trigger_dagrun.py

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import datetime
import json
import time
from typing import Dict, List, Optional, Union

from airflow.api.common.trigger_dag import trigger_dag
from airflow.exceptions import AirflowException, DagNotFound, DagRunAlreadyExists
from airflow.models import BaseOperator, BaseOperatorLink, DagBag, DagModel, DagRun
from airflow.models.xcom import XCom
from airflow.utils import timezone
from airflow.utils.helpers import build_airflow_url_with_query
from airflow.utils.state import State
from airflow.utils.types import DagRunType

XCOM_EXECUTION_DATE_ISO = "trigger_execution_date_iso"
XCOM_RUN_ID = "trigger_run_id"


class TriggerDagRunLink(BaseOperatorLink):
    """
    Operator link for TriggerDagRunOperator. It allows users to access
    DAG triggered by task using TriggerDagRunOperator.
    """

    name = 'Triggered DAG'

    def get_link(self, operator, dttm):
        # Fetch the correct execution date for the triggerED dag which is
        # stored in xcom during execution of the triggerING task.
        trigger_execution_date_iso = XCom.get_one(
            execution_date=dttm, key=XCOM_EXECUTION_DATE_ISO, task_id=operator.task_id, dag_id=operator.dag_id
        )

        query = {"dag_id": operator.trigger_dag_id, "base_date": trigger_execution_date_iso}
        return build_airflow_url_with_query(query)


class TriggerDagRunOperator(BaseOperator):
    """
    Triggers a DAG run for a specified ``dag_id``

    :param trigger_dag_id: The dag_id to trigger (templated).
    :type trigger_dag_id: str
    :param trigger_run_id: The run ID to use for the triggered DAG run (templated).
        If not provided, a run ID will be automatically generated.
    :type trigger_run_id: str
    :param conf: Configuration for the DAG run.
    :type conf: dict
    :param execution_date: Execution date for the dag (templated).
    :type execution_date: str or datetime.datetime
    :param reset_dag_run: Whether or not clear existing dag run if already exists.
        This is useful when backfill or rerun an existing dag run.
        When reset_dag_run=False and dag run exists, DagRunAlreadyExists will be raised.
        When reset_dag_run=True and dag run exists, existing dag run will be cleared to rerun.
    :type reset_dag_run: bool
    :param wait_for_completion: Whether or not wait for dag run completion. (default: False)
    :type wait_for_completion: bool
    :param poke_interval: Poke interval to check dag run status when wait_for_completion=True.
        (default: 60)
    :type poke_interval: int
    :param allowed_states: List of allowed states, default is ``['success']``.
    :type allowed_states: list
    :param failed_states: List of failed or dis-allowed states, default is ``None``.
    :type failed_states: list
    """

    template_fields = ("trigger_dag_id", "trigger_run_id", "execution_date", "conf")
    template_fields_renderers = {"conf": "py"}
    ui_color = "#ffefeb"

    @property
    def operator_extra_links(self):
        """Return operator extra links"""
        return [TriggerDagRunLink()]

    def __init__(
        self,
        *,
        trigger_dag_id: str,
        trigger_run_id: Optional[str] = None,
        conf: Optional[Dict] = None,
        execution_date: Optional[Union[str, datetime.datetime]] = None,
        reset_dag_run: bool = False,
        wait_for_completion: bool = False,
        poke_interval: int = 60,
        allowed_states: Optional[List] = None,
        failed_states: Optional[List] = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.trigger_dag_id = trigger_dag_id
        self.trigger_run_id = trigger_run_id
        self.conf = conf
        self.reset_dag_run = reset_dag_run
        self.wait_for_completion = wait_for_completion
        self.poke_interval = poke_interval
        self.allowed_states = allowed_states or [State.SUCCESS]
        self.failed_states = failed_states or [State.FAILED]

        if execution_date is not None and not isinstance(execution_date, (str, datetime.datetime)):
            raise TypeError(
                "Expected str or datetime.datetime type for execution_date."
                "Got {}".format(type(execution_date))
            )

        self.execution_date = execution_date

        try:
            json.dumps(self.conf)
        except TypeError:
            raise AirflowException("conf parameter should be JSON Serializable")

    def execute(self, context: Dict):
        if isinstance(self.execution_date, datetime.datetime):
            parsed_execution_date = self.execution_date
        elif isinstance(self.execution_date, str):
            parsed_execution_date = timezone.parse(self.execution_date)
        else:
            parsed_execution_date = timezone.utcnow()

        if self.trigger_run_id:
            run_id = self.trigger_run_id
        else:
            run_id = DagRun.generate_run_id(DagRunType.MANUAL, parsed_execution_date)
        try:
            dag_run = trigger_dag(
                dag_id=self.trigger_dag_id,
                run_id=run_id,
                conf=self.conf,
                execution_date=parsed_execution_date,
                replace_microseconds=False,
            )

        except DagRunAlreadyExists as e:
            if self.reset_dag_run:
                self.log.info("Clearing %s on %s", self.trigger_dag_id, parsed_execution_date)

                # Get target dag object and call clear()

                dag_model = DagModel.get_current(self.trigger_dag_id)
                if dag_model is None:
                    raise DagNotFound(f"Dag id {self.trigger_dag_id} not found in DagModel")

                dag_bag = DagBag(dag_folder=dag_model.fileloc, read_dags_from_db=True)
                dag = dag_bag.get_dag(self.trigger_dag_id)
                dag.clear(start_date=parsed_execution_date, end_date=parsed_execution_date)
                dag_run = DagRun.find(dag_id=dag.dag_id, run_id=run_id)[0]
            else:
                raise e

        # Store the execution date from the dag run (either created or found above) to
        # be used when creating the extra link on the webserver.
        ti = context['task_instance']
        ti.xcom_push(key=XCOM_EXECUTION_DATE_ISO, value=dag_run.execution_date.isoformat())
        ti.xcom_push(key=XCOM_RUN_ID, value=dag_run.run_id)

        if self.wait_for_completion:
            # wait for dag to complete
            while True:
                self.log.info(
                    'Waiting for %s on %s to become allowed state %s ...',
                    self.trigger_dag_id,
                    dag_run.execution_date,
                    self.allowed_states,
                )
                time.sleep(self.poke_interval)

                dag_run.refresh_from_db()
                state = dag_run.state
                if state in self.failed_states:
                    raise AirflowException(f"{self.trigger_dag_id} failed with failed states {state}")
                if state in self.allowed_states:
                    self.log.info("%s finished with allowed state %s", self.trigger_dag_id, state)
                    return