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from __future__ import annotations
import asyncio
import time
from typing import Any, AsyncIterator
from airflow.providers.microsoft.azure.hooks.data_factory import (
AzureDataFactoryAsyncHook,
AzureDataFactoryPipelineRunStatus,
)
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]class ADFPipelineRunStatusSensorTrigger(BaseTrigger):
"""
ADFPipelineRunStatusSensorTrigger is fired as deferred class with params to run the
task in trigger worker, when ADF Pipeline is running
:param run_id: The pipeline run identifier.
:param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory.
:param poke_interval: polling period in seconds to check for the status
:param resource_group_name: The resource group name.
:param factory_name: The data factory name.
"""
def __init__(
self,
run_id: str,
azure_data_factory_conn_id: str,
poke_interval: float,
resource_group_name: str | None = None,
factory_name: str | None = None,
):
super().__init__()
self.run_id = run_id
self.azure_data_factory_conn_id = azure_data_factory_conn_id
self.resource_group_name = resource_group_name
self.factory_name = factory_name
self.poke_interval = poke_interval
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes ADFPipelineRunStatusSensorTrigger arguments and classpath."""
return (
"airflow.providers.microsoft.azure.triggers.data_factory.ADFPipelineRunStatusSensorTrigger",
{
"run_id": self.run_id,
"azure_data_factory_conn_id": self.azure_data_factory_conn_id,
"resource_group_name": self.resource_group_name,
"factory_name": self.factory_name,
"poke_interval": self.poke_interval,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]:
"""Make async connection to Azure Data Factory, polls for the pipeline run status"""
hook = AzureDataFactoryAsyncHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
try:
while True:
pipeline_status = await hook.get_adf_pipeline_run_status(
run_id=self.run_id,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
)
if pipeline_status == AzureDataFactoryPipelineRunStatus.FAILED:
yield TriggerEvent(
{"status": "error", "message": f"Pipeline run {self.run_id} has Failed."}
)
elif pipeline_status == AzureDataFactoryPipelineRunStatus.CANCELLED:
msg = f"Pipeline run {self.run_id} has been Cancelled."
yield TriggerEvent({"status": "error", "message": msg})
elif pipeline_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED:
msg = f"Pipeline run {self.run_id} has been Succeeded."
yield TriggerEvent({"status": "success", "message": msg})
await asyncio.sleep(self.poke_interval)
except Exception as e:
yield TriggerEvent({"status": "error", "message": str(e)})
[docs]class AzureDataFactoryTrigger(BaseTrigger):
"""
AzureDataFactoryTrigger is triggered when Azure data factory pipeline job succeeded or failed.
When wait_for_termination is set to False it triggered immediately with success status
:param run_id: Run id of a Azure data pipeline run job.
:param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory.
:param end_time: Time in seconds when triggers will timeout.
:param resource_group_name: The resource group name.
:param factory_name: The data factory name.
:param wait_for_termination: Flag to wait on a pipeline run's termination.
:param check_interval: Time in seconds to check on a pipeline run's status.
"""
def __init__(
self,
run_id: str,
azure_data_factory_conn_id: str,
end_time: float,
resource_group_name: str | None = None,
factory_name: str | None = None,
wait_for_termination: bool = True,
check_interval: int = 60,
):
super().__init__()
self.azure_data_factory_conn_id = azure_data_factory_conn_id
self.check_interval = check_interval
self.run_id = run_id
self.wait_for_termination = wait_for_termination
self.resource_group_name = resource_group_name
self.factory_name = factory_name
self.end_time = end_time
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes AzureDataFactoryTrigger arguments and classpath."""
return (
"airflow.providers.microsoft.azure.triggers.data_factory.AzureDataFactoryTrigger",
{
"azure_data_factory_conn_id": self.azure_data_factory_conn_id,
"check_interval": self.check_interval,
"run_id": self.run_id,
"wait_for_termination": self.wait_for_termination,
"resource_group_name": self.resource_group_name,
"factory_name": self.factory_name,
"end_time": self.end_time,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]:
"""Make async connection to Azure Data Factory, polls for the pipeline run status"""
hook = AzureDataFactoryAsyncHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
try:
pipeline_status = await hook.get_adf_pipeline_run_status(
run_id=self.run_id,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
)
if self.wait_for_termination:
while self.end_time > time.time():
pipeline_status = await hook.get_adf_pipeline_run_status(
run_id=self.run_id,
resource_group_name=self.resource_group_name,
factory_name=self.factory_name,
)
if pipeline_status in AzureDataFactoryPipelineRunStatus.FAILURE_STATES:
yield TriggerEvent(
{
"status": "error",
"message": f"The pipeline run {self.run_id} has {pipeline_status}.",
"run_id": self.run_id,
}
)
elif pipeline_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED:
yield TriggerEvent(
{
"status": "success",
"message": f"The pipeline run {self.run_id} has {pipeline_status}.",
"run_id": self.run_id,
}
)
self.log.info(
"Sleeping for %s. The pipeline state is %s.", self.check_interval, pipeline_status
)
await asyncio.sleep(self.check_interval)
yield TriggerEvent(
{
"status": "error",
"message": f"Timeout: The pipeline run {self.run_id} has {pipeline_status}.",
"run_id": self.run_id,
}
)
else:
yield TriggerEvent(
{
"status": "success",
"message": f"The pipeline run {self.run_id} has {pipeline_status} status.",
"run_id": self.run_id,
}
)
except Exception as e:
yield TriggerEvent({"status": "error", "message": str(e), "run_id": self.run_id})
