conn_id,
run_id=self.run_id,
wait_for_termination=self.wait_for_termination,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
check_interval=self.check_interval,
end_time=end_time,
),
method_name="execute_complete",
)
elif pipeline_run_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED:
self.log.info("Pipeline run %s has completed successfully.", self.run_id)
elif pipeline_run_status in AzureDataFactoryPipelineRunStatus.FAILURE_STATES:
raise AzureDataFactoryPipelineRunException(
f"Pipeline run {self.run_id} has failed or has been cancelled."
)
else:
if self.deferrable is True:
warnings.warn(
"Argument `wait_for_termination` is False and `deferrable` is True , hence "
"`deferrable` parameter doesn't have any effect",
UserWarning,
stacklevel=2,
)
[docs] def execute_complete(self, context: Context, event: dict[str, str]) -> None:
"""
Return immediately - callback for when the trigger fires.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
if event:
if event["status"] == "error":
raise AirflowException(event["message"])
self.log.info(event["message"])
[docs] def on_kill(self) -> None:
if self.run_id:
self.hook.cancel_pipeline_run(
run_id=self.run_id,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
)
# Check to ensure the pipeline run was cancelled as expected.
if self.hook.wait_for_pipeline_run_status(
run_id=self.run_id,
expected_statuses=AzureDataFactoryPipelineRunStatus.CANCELLED,
check_interval=self.check_interval,
timeout=self.timeout,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
):
self.log.info("Pipeline run %s has been cancelled successfully.", self.run_id)
else:
raise AzureDataFactoryPipelineRunException(f"Pipeline run {self.run_id} was not cancelled.")