krzysztof-kubis opened a new issue, #43347:
URL: https://github.com/apache/airflow/issues/43347

   ### Apache Airflow Provider(s)
   
   dbt-cloud
   
   ### Versions of Apache Airflow Providers
   
   Astronomer Runtime 12.1.0 based on Airflow 2.10.1+astro.1
   Git Version: .release:7a1ffe6438b5ea8fcf75c4e5a356a6c23ab18404
   
   ### Apache Airflow version
   
   Airflow 2.10.1+astro.1
   
   ### Operating System
   
   Debian GNU/Linux 12 (bookworm)
   
   ### Deployment
   
   Astronomer
   
   ### Deployment details
   
   pure: `https://github.com/dbt-labs/airflow-dbt-cloud`
   
   ### What happened
   
   With the `retry_from_failure=True` flag set, each run only executes the 
models that failed in the previous run, which is fine. However, if there is an 
error in one model that can't be resolved (e.g., due to a data source issue), 
the flag prevents the other models from being refreshed, even in subsequent 
**scheduled** runs.
   
   ### What you think should happen instead
   
   I think `retry_from_failure` should only apply to reruns. Two improvements 
might be thought of.
   
   1. add a condition that triggers `{account_id}/jobs/{job_id}/rerun/` only 
during task reruns
   e.g. replacing line 463 in `providers/dbt/cloud/hooks/dbt.py` 
   from `if retry_from_failure:` to something like `if retry_from_failure and 
context['task_instance'].try_number!=1:`
   
   2. a more general solution replacing the flag with a parameter with several 
values ​​e.g.
   `retry_from_failure = ["Never", "Rerun", "Always"]`
   
   ### How to reproduce
   
   You don't need to do anything specific to reproduce the issue. The flag 
works on every run, but it should likely only affect reruns.
   
   ### Anything else
   
   _No response_
   
   ### Are you willing to submit PR?
   
   - [X] Yes I am willing to submit a PR!
   
   ### Code of Conduct
   
   - [X] I agree to follow this project's [Code of 
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
   


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