How to use Python's AirFlow class library to create a custom scheduling process

How to use Python's AirFlow class library to create a custom scheduling process Airflow is an open source task scheduling and workflow arrangement platform, which can help you easily manage complex workflows.Its scheduling process consists of dependencies between tasks, and each task can be performed in parallel, retry, and error handling.This article will introduce how to use Python's AirFlow class library to create a custom scheduling process. Step 1: Install Airflow First, you need to install AirFlow in the system.You can use the PIP command to install the AirFlow class library: python pip install apache-airflow Step 2: Initialize AirFlow database After installing AirFlow, you need to initialize the AirFlow database.Use the following commands to complete the initialization: bash airflow db init Step 3: Create Dag (Direct Acyclic Graph) DAG is a working stream composed of tasks and dependency relationships.Create a Python script to define and configure the scheduling process.In the script, you need to import the necessary modules and classes, and configure the parameters and tasks of DAG.The following is an example code: python from datetime import datetime from airflow import DAG from airflow.operators.python_operator import PythonOperator # Define the parameters of DAG default_args = { 'start_date': datetime(2022, 1, 1), 'retries': 3, 'retry_delay': timedelta(minutes=5) } # Define task function def task1(): print("Task 1 executed.") def task2(): print("Task 2 executed.") def task3(): print("Task 3 executed.") # Create DAG object dag = DAG( 'custom_dag', schedule_interval='@daily', default_args=default_args ) # 创 创 task_1 = PythonOperator( task_id='task_1', python_callable=task1, dag=dag ) task_2 = PythonOperator( task_id='task_2', python_callable=task2, dag=dag ) task_3 = PythonOperator( task_id='task_3', python_callable=task3, dag=dag ) # Define the dependence between tasks task_1 >> task_2 >> task_3 In the above code, we first define some default parameters, such as the start date, number of retry and delay time of the task.We then define three task functions TASK1, Task2 and Task3, and each function performs different tasks.Next, we created a DAG object called Custom_dag and set up scheduling interval and default parameters.Later, according to the defined dependence, the order between tasks was set up, where TASK_1 relies on task_2, and task_2 relies on task_3. Step 4: Run the scheduling process Save the script and use the following command to start the AirFlow scheduling program: bash airflow scheduler This command will run the AirFlow scheduling program and automatically trigger the execution of the task according to the scheduling interval of DAG. Step 5: The execution of the monitoring task You can use the following command to start the web server of AirFlow to monitor the execution of the task: bash airflow webserver Then, by visiting http: // localhost: 8080, you will be able to view and monitor the execution of the task in the Web interface of Airflow. Summarize By using Python's AirFlow class library, you can easily create a custom scheduling process.By defining the dependency relationship between DAG and tasks, you can flexibly configure the execution order and scheduling plan of the task.Airflow provides powerful functions, such as task retry, error handling and task monitoring to help you manage and schedule complex workflow.