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Airflow Executors | Astronomer Documentation

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If you're new to Apache Airflow, the world of Executors is difficult to navigate. Even if you're a veteran user overseeing 20 or more DAGs, knowing what Executor best suits your use case at any given time isn't always easy - especially as the OSS project and its utilities continue to grow and develop. This guide will help you: To get the most out of this guide, you should have an understanding of: After a DAG is defined, the following needs to happen in order for the tasks within that DAG to execute and be completed: The Metadata Database keeps a record of all tasks within a DAG and their corresponding status (queued, scheduled, running, success, failed, and so on) behind the scenes. The scheduler reads from the metadata database to check on the status of each task and decide what needs to get done and when. The executor works closely with the scheduler to determine what resources will actually complete those tasks (using a worker process or otherwise) as they're queued. The difference

> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://www.astronomer.io/docs/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://www.astronomer.io/docs/_mcp/server. # Apache Airflow® Executors > An introduction to Apache Airflow® Executors Executors are a configuration property of the [Airflow scheduler component](airflow-components). The executor you choose for a task determines _where_ and _how_ a task is run. You can choose from several pre-configured executors that are designed for

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