How to use the AirFlow class library in Python for ETL task scheduling

Airflow is an open source task scheduling and workflow arrangement platform that can help developers easily manage, schedule and monitor ETL (extraction, conversion, loading) tasks.This article will introduce how to use the AIRFLOW class library in Python for ETL task scheduling. ### Installation and configuration Airflow First, Python and PIP need to be installed.You can use the following command to install AirFlow: shell pip install apache-airflow After the installation is completed, you can initialize the AirFlow database through the following command: shell airflow initdb Next, you can start the AirFlow web server and scheduler: shell airflow webserver -p 8080 airflow scheduler Enter the `http: // localhost: 8080` to access the AirFlow web interface. ### Create an ETL task In AirFlow, each task is defined as a DAG (there is no loop map).The following is a simple example: python from datetime import datetime from airflow import DAG from airflow.operators.python_operator import PythonOperator def etl_task(): # ETL task specific implementation code # ... dag = DAG('etl_dag', schedule_interval='0 0 * * *', start_date=datetime(2022, 1, 1)) task = PythonOperator( task_id='etl_task', python_callable=etl_task, dag=dag ) The above code creates a DAG called `etl_dag`, and defines a task called` etl_task`.The specific implementation code of the task should be written in the `ETL_TASK` function. The parameter of `schedule_interval` defines the time interval of task scheduling.In the above code, the task will be executed once every morning. ### mission dependence and scheduling Airflow allows dependency relationships between tasks in DAG.The following is an example: python from datetime import timedelta task1 = PythonOperator( task_id='task1', python_callable=task1_function, dag=dag ) task2 = PythonOperator( task_id='task2', python_callable=task2_function, dag=dag ) task1 >> task2 dag = DAG('etl_dag', schedule_interval='0 0 * * *', start_date=datetime(2022, 1, 1)) In the above code, the `task2` task depends on the` task1` task, that is, the `task1` task is completed before the execution of the` task2` task. `Start_date` Parameters are used to specify the start date of DAG.In the above examples, DAG will start dispatching tasks on January 1, 2022. ### monitoring and management task Airflow provides a web interface for monitoring and management tasks.You can check the task's running status, logs and task dependence in the web interface. In addition, AirFlow also supports functions such as email notifications, alarms, and custom plugins. It can send email notifications when the task fails or success, and perform visual reporting and other operations. ### Summarize Through the AirFlow class library, we can easily schedule ETL mission in Python.Using AirFlow can define the dependency relationship between tasks, the scheduling time of the task flexibly, and the operating status of the operation of the task and the management task through the web interface.