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"""This module contains a Cloud Composer sensor."""
from __future__ import annotations
import json
from collections.abc import Iterable, Sequence
from datetime import datetime, timedelta
from typing import TYPE_CHECKING
from dateutil import parser
from google.cloud.orchestration.airflow.service_v1.types import ExecuteAirflowCommandResponse
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.cloud_composer import CloudComposerHook
from airflow.providers.google.cloud.triggers.cloud_composer import CloudComposerDAGRunTrigger
from airflow.providers.google.common.consts import GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME
from airflow.sensors.base import BaseSensorOperator
from airflow.utils.state import TaskInstanceState
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class CloudComposerDAGRunSensor(BaseSensorOperator):
"""
Check if a DAG run has completed.
:param project_id: Required. The ID of the Google Cloud project that the service belongs to.
:param region: Required. The ID of the Google Cloud region that the service belongs to.
:param environment_id: The name of the Composer environment.
:param composer_dag_id: The ID of executable DAG.
:param allowed_states: Iterable of allowed states, default is ``['success']``.
:param execution_range: execution DAGs time range. Sensor checks DAGs states only for DAGs which were
started in this time range. For yesterday, use [positive!] datetime.timedelta(days=1).
For future, use [negative!] datetime.timedelta(days=-1). For specific time, use list of
datetimes [datetime(2024,3,22,11,0,0), datetime(2024,3,22,12,0,0)].
Or [datetime(2024,3,22,0,0,0)] in this case sensor will check for states from specific time in the
past till current time execution.
Default value datetime.timedelta(days=1).
: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).
:param poll_interval: Optional: Control the rate of the poll for the result of deferrable run.
:param deferrable: Run sensor in deferrable mode.
"""
[docs] template_fields = (
"project_id",
"region",
"environment_id",
"composer_dag_id",
"impersonation_chain",
)
def __init__(
self,
*,
project_id: str,
region: str,
environment_id: str,
composer_dag_id: str,
allowed_states: Iterable[str] | None = None,
execution_range: timedelta | list[datetime] | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: int = 10,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.region = region
self.environment_id = environment_id
self.composer_dag_id = composer_dag_id
self.allowed_states = list(allowed_states) if allowed_states else [TaskInstanceState.SUCCESS.value]
self.execution_range = execution_range
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
self.poll_interval = poll_interval
def _get_logical_dates(self, context) -> tuple[datetime, datetime]:
if isinstance(self.execution_range, timedelta):
if self.execution_range < timedelta(0):
return context["logical_date"], context["logical_date"] - self.execution_range
else:
return context["logical_date"] - self.execution_range, context["logical_date"]
elif isinstance(self.execution_range, list) and len(self.execution_range) > 0:
return self.execution_range[0], self.execution_range[1] if len(
self.execution_range
) > 1 else context["logical_date"]
else:
return context["logical_date"] - timedelta(1), context["logical_date"]
[docs] def poke(self, context: Context) -> bool:
start_date, end_date = self._get_logical_dates(context)
if datetime.now(end_date.tzinfo) < end_date:
return False
dag_runs = self._pull_dag_runs()
self.log.info("Sensor waits for allowed states: %s", self.allowed_states)
allowed_states_status = self._check_dag_runs_states(
dag_runs=dag_runs,
start_date=start_date,
end_date=end_date,
)
return allowed_states_status
def _pull_dag_runs(self) -> list[dict]:
"""Pull the list of dag runs."""
hook = CloudComposerHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
dag_runs_cmd = hook.execute_airflow_command(
project_id=self.project_id,
region=self.region,
environment_id=self.environment_id,
command="dags",
subcommand="list-runs",
parameters=["-d", self.composer_dag_id, "-o", "json"],
)
cmd_result = hook.wait_command_execution_result(
project_id=self.project_id,
region=self.region,
environment_id=self.environment_id,
execution_cmd_info=ExecuteAirflowCommandResponse.to_dict(dag_runs_cmd),
)
dag_runs = json.loads(cmd_result["output"][0]["content"])
return dag_runs
def _check_dag_runs_states(
self,
dag_runs: list[dict],
start_date: datetime,
end_date: datetime,
) -> bool:
for dag_run in dag_runs:
if (
start_date.timestamp()
< parser.parse(dag_run["logical_date"]).timestamp()
< end_date.timestamp()
) and dag_run["state"] not in self.allowed_states:
return False
return True
[docs] def execute(self, context: Context) -> None:
if self.deferrable:
start_date, end_date = self._get_logical_dates(context)
self.defer(
trigger=CloudComposerDAGRunTrigger(
project_id=self.project_id,
region=self.region,
environment_id=self.environment_id,
composer_dag_id=self.composer_dag_id,
start_date=start_date,
end_date=end_date,
allowed_states=self.allowed_states,
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
poll_interval=self.poll_interval,
),
method_name=GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME,
)
super().execute(context)
[docs] def execute_complete(self, context: Context, event: dict):
if event and event["status"] == "error":
raise AirflowException(event["message"])
self.log.info("DAG %s has executed successfully.", self.composer_dag_id)