Exploration of the advanced characteristics and best practice of the AirFlow class library

Exploration of the advanced characteristics and best practice of the AirFlow class library Airflow is an open source work process management system that is used to schedule, monitor and manage complex workflows.It provides an intuitive interface to define, schedule and monitor tasks, as well as a powerful programming API that can be used to write a customized workflow.This article will explore the advanced characteristics and best practice of the AirFlow class library to help readers better use and optimize the workflow. 1. Advanced features of Airflow 1. Dynamic task scheduling: Airflow allows users to dynamically dispatch tasks based on dependencies.The scheduling sequence and dependencies of the task can be defined and adjusted through code and configuration files.This flexibility allows users to intelligently schedule according to the relationship and conditions between tasks to achieve more efficient workflow management. 2. Visual interface: Airflow provides a visualized interface for viewing and monitoring tasks.Through this interface, users can easily view the dependence, scheduling and operating results of the task.This greatly simplifies the monitoring and debugging process of the workflow. 3. Powerful task scheduler: Airflow uses a DAG -based task scheduler, which can easily define the dependent relationship between and manage the task.Users can use the Python code to define tasks, and use API provided by Airflow to organize them into a ringless diagram.The task scheduler automatically schedules the task according to the defined dependencies. 4. Monitoring and alarm function: Airflow provides a complete set of monitoring and alarm functions that can help users monitor the implementation of tasks in real time.Users can configure monitoring indicators and alarm rules, and receive warning information via email, SLACK and other channels.This allows users to discover and solve problems in the task execution process in time. Second, the best practice of Airflow 1. Reasonable use of XCOMS: XCOMS is a mechanism that transmits data and status between tasks in Airflow.When using XCOMS, a large amount of data should be avoided to reduce performance consumption.In addition, it is recommended to clean up the XCOMS that is no longer needed during the task to reduce the occupation of storage space. 2. Reasonably set the task retry strategy: Airflow allows users to define the trial strategy of tasks.When designing a retry strategy, the possible cause of the failure of the task should be considered, and the appropriate number of retries and the time interval of the review should be determined.At the same time, we should try to avoid the circulation dependence between tasks to reduce the number and delay of task trials. 3. Optimize task parallelism: Airflow allows users to perform multiple tasks at the same time to improve the parallelism of the workflow.When setting the mission parallelism, reasonable adjustments should be made according to the performance requirements of system resources and tasks.You can use the scheduler configuration parameters provided by AirFlow to control the number of concurrent executions of the task. 4. Regularly clean up task history: Since AirFlow preserves the execution history of the task, this may occupy a large amount of storage space.In order to save storage resources, it is recommended to clean up the old execution records regularly.You can use the command line tool or API provided by AirFlow to delete historical records that are no longer needed. ------------ The following is a complete code and related configuration about the AirFlow task scheduling: 1. Write dag: DAG (has a ringless diagram) is the basic unit of task scheduling in Airflow.We can use the Python code to define and organize DAG. python from airflow import DAG from airflow.operators.bash_operator import BashOperator # Define the scheduling rules and default parameters of DAG dag = DAG('my_dag', schedule_interval='0 0 * * *', default_args={'owner': 'airflow'}) # Definition task task1 = BashOperator(task_id='task_1', bash_command='echo "Task 1"', dag=dag) task2 = BashOperator(task_id='task_2', bash_command='echo "Task 2"', dag=dag) # Define the dependence between tasks task1 >> task2 In the above code, we define a DAG called `my_dag`.The DAG scheduling rule is performed once a day.We used `Bashoperator` to define two tasks,` task1` and `task2`, and output" Task 1 "and" Task 2 ", respectively.Finally, we use the dependency relationship between the `>>` `operator. 2. Configure Airflow: Before using Airflow, we need to perform some configurations, such as database connection, task scheduling configuration, etc.Airflow configuration files are usually located in `/etc/Airflow/Airflow.cfg`, and we can make corresponding configurations according to actual needs. 3. Run the AirFlow scheduler: After the configuration is completed, we can start the AirFlow scheduler to execute the DAG we defined.Run the following command startup scheduler: shell airflow scheduler The AirFlow scheduler will perform tasks in accordance with the scheduling rules defined by us. 4. Trigger task execution: If we want to manually trigger the task execution, we can run the following command: shell airflow trigger_dag my_dag This will trigger the task in DAG, which executes the DAG named `My_dag`. In summary, AirFlow provides rich advanced characteristics and best practice, allowing us to better manage and optimize the workflow.Through flexible task scheduling, visual interface, powerful task scheduler, and monitoring and alarm functions, we can arrange and manage tasks more efficiently.When using AirFlow, we need to make reasonable configuration and optimization according to actual needs, and follow the best practice to ensure the stability and efficient execution of tasks.