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from __future__ import annotations
import asyncio
from typing import Any, Sequence
from google.cloud.dataflow_v1beta3 import JobState
from airflow.providers.google.cloud.hooks.dataflow import AsyncDataflowHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]DEFAULT_DATAFLOW_LOCATION = "us-central1"
[docs]class TemplateJobStartTrigger(BaseTrigger):
"""Dataflow trigger to check if templated job has been finished.
:param project_id: Required. the Google Cloud project ID in which the job was started.
:param job_id: Required. ID of the job.
:param location: Optional. the location where job is executed. If set to None then
the value of DEFAULT_DATAFLOW_LOCATION will be used
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
: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).
:param cancel_timeout: Optional. How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed.
"""
def __init__(
self,
job_id: str,
project_id: str | None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
impersonation_chain: str | Sequence[str] | None = None,
cancel_timeout: int | None = 5 * 60,
):
super().__init__()
self.project_id = project_id
self.job_id = job_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.poll_sleep = poll_sleep
self.impersonation_chain = impersonation_chain
self.cancel_timeout = cancel_timeout
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes class arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.dataflow.TemplateJobStartTrigger",
{
"project_id": self.project_id,
"job_id": self.job_id,
"location": self.location,
"gcp_conn_id": self.gcp_conn_id,
"poll_sleep": self.poll_sleep,
"impersonation_chain": self.impersonation_chain,
"cancel_timeout": self.cancel_timeout,
},
)
[docs] async def run(self):
"""
Main loop of the class in where it is fetching the job status and yields certain Event.
If the job has status success then it yields TriggerEvent with success status, if job has
status failed - with error status. In any other case Trigger will wait for specified
amount of time stored in self.poll_sleep variable.
"""
hook = self._get_async_hook()
while True:
try:
status = await hook.get_job_status(
project_id=self.project_id,
job_id=self.job_id,
location=self.location,
)
if status == JobState.JOB_STATE_DONE:
yield TriggerEvent(
{
"job_id": self.job_id,
"status": "success",
"message": "Job completed",
}
)
return
elif status == JobState.JOB_STATE_FAILED:
yield TriggerEvent(
{
"status": "error",
"message": f"Dataflow job with id {self.job_id} has failed its execution",
}
)
return
elif status == JobState.JOB_STATE_STOPPED:
yield TriggerEvent(
{
"status": "stopped",
"message": f"Dataflow job with id {self.job_id} was stopped",
}
)
return
else:
self.log.info("Job is still running...")
self.log.info("Current job status is: %s", status)
self.log.info("Sleeping for %s seconds.", self.poll_sleep)
await asyncio.sleep(self.poll_sleep)
except Exception as e:
self.log.exception("Exception occurred while checking for job completion.")
yield TriggerEvent({"status": "error", "message": str(e)})
return
def _get_async_hook(self) -> AsyncDataflowHook:
return AsyncDataflowHook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.cancel_timeout,
)
