[GitHub] [airflow] KevinYang21 opened a new pull request #5908: [WIP]Revert "[AIRFLOW-4797] Improve performance and behaviour of zombie de…

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KevinYang21 opened a new pull request #5908: [WIP]Revert "[AIRFLOW-4797] Improve performance and behaviour of zombie de…
URL: https://github.com/apache/airflow/pull/5908
 
 
   ### 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
   
   - [x] Here are some details about my PR, including screenshots of any UI changes:
   Original reason stated in the PR why zombie detection was moved 
   ```Zombie tasks will be calculate by DAG parsing manager and send to DAG parsing processor to kill. This is to reduce DB CPU load( identified to produce 80% of CPU load during stress test, CPU usage went down from 80%+ to ~40% after this change).``` 
   from https://github.com/apache/airflow/pull/3873.
   
   I see no point sending a query joining two biggest tables in every DAG parsing. Establishing new connections is much more expensive than sending an aggregated query. It doesn't seem to deliver any immediate value: the DB load in a smaller cluster was not changing. And if we want to compare the running time diff we compare the aggregated query running time on all DAG file processors vs. the old query running time instead of compare the individual query. We parse a couple thoudsand files in 2 mins and it will generate heavy load to the DB, which I believe is the biggest bottelneck of Airflow scalibility.
   
   ### Tests
   
   - [x] My PR adds the following unit tests __OR__ does not need testing for this extremely good reason:
   Reverting PR
   
   ### 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
   
   ### Code Quality
   
   - [x] Passes `flake8`
   

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