Apache Airflow Architecture
Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows.
Its architecture is built to be dynamic, extensible, and scalable, making it ideal for tasks ranging from simple batch jobs to complex data pipelines involving multiple dependencies
Core Components
Airflow’s architecture is centered around four primary components:
Web Server: A Flask-based web application used for viewing and managing workflows. It provides a user-friendly interface for monitoring workflow executions and their outcomes, editing and managing DAGs (Directed Acyclic Graphs), and managing the Airflow environment.
Scheduler: The heart of Airflow that schedules workflows based on dependencies and schedules defined in DAGs. The scheduler triggers task instances to run at their scheduled times and manages their execution. It continuously polls for tasks ready to be queued and sends them to the executor for execution.
Executor: Responsible for executing the tasks that the scheduler sends to it. Airflow supports several types of executors for scaling task execution, including the SequentialExecutor (for development/testing), LocalExecutor (for single machine environments), CeleryExecutor (for distributed execution using Celery), and KubernetesExecutor (for execution in Kubernetes environments).
Metadata Database: A database that stores state and metadata about all workflows managed by Airflow. It includes information about the structure of workflows (DAGs), their schedules, execution history, and the status of all tasks. The database is essential for Airflow’s operation, allowing it to resume or retry tasks, keep history, and ensure idempotency.
Key Concepts
DAG (Directed Acyclic Graph): A collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG defines the workflow that Airflow will manage and execute.
Operator: Defines a single task in a workflow. Airflow comes with many predefined operators that can be used to perform common tasks, such as running a Python function, executing a bash script, or transferring data between systems.
Task: An instance of an operator. When a DAG runs, Airflow creates tasks that represent instances of operators; these tasks then can be executed.
Task Instance: A specific run of a task, characterized by a point in time (execution date) and a related DAG.
Execution Flow
DAG Definition: Developers define workflows as DAGs, specifying tasks and their dependencies using Python code.
DAG Scheduling: The scheduler reads the DAG definitions and schedules the tasks based on their start times, dependencies, and retries.
Task Execution: The executor picks up tasks scheduled by the scheduler and executes them. The choice of executor depends on the environment and the scalability needs of the tasks.
Monitoring and Management: Throughout execution, users can monitor and manage workflows using the Airflow web server. Upon completion, task statuses are updated in the metadata database, allowing users to review the execution history and logs.
Scalability and Extensibility
Airflow’s architecture supports scaling through its pluggable executor model, which allows it to run tasks on a variety of backend systems.
Its use of a central metadata database enables distributed execution and monitoring. Airflow’s design also emphasizes extensibility, allowing developers to define custom operators, executors, and hooks to integrate with external systems, making it a flexible tool for building complex data pipelines.
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