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#
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#
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from typing import TYPE_CHECKING, Optional, Sequence, Set, Tuple, Union
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.workflows.executions_v1beta import Execution
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.workflows import WorkflowsHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class WorkflowExecutionSensor(BaseSensorOperator):
"""
Checks state of an execution for the given ``workflow_id`` and ``execution_id``.
:param workflow_id: Required. The ID of the workflow.
:param execution_id: Required. The ID of the execution.
:param project_id: Required. The ID of the Google Cloud project the cluster belongs to.
:param location: Required. The Cloud Dataproc region in which to handle the request.
:param success_states: Execution states to be considered as successful, by default
it's only ``SUCCEEDED`` state
:param failure_states: Execution states to be considered as failures, by default
they are ``FAILED`` and ``CANCELLED`` states.
:param retry: A retry object used to retry requests. If ``None`` is specified, requests will not be
retried.
:param request_timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
``retry`` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
"""
[docs] template_fields: Sequence[str] = ("location", "workflow_id", "execution_id")
def __init__(
self,
*,
workflow_id: str,
execution_id: str,
location: str,
project_id: Optional[str] = None,
success_states: Optional[Set[Execution.State]] = None,
failure_states: Optional[Set[Execution.State]] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
request_timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.success_states = success_states or {Execution.State(Execution.State.SUCCEEDED)}
self.failure_states = failure_states or {
Execution.State(Execution.State.FAILED),
Execution.State(Execution.State.CANCELLED),
}
self.workflow_id = workflow_id
self.execution_id = execution_id
self.location = location
self.project_id = project_id
self.retry = retry
self.request_timeout = request_timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def poke(self, context: 'Context'):
hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
self.log.info("Checking state of execution %s for workflow %s", self.execution_id, self.workflow_id)
execution: Execution = hook.get_execution(
workflow_id=self.workflow_id,
execution_id=self.execution_id,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.request_timeout,
metadata=self.metadata,
)
state = execution.state
if state in self.failure_states:
raise AirflowException(
f"Execution {self.execution_id} for workflow {self.execution_id} "
f"failed and is in `{state}` state",
)
if state in self.success_states:
self.log.info(
"Execution %s for workflow %s completed with state: %s",
self.execution_id,
self.workflow_id,
state,
)
return True
self.log.info(
"Execution %s for workflow %s does not completed yet, current state: %s",
self.execution_id,
self.workflow_id,
state,
)
return False
