Python  

Apache Airflow: Architecture and Working

Introduction

Hi Everyone,

In this article, we will be learning about Apache Airflow.

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. Initially developed by Airbnb in 2014, it has become the standard for orchestrating complex data pipelines and batch processing jobs in modern data engineering environments.

What is Apache Airflow?

Apache Airflow is a workflow orchestration tool that allows you to define workflows as code using Python. It treats workflows as Directed Acyclic Graphs (DAGs) where each node represents a task, and edges define dependencies between tasks. This approach provides flexibility, version control, and dynamic pipeline generation capabilities.

Features

  • Workflow as Code: Define pipelines using Python scripts.
  • Rich UI: Web-based interface for monitoring and managing workflows.
  • Extensibility: Large ecosystem of operators and hooks.
  • Scalability: Distributed execution across multiple workers.
  • Scheduling: Robust scheduling with cron-like expressions.

Uses of Apache Airflow

  1. Data Engineering Pipelines: ETL/ELT processes, data migration, and batch processing jobs that require complex scheduling and dependency management.
  2. Machine Learning Workflows: Orchestrating model training, feature engineering, and deployment pipelines with proper sequencing and error handling.
  3. System Administration: Automating backup processes, log cleanup, and system maintenance tasks that need reliable scheduling.
  4. Multi-system Integration: Coordinating tasks across different systems, databases, and cloud services with proper error handling and retry mechanisms.
  5. Avoid Airflow: Real-time streaming data, simple cron jobs, or workflows that require sub-second latency.

Architecture

Core Components

  1. Web Server: Provides the user interface for monitoring DAGs, viewing logs, and managing workflow execution. Built on the Flask framework.
  2. Scheduler: The heart of Airflow that reads DAG definitions, schedules task instances, and manages workflow execution based on dependencies and schedules.
  3. Executor: Determines how and where tasks are executed. Options include Sequential, Local, Celery, Kubernetes, and CeleryKubernetes executors.
  4. Metadata Database: Stores all metadata, including DAG definitions, task instances, variables, connections, and execution history. Supports PostgreSQL, MySQL, and SQLite.
  5. Workers: Execute the actual tasks. In distributed setups, workers run on separate machines and communicate through message brokers, such as Redis or RabbitMQ.

Workflow Components

  1. DAGs (Directed Acyclic Graphs): Python files defining the workflow structure, task dependencies, and scheduling parameters.
  2. Tasks: Individual units of work within a DAG, implemented using operators like BashOperator, PythonOperator, or custom operators.
  3. Operators: Define what actually gets executed. Airflow provides operators for various systems, including databases, cloud services, and file systems.
  4. Hooks: Interfaces to external systems, providing reusable connection logic for databases, APIs, and cloud services.

Execution Flow

  1. DAG Parsing: Scheduler parses Python files in the DAGs folder to identify workflow definitions
  2. Task Scheduling: Based on schedule intervals and dependencies, the scheduler queues eligible tasks.
  3. Task Execution: Executor picks up queued tasks and assigns them to available workers
  4. Status Tracking: Task status updates are stored in the metadata database
  5. Dependency Resolution: Scheduler evaluates dependencies and schedules downstream tasks upon completion

Summary

Apache Airflow offers a robust and scalable solution for workflow orchestration, thanks to its code-first approach and comprehensive architecture. Its modular design allows organizations to start simple and scale according to their needs, while the rich ecosystem of operators and integrations makes it suitable for a diverse range of use cases. The platform excels in environments requiring complex scheduling, dependency management, and monitoring capabilities. However, it's essential to evaluate whether Airflow's complexity aligns with your specific requirements, as simpler alternatives might suffice for basic automation needs.

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