Introduction to the AirFlow class library in Python

Introduction to the AirFlow class library in Python Airflow is a Python -based open source workflow management system that can be used to create, dispatch and monitor data processing pipelines.It provides a code similar to code to define the workflow, so that developers can easily write, maintain and schedule complex data processing tasks. This article will introduce how to use the AirFlow class library and provide related programming code and configuration description. Step 1: Install Airflow First, make sure that Python and PIP are installed in your system.Then, install AirFlow by running the following command: pip install apache-airflow Step 2: Initialize AirFlow database After the installation is completed, the SQLite database of Airflow needs to be initialized.Run the following commands in the command line: airflow initdb This will create a SQLite database file and store metadata of AirFlow. Step 3: Start the AirFlow Web server and scheduler In the command line, run the following commands to start the web server and scheduler of Airflow: airflow webserver -p 8080 This will start a local web server that can open the web interface of Airflow by accessing `http:// localhost: 8080`.At the same time, run the following commands to start the scheduler: airflow scheduler The scheduler is responsible for performing the task defined in AirFlow according to the scheduled plan. Step 4: Define tasks and workflows In AirFlow, the task is called Operator, and the workflow is called Dag (Directed Acyclic Graph).Below is a simple example, showing how to define a task and a DAG. First, create a Python script file, such as `my_dag.py`.Import the required classes and functions in the script: python from airflow import DAG from airflow.operators.bash_operator import BashOperator from datetime import datetime Next, define a DAG and specify the name, scheduling time and default parameter for it: python dag = DAG( 'my_dag', description='A simple AirFlow DAG', schedule_interval='0 0 * * *', start_date=datetime(2022, 1, 1), catchup=False ) In this example, the name of the DAG is `My_dag`, describing" a Simple Airflow Dag ".`schedule_interval` specifies the time interval of task execution. This is the daily midnight.`START_DATE` specifies the start date of the task.`catchup = false` means that Airflow will not quickly catch up with the tasks that have not been performed quickly. Next, define a task, which can be any executable operation.In this example, we use the `Bashoperator` to run a simple Bash command: python task = BashOperator( task_id='my_task', bash_command='echo "Hello, AirFlow"', dag=dag ) The name of this task is `my_task`, it will run a Bash command` echo "Hello, Airflow" `.`dag = dag` means that this task belongs to the` my_dag` that we previously defined. Finally, save and run the script: python my_dag.py This will add DAG and tasks to the AIRFlow database and start scheduling and execution. Step 5: Monitor and manage tasks By visiting the web interface of AirFlow (`http:// localhost: 8080`), you can monitor and manage your DAG and tasks.In the web interface, you can view the running status, logs and dependencies of the task.You can also start, stop or re -run the task. This is just an entry guide for the AirFlow class library, and there are many more complex functions and concepts to explore.You can learn more information and example code in Airflow's official documentation (https://airflow.apache.org/docs/). I hope this article can help you get started using the AirFlow class library and start building and managing your own data processing workflow!