![]() def print_context ( ds = None, ** kwargs ): """Print the Airflow context and ds variable from the context.""" pprint ( kwargs ) print ( ds ) return "Whatever you return gets printed in the logs" run_this = print_context () # ( task_id = "log_sql_query", templates_dict = " ) print ( "Sleeping" ) for _ in range ( 4 ): print ( "Please wait.", flush = True ) sleep ( 1 ) print ( "Finished" ) external_python_task = callable_external_python () # external_classic = ExternalPythonOperator ( task_id = "external_python_classic", python = PATH_TO_PYTHON_BINARY, python_callable = x, ) # virtual_classic = PythonVirtualenvOperator ( task_id = "virtualenv_classic", requirements = "colorama=0.4. I'm struggling to make difference between the start date, the execution date, and backfilling. """ from _future_ import annotations import logging import shutil import sys import tempfile import time from pprint import pprint import pendulum from airflow import DAG from corators import task from import ExternalPythonOperator, PythonVirtualenvOperator I would like to run a simple DAG at a specified date. Be it in a custom Apache Airflow setup or a Google Cloud Composer instance. The following examples show a few popular Airflow operators. """ Example DAG demonstrating the usage of the TaskFlow API to execute Python functions natively and within a virtual environment. Apache Airflow 2.0 Examples - A basic DAG template for any project By Krisjan Oldekamp J(updated on July 2, 2023) 3 min apache-airflow google-cloud-composer python Over the years I've written a lot of Apache Airflow pipelines (DAGs). See the License for the # specific language governing permissions and limitations # under the License. Once Airflow is installed, and the database is initiated, the following steps will help create a simple Apache Airflow DAG. 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. This project is licensed under the Apache License, see the LICENSE file for details.# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. For the available versions, see the tags on the GitHub repository. ![]() The usage will look like: $ airflow-dag build -t airflow/templates -c airflow/configs/dag.yml -o airflow/dagsĪirflow-dag uses Semantic Versioning. The dag yaml configs can be placed in a configs directory in the same home folder, and the output path can then be the Airflow dags folder. You can define your own dag templates too, and put them in a templates directory in Airflow's home folder. ![]() If a template path is not provided, airflow-dag will look into the default templates. t, -template-dir TEXT Path to dag templates You can use the build command to convert a yaml config to an Airflow dag: $ airflow-dag build -t examples/ -c examples/notebook.yml -o examples/out You can use pip to install airflow-dag: $ pip install airflow-dag ![]()
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