Airflow Combat Series: Python-Based Scheduling and Monitoring Workflow Platform

General
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Airflow Combat Series: Python-Based Scheduling and Monitoring Workflow Platform

In this article, we will delve into the world of Airflow, a powerful Python-based scheduling and monitoring workflow platform. We will explore its architecture, advantages, features, and common commands, as well as its ability to handle Extract, Transform, and Load (ETL) tasks and manage task dependencies.

Introduction

Airflow is a platform for scheduling and monitoring workflows, written in Python. It is used internally by Airbnb to create, monitor, and adjust data pipelines. Any workflow can run on this platform, written in Python, making it a versatile tool for workflow developers. Airflow provides an easy way to create, maintain, and periodically schedule workflows (Directed Acyclic Graphs or DAGs).

In Airbnb, these workflows include multi-department use cases such as data storage, growth analysis, email sending, A/B testing, and more. The platform has the ability to interact with various data systems, including Hive, Presto, MySQL, HDFS, Postgres, and S3, providing good scalability. Additionally, it provides a command-line interface and a web-based user interface that allows users to visualize pipeline dependencies, monitor progress, trigger tasks, and more.

Airflow Architecture

In an extended production environment, Airflow consists of the following components:

  1. One yuan database (MySQL or Postgres): stores metadata information about DAGs and their operations.
  2. A set of nodes Airflow work: execute tasks in the DAG.
  3. Regulator (or Redis RabbitMQ): manages the workflow and schedules tasks.
  4. Airflow Web server: provides a user interface for monitoring and managing workflows.

All of these components can be freely extended to run on a machine. If you use LocalExcuter moderately, you can get quite a bit of extra performance.

Advantages

Airflow offers several advantages, including:

  1. Python script to achieve DAG: very easy to expand.
  2. Visualization dependent workflow: no XML.
  3. Testable: as an alternative to crontab.
  4. Can implement complex dependency rules: pools, CLI, and Web UI.

Features

Airflow provides several features, including:

  1. Common commands:
    • initdb: initializes metadata database.
    • resetdb: empties metadata database.
    • list_dags: lists all DAGs.
    • list_tasks: lists all tasks in a DAG.
    • test: tests the health of a task.
    • backfill: tests the health of a DAG at a set date range.
    • webserver: opens web server service.
    • scheduler: monitors and triggers DAGs.
  2. ETL: extracts, transforms, and loads data from source to destination.
  3. Task dependencies: manages dependencies between tasks, including time, external system, machine, and resource dependencies.

ETL

ETL is a process of extracting, transforming, and loading data from source to destination. Airflow was designed to handle ETL tasks well, but its sophisticated design can be used to solve various dependencies of tasks.

Task Dependencies

Task dependencies are a crucial aspect of Airflow. They include:

  1. Time dependence: tasks wait for triggering at a certain time.
  2. External system dependencies: tasks rely on data in external systems, such as MySQL, HDFS, etc.
  3. Machine dependency: tasks can only be performed in a specific environment.
  4. Dependence between tasks: tasks are started after another task is completed.
  5. Resource dependence: tasks consume resources, such as memory or CPU.

How to Understand Crontab

Crontab is a dependency management system that manages timed tasks. However, it has limitations, as it only manages time dependencies and does not fall within the jurisdiction of complex logic programs.

Airflow Process Dependent Manner

Airflow solves the problem of task dependencies using Directed Acyclic Graphs (DAGs). DAGs consist of one or more tasks, and the dependency relationship between tasks is well represented by DAGs. Airflow fully supports crontab expressions and the direct use of Python’s datetime to express time and time differences.

Conclusion

In this article, we have explored the world of Airflow, a powerful Python-based scheduling and monitoring workflow platform. We have discussed its architecture, advantages, features, and common commands, as well as its ability to handle ETL tasks and manage task dependencies. Airflow provides a versatile tool for workflow developers, making it easy to create, maintain, and periodically schedule workflows. Its sophisticated design can be used to solve various dependencies of tasks, making it a powerful platform for managing complex workflows.