The application of the AirFlow class library in the management of Python Workflow

Airflow is an open source Python class library for creating, scheduling and monitoring workflows.It provides an easy -to -use interface to define the task and dependencies of the workflow, and automatically manage its execution.The main features of Airflow include scalability, flexible task scheduling and rich plug -in ecosystems, making it the preferred tool in Python workflow management. Using AirFlow, the workflow can be defined by writing the Python code, called Dag (Direct Acyclic Graph).DAG is a set of rings without diagrams. The nodes indicate tasks, and they represent the dependence between tasks.We can use Python code to define DAG to specify the execution order and dependencies of the specified task. Here are a sample code created and running DAG with AirFlow: python from airflow import DAG from airflow.operators.python_operator import PythonOperator from datetime import datetime # Definition DAG dag = DAG( 'My_dag', # DAG name description = 'A simple DAG example', # DAG description schedule_interval = '0 0 * * * *', # time scheduling start_date = Datetime (2022, 1, 1) # DAG starts ) # Definition task def task1(): print("Hello, I'm task 1!") def task2(): print("Hello, I'm task 2!") # Create task instance task1 = PythonOperator( task_id='task1', python_callable=task1, dag=dag ) task2 = PythonOperator( task_id='task2', python_callable=task2, dag=dag ) # Define the dependence between tasks task1 >> task2 In the above example, we define a DAG called "My_dag", which contains two tasks "Task1" and "Task2", and the task "Task2" depends on the task "TASK1".We also designated DAG's scheduling interval to daily midnight ('0 0 * * * *'), and set its starting date to January 1, 2022. To run this DAG, we need to configure the related parameters of AirFlow.First of all, we need a meta database of AirFlow for metadata for storing and managing workflows.We can initialize the metadata database by running the following command: shell airflow initdb Then, we need to start the scheduling program and web server of AirFlow: shell airflow scheduler airflow webserver After completing the above steps, we can manage and monitor the workflow through the web interface of AirFlow.We can view the status, execution records and logs of DAG, manually trigger the operation of DAG, and configure other AirFlow -related settings. In summary, AirFlow is a powerful Python class library for management of workflow creation, scheduling and monitoring.It provides rich functions and flexible programming interfaces, allowing us to easily define and manage complex workflows.Through AirFlow, we can better organize and dispatch tasks, improve work efficiency, and provide reliable task monitoring and log records.Regardless of whether it is a simple task scheduling or a complex data channel, Airflow is an ideal choice.