nuclearpinguin opened a new pull request #6750: [AIRFLOW-YYYY] Experimental Airflow native executor URL: https://github.com/apache/airflow/pull/6750 Make sure you have checked _all_ steps below. ### Jira - [ ] My PR addresses the following [Airflow Jira](https://issues.apache.org/jira/browse/AIRFLOW/) issues and references them in the PR title. For example, "\[AIRFLOW-XXX\] My Airflow PR" - https://issues.apache.org/jira/browse/AIRFLOW-XXX - In case you are fixing a typo in the documentation you can prepend your commit with \[AIRFLOW-XXX\], code changes always need a Jira issue. - In case you are proposing a fundamental code change, you need to create an Airflow Improvement Proposal ([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvements+Proposals)). - In case you are adding a dependency, check if the license complies with the [ASF 3rd Party License Policy](https://www.apache.org/legal/resolved.html#category-x). ### Description - [ ] Here are some details about my PR, including screenshots of any UI changes: This is an experimental work. The PR proposes an Airflow-native executor bases on AMQ protocol. As an interface to the queue I used Kombu which is also a base for Celery. The main idea is to have an executor that publishes tasks to a queue and a consumer (Worker) that reads and execute them. The information about tasks state is propagated back to scheduler through our Airflow database. Each `Worker` consist of few `TaskWorkers` and their number is defined by worker concurrency. The main purpose of the `Worker` is to keep `TaskWorkers` alive and handle graceful exit. Each `TaskWorker` runs a single `TaskConsumer` that is a Kombu consumer that executes tasks by calling `airflow task run`.  The reason behind this proposition is to create a scalable and cloud native way to run Airflow. Of course CeleryExecutor seems to be doing this already but... we are using only a few percents of what Celery can do. Moreover, I believe that our custom implementation can bring more control. ### Tests - [ ] My PR adds the following unit tests __OR__ does not need testing for this extremely good reason: ### Commits - [ ] My commits all reference Jira issues in their subject lines, and I have squashed multiple commits if they address the same issue. In addition, my commits follow the guidelines from "[How to write a good git commit message](http://chris.beams.io/posts/git-commit/)": 1. Subject is separated from body by a blank line 1. Subject is limited to 50 characters (not including Jira issue reference) 1. Subject does not end with a period 1. Subject uses the imperative mood ("add", not "adding") 1. Body wraps at 72 characters 1. Body explains "what" and "why", not "how" ### Documentation - [ ] In case of new functionality, my PR adds documentation that describes how to use it. - All the public functions and the classes in the PR contain docstrings that explain what it does - If you implement backwards incompatible changes, please leave a note in the [Updating.md](https://github.com/apache/airflow/blob/master/UPDATING.md) so we can assign it to a appropriate release
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