Source code for airflow.providers.google.cloud.triggers.cloud_composer

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from __future__ import annotations

import asyncio
import json
from datetime import datetime
from typing import Any, Sequence

from dateutil import parser
from google.cloud.orchestration.airflow.service_v1.types import ExecuteAirflowCommandResponse

from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.cloud_composer import CloudComposerAsyncHook
from airflow.triggers.base import BaseTrigger, TriggerEvent


[docs]class CloudComposerExecutionTrigger(BaseTrigger):
    """The trigger handles the async communication with the Google Cloud Composer."""

    def __init__(
        self,
        project_id: str,
        region: str,
        operation_name: str,
        gcp_conn_id: str = "google_cloud_default",
        impersonation_chain: str | Sequence[str] | None = None,
        pooling_period_seconds: int = 30,
    ):
        super().__init__()
        self.project_id = project_id
        self.region = region
        self.operation_name = operation_name
        self.gcp_conn_id = gcp_conn_id
        self.impersonation_chain = impersonation_chain
        self.pooling_period_seconds = pooling_period_seconds

        self.gcp_hook = CloudComposerAsyncHook(
            gcp_conn_id=self.gcp_conn_id,
            impersonation_chain=self.impersonation_chain,
        )

[docs]    def serialize(self) -> tuple[str, dict[str, Any]]:
        return (
            "airflow.providers.google.cloud.triggers.cloud_composer.CloudComposerExecutionTrigger",
            {
                "project_id": self.project_id,
                "region": self.region,
                "operation_name": self.operation_name,
                "gcp_conn_id": self.gcp_conn_id,
                "impersonation_chain": self.impersonation_chain,
                "pooling_period_seconds": self.pooling_period_seconds,
            },
        )

[docs]    async def run(self):
        while True:
            operation = await self.gcp_hook.get_operation(operation_name=self.operation_name)
            if operation.done:
                break
            elif operation.error.message:
                raise AirflowException(f"Cloud Composer Environment error: {operation.error.message}")
            await asyncio.sleep(self.pooling_period_seconds)
        yield TriggerEvent(
            {
                "operation_name": operation.name,
                "operation_done": operation.done,
            }
        )


[docs]class CloudComposerAirflowCLICommandTrigger(BaseTrigger):
    """The trigger wait for the Airflow CLI command result."""

    def __init__(
        self,
        project_id: str,
        region: str,
        environment_id: str,
        execution_cmd_info: dict,
        gcp_conn_id: str = "google_cloud_default",
        impersonation_chain: str | Sequence[str] | None = None,
        poll_interval: int = 10,
    ):
        super().__init__()
        self.project_id = project_id
        self.region = region
        self.environment_id = environment_id
        self.execution_cmd_info = execution_cmd_info
        self.gcp_conn_id = gcp_conn_id
        self.impersonation_chain = impersonation_chain
        self.poll_interval = poll_interval

        self.gcp_hook = CloudComposerAsyncHook(
            gcp_conn_id=self.gcp_conn_id,
            impersonation_chain=self.impersonation_chain,
        )

[docs]    def serialize(self) -> tuple[str, dict[str, Any]]:
        return (
            "airflow.providers.google.cloud.triggers.cloud_composer.CloudComposerAirflowCLICommandTrigger",
            {
                "project_id": self.project_id,
                "region": self.region,
                "environment_id": self.environment_id,
                "execution_cmd_info": self.execution_cmd_info,
                "gcp_conn_id": self.gcp_conn_id,
                "impersonation_chain": self.impersonation_chain,
                "poll_interval": self.poll_interval,
            },
        )

[docs]    async def run(self):
        try:
            result = await self.gcp_hook.wait_command_execution_result(
                project_id=self.project_id,
                region=self.region,
                environment_id=self.environment_id,
                execution_cmd_info=self.execution_cmd_info,
                poll_interval=self.poll_interval,
            )
        except AirflowException as ex:
            yield TriggerEvent(
                {
                    "status": "error",
                    "message": str(ex),
                }
            )
            return

        yield TriggerEvent(
            {
                "status": "success",
                "result": result,
            }
        )
        return


[docs]class CloudComposerDAGRunTrigger(BaseTrigger):
    """The trigger wait for the DAG run completion."""

    def __init__(
        self,
        project_id: str,
        region: str,
        environment_id: str,
        composer_dag_id: str,
        start_date: datetime,
        end_date: datetime,
        allowed_states: list[str],
        gcp_conn_id: str = "google_cloud_default",
        impersonation_chain: str | Sequence[str] | None = None,
        poll_interval: int = 10,
    ):
        super().__init__()
        self.project_id = project_id
        self.region = region
        self.environment_id = environment_id
        self.composer_dag_id = composer_dag_id
        self.start_date = start_date
        self.end_date = end_date
        self.allowed_states = allowed_states
        self.gcp_conn_id = gcp_conn_id
        self.impersonation_chain = impersonation_chain
        self.poll_interval = poll_interval

        self.gcp_hook = CloudComposerAsyncHook(
            gcp_conn_id=self.gcp_conn_id,
            impersonation_chain=self.impersonation_chain,
        )

[docs]    def serialize(self) -> tuple[str, dict[str, Any]]:
        return (
            "airflow.providers.google.cloud.triggers.cloud_composer.CloudComposerDAGRunTrigger",
            {
                "project_id": self.project_id,
                "region": self.region,
                "environment_id": self.environment_id,
                "composer_dag_id": self.composer_dag_id,
                "start_date": self.start_date,
                "end_date": self.end_date,
                "allowed_states": self.allowed_states,
                "gcp_conn_id": self.gcp_conn_id,
                "impersonation_chain": self.impersonation_chain,
                "poll_interval": self.poll_interval,
            },
        )

    async def _pull_dag_runs(self) -> list[dict]:
        """Pull the list of dag runs."""
        dag_runs_cmd = await self.gcp_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 = await self.gcp_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["execution_date"]).timestamp()
                < end_date.timestamp()
            ) and dag_run["state"] not in self.allowed_states:
                return False
        return True

[docs]    async def run(self):
        try:
            while True:
                if datetime.now(self.end_date.tzinfo).timestamp() > self.end_date.timestamp():
                    dag_runs = await self._pull_dag_runs()

                    self.log.info("Sensor waits for allowed states: %s", self.allowed_states)
                    if self._check_dag_runs_states(
                        dag_runs=dag_runs,
                        start_date=self.start_date,
                        end_date=self.end_date,
                    ):
                        yield TriggerEvent({"status": "success"})
                        return
                self.log.info("Sleeping for %s seconds.", self.poll_interval)
                await asyncio.sleep(self.poll_interval)
        except AirflowException as ex:
            yield TriggerEvent(
                {
                    "status": "error",
                    "message": str(ex),
                }
            )
            return