This makes it easier to run distinct environments for say production and development, tests, or for different teams or security profiles. Some possible setting are database to use as a backend and what executor to use to fire off tasks. class DagBag ( LoggingMixin ): """ A dagbag is a collection of dags, parsed out of a folder tree and has high level configuration settings. from _future_ import annotations import hashlib import importlib import importlib.machinery import importlib.util import os import sys import textwrap import traceback import warnings import zipfile from datetime import datetime, timedelta from typing import TYPE_CHECKING, NamedTuple from sqlalchemy.exc import OperationalError from sqlalchemy.orm import Session from tabulate import tabulate from airflow import settings from nfiguration import conf from airflow.exceptions import ( AirflowClusterPolicyError, AirflowClusterPolicySkipDag, AirflowClusterPolicyViolation, AirflowDagCycleException, AirflowDagDuplicatedIdException, RemovedInAirflow3Warning, ) from airflow.stats import Stats from airflow.utils import timezone from _cycle_tester import check_cycle from import get_docs_url from import correct_maybe_zipped, list_py_file_paths, might_contain_dag from _mixin import LoggingMixin from import MAX_DB_RETRIES, run_with_db_retries from import NEW_SESSION, provide_session from import timeout from import NOTSET, ArgNotSet if TYPE_CHECKING : import pathlib from import DAG See the License for the # specific language governing permissions and limitations # under the License. You may obtain a copy of the License at # 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. 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. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. datetime ( 2022, 1, 1 ), schedule =, tags =, ) as dag : start = EmptyOperator ( task_id = "start", ) section_1 = SubDagOperator ( task_id = "section-1", subdag = subdag ( DAG_NAME, "section-1", dag. Defaults to """ get_ip = GetRequestOperator ( task_id = "get_ip", url = "" ) ( multiple_outputs = True ) def prepare_email ( raw_json : dict ) -> dict : external_ip = raw_json return, start_date = datetime. datetime ( 2021, 1, 1, tz = "UTC" ), catchup = False, tags =, ) def example_dag_decorator ( email : str = ): """ DAG to send server IP to email. Schedule interval put in place, the logical date is going to indicate the timeĪt which it marks the start of the data interval, where the DAG run’s startĭate would then be the logical date + scheduled ( schedule = None, start_date = pendulum. However, when the DAG is being automatically scheduled, with certain Logical is because of the abstract nature of it having multiple meanings,ĭepending on the context of the DAG run itself.įor example, if a DAG run is manually triggered by the user, its logical date would be theĭate and time of which the DAG run was triggered, and the value should be equal (formally known as execution date), which describes the intended time aĭAG run is scheduled or triggered. Run’s start and end date, there is another date called logical date This period describes the time when the DAG actually ‘ran.’ Aside from the DAG Tasks specified inside a DAG are also instantiated intoĪ DAG run will have a start date when it starts, and end date when it ends. In much the same way a DAG instantiates into a DAG Run every time it’s run, Run will have one data interval covering a single day in that 3 month period,Īnd that data interval is all the tasks, operators and sensors inside the DAG Those DAG Runs will all have been started on the same actual day, but each DAG The previous 3 months of data-no problem, since Airflow can backfill the DAGĪnd run copies of it for every day in those previous 3 months, all at once. It’s been rewritten, and you want to run it on Same DAG, and each has a defined data interval, which identifies the period ofĪs an example of why this is useful, consider writing a DAG that processes aĭaily set of experimental data. If schedule is not enough to express the DAG’s schedule, see Timetables.įor more information on logical date, see Data Interval andĮvery time you run a DAG, you are creating a new instance of that DAG whichĪirflow calls a DAG Run. For more information on schedule values, see DAG Run.
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