Application Guide of Python's AirFlow Class Library in Big Data Processing

Application Guide of Python's AirFlow Class Library in Big Data Processing preface: With the rapid development of big data technology, the demand for large -scale data has become increasingly urgent.As an open source work process management library, Airflow is widely used in big data processing.It provides a reliable and easy -to -use work process arrangement method that breaks complex tasks into managable task units, and automated scheduling and monitoring tasks.This article will introduce the application guidelines of the AirFlow class library in big data processing, and provide the necessary programming code and related configuration description. 1. Introduction to Airflow: Airflow is a Python class library developed by Airbnb for creation, scheduling and monitoring workflow.It organizes tasks in a data stream diagram and provides an intuitive user interface to manage the dependencies and scheduling time of the task.Airflow has many powerful characteristics, including reliability, ease of use, scalability and visualization, etc., which have been widely used in big data processing. 2. The application scenario of AirFlow: AirFlow is suitable for various big data processing scenarios, including ETL (Extract, Transform, LOAD) process, the construction of data warehouses, training and reasoning of machine learning models, etc.It provides rich task types and operators, which can be seamlessly integrated with common big data tools and platforms, such as Hadoop, Spark, Hive, and Presto. 3. Core concept of AirFlow: Several core concepts of Airflow need to understand: -DAGS (Direct ACYCLIC Graphs): Data stream diagram, used to organize dependency relationships between tasks. -Tasks: task unit, which represents specific operations to be performed. -Operators: task operators are used to define the specific logic of tasks. -Sensors: Sensor, for waiting for external conditions to meet, perform tasks. -EXECUTORS: Task actuator for distributed execution tasks. -Scheduler: The scheduler, the execution order and time of automatic calculation of the task. 4. Programming example of AirFlow: The following is a simple Airflow example to demonstrate how to create and dispatch a workflow: python from airflow import DAG from airflow.operators.bash_operator import BashOperator from datetime import datetime, timedelta default_args = { 'owner': 'airflow', 'start_date': datetime(2022, 1, 1), 'retries': 1, 'retry_delay': timedelta(minutes=5), } dag = DAG('data_processing', default_args=default_args, schedule_interval='0 0 * * *') task1 = BashOperator( task_id='data_extraction', bash_command='python extract.py', dag=dag, ) task2 = BashOperator( task_id='data_transformation', bash_command='python transform.py', dag=dag, ) task3 = BashOperator( task_id='data_loading', bash_command='python load.py', dag=dag, ) task2.set_upstream(task1) task3.set_upstream(task2) The above code creates a workflow containing three tasks, which are used for data extraction, data conversion, and data loading.The dependencies between tasks are defined by the method of `set_upstream ()`.In this example, Mission 2 relies on the completion of mission 1, and task 3 rely on the completion of Mission 2.With the parameter of the `schedule_interVal` parameter, we can define the scheduling time of the task.In this example, the task will be executed once in the early morning. 5. The configuration description of AirFlow: Before using Airflow, you need to perform some configuration settings.The main configuration file is `Airflow.cfg`, which contains the global configuration options of AirFlow, such as the type of task scheduler, database connection, log configuration, etc.The configuration file also contains configuration items for each component, such as schedules, actuators, task logs, etc.According to needs, you can adjust these configuration items to meet the needs of big data processing. Summarize: The AirFlow class library is a powerful and flexible tool in big data processing.It provides an efficient workflow arrangement to help us better manage and dispatch large -scale tasks.This article introduces the basic concepts, application scenarios, programming examples, and configuration descriptions of AirFlow. I hope to help readers better understand and apply the value of Airflow in big data processing.