Source code for airflow.providers.google.cloud.sensors.datafusion
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"""This module contains a Google Cloud Data Fusion sensors."""
from __future__ import annotations
from typing import TYPE_CHECKING, Iterable, Sequence
from airflow.exceptions import AirflowException, AirflowNotFoundException
from airflow.providers.google.cloud.hooks.datafusion import DataFusionHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class CloudDataFusionPipelineStateSensor(BaseSensorOperator):
"""
Check the status of the pipeline in the Google Cloud Data Fusion.
:param pipeline_name: Your pipeline name.
:param pipeline_id: Your pipeline ID.
:param expected_statuses: State that is expected
:param failure_statuses: State that will terminate the sensor with an exception
:param instance_name: The name of the instance.
:param location: The Cloud Data Fusion location in which to handle the request.
:param project_id: The ID of the Google Cloud project that the instance belongs to.
:param namespace: If your pipeline belongs to a Basic edition instance, the namespace ID
is always default. If your pipeline belongs to an Enterprise edition instance, you
can create a namespace.
:param gcp_conn_id: The connection ID to use when fetching connection info.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = ("pipeline_id",)
def __init__(
self,
pipeline_name: str,
pipeline_id: str,
expected_statuses: Iterable[str],
instance_name: str,
location: str,
failure_statuses: Iterable[str] | None = None,
project_id: str | None = None,
namespace: str = "default",
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.pipeline_name = pipeline_name
self.pipeline_id = pipeline_id
self.expected_statuses = expected_statuses
self.failure_statuses = failure_statuses
self.instance_name = instance_name
self.location = location
self.project_id = project_id
self.namespace = namespace
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def poke(self, context: Context) -> bool:
self.log.info(
"Waiting for pipeline %s to be in one of the states: %s.",
self.pipeline_id,
", ".join(self.expected_statuses),
)
hook = DataFusionHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
instance = hook.get_instance(
instance_name=self.instance_name,
location=self.location,
project_id=self.project_id,
)
api_url = instance["apiEndpoint"]
pipeline_status = None
try:
pipeline_workflow = hook.get_pipeline_workflow(
pipeline_name=self.pipeline_name,
instance_url=api_url,
pipeline_id=self.pipeline_id,
namespace=self.namespace,
)
pipeline_status = pipeline_workflow["status"]
except AirflowNotFoundException:
raise AirflowException("Specified Pipeline ID was not found.")
except AirflowException:
pass # Because the pipeline may not be visible in system yet
if pipeline_status is not None:
if self.failure_statuses and pipeline_status in self.failure_statuses:
raise AirflowException(
f"Pipeline with id '{self.pipeline_id}' state is: {pipeline_status}. "
f"Terminating sensor..."
)
self.log.debug(
"Current status of the pipeline workflow for %s: %s.", self.pipeline_id, pipeline_status
)
return pipeline_status in self.expected_statuses
