Airflow Architecture

✍️ Apache Airflow is a platform designed to manage and orchestrate complex workflows.

🛡️ Scheduler: The scheduler is responsible for reading DAGs, scheduling tasks, monitoring task execution, and triggering downstream tasks once their dependencies are met.

🛡️ Executor: The executor is responsible for running tasks in the DAGs. It is a pluggable component that can be customized to suit specific needs.

🛡️ Webserver: The webserver provides a user-friendly interface to inspect, trigger, and debug DAGs and tasks. It allows users to monitor the status of workflows, view logs, and configure workflow settings.

🛡️ DAG Files: DAG files are the configuration files that define the workflows. They contain the definitions of tasks, dependencies, and data flows. DAGs can be written in Python or other languages supported by Airflow.

🛡️ Metadata Database: The metadata database is the backend database that stores information about workflows, tasks, task instances, and their execution status. It serves as the central repository for managing and monitoring workflows. The default database is SQLite, but other databases like PostgreSQL and MySQL can be used.

📂 Key Components and Interactions

🔸Scheduler: Reads DAGs, schedules tasks, monitors task execution, and triggers downstream tasks. 🔹Executor: Runs tasks in the DAGs, with options for sequential or parallel execution. 🔸Webserver: Provides a user interface for monitoring and managing workflows. 🔹DAG Files: Define the workflows, including tasks, dependencies, and data flows. 🔸Metadata Database: Stores information about workflows, tasks, and task instances.

📂 Best Practices and Customization

🔸Custom Executors: Can be created to integrate with specific compute services or tools. 🔹Task Computing Flexibility: Enables flexible task execution with fewer dependencies. 🔸Observability: Provides better task observability and monitoring capabilities. 🔹High Availability: Ensures that Airflow can continue running data pipelines even in the event of a node failure. 🔸Scalability: Supports horizontal scalability to handle large volumes of data and tasks.

📂 Community and Open-Source Functionality

🔹Large Community: Engaged maintainers, committers, and contributors provide extensive resources and support. 🔸Documentation: Comprehensive documentation and tutorials are available for novice and experienced users. 🔹Community Forums: Active discussions and a dev mailing list facilitate troubleshooting and customization.

📂 Cloud-Native and Digital Transformation

🔸Cloud-Native: Designed to automate data flows between cloud and on-premises environments. 🔹Digital Transformation: Plays a key role in digital transformation by providing a programmatic foundation for managing and automating data-driven processes.

📂 Key Considerations

🔸Familiarity with BaseExecutor Methods: Understanding the executor lifecycle within Airflow's architecture is crucial. 🔹Compatibility with Scheduler and Worker Paradigms: Ensuring compatibility with Airflow's scheduler and worker paradigms is essential. 🔸Testing and Debugging: Testing and debugging custom executors and workflows are critical for ensuring reliable execution.

📂 Implementation Steps

🔹Subclass BaseExecutor: Implement required methods such as start, execute, and end. 🔸Handle Task Queuing and Execution Logic: Implement task queuing and execution logic within the custom executor. 🔹Test the Custom Executor: Test the custom executor in a controlled environment to ensure reliability and performance.

📂 Best Practices

🔸Avoid Expensive Operations: Avoid importing or executing expensive operations at the module level. 🔹Implement get_task_logs: Implement enhanced logging capabilities. 🔸Follow Configuration Guidelines: Follow Airflow's configuration guidelines for executors and DAGs.

📂 Additional Resources

🔹Airflow Documentation: Comprehensive documentation and tutorials are available for novice and experienced users. 🔸Airflow Community: Engaged maintainers, committers, and contributors provide extensive resources and support. 🔹Airflow Tutorials: Use-case-specific tutorials and guides are available for implementing and customizing Airflow.