amoghrajesh commented on issue #44352:
URL: https://github.com/apache/airflow/issues/44352#issuecomment-3219718374

   Alright, nice discussions above. I would be in favour of _**Option 1: 
Complete Independence**_ as I think it is the most practical and 
architecturally sound approach with Airflow3 architecture. Let me explain the 
fundamental problems that eliminate Options 2, 3, and 4 from consideration:
   
   * Option 2: API-Based Inheritance
   It creates network dependencies between the workers and the API server and 
now makes the workers running task sdk dependent on API server even for the 
most basic dag authoring experience, I do not know how we would be able to deal 
with operator and dag defaults here, not to mention it might turn into really 
huge payloads across the networks making a mark on performance of Airflow by 
having too many API calls and also by making "context" as a dumping groung.
   
   
   * Option 3: Bootstrap Injection
   Could potentially lead into slower bootstrapping for configs that need to be 
calculated and it still requires API calls and doesn't solve the DAG 
processor/triggerer problem. Another issue I see is static nature of injected 
configuration means no dynamic updates are possible, leading to inconsistency 
when configuration changes on the server side while tasks are running (could be 
an issue for things like execution url changing)
   
   * Option 4: Hybrid Local-First + API Fallback
   This is a decent option and my proposal would be more like a hybrid of 
option 4 and 1, but 4 in itself could be overly complex for very little benefit?
   
   
   I propose that we move the `AirflowConfigParser` (not configs) to a shared 
module, while keeping the actual configuration files and values separate and 
independent for each component.
   
   Each component now gets exactly what it needs - the shared parsing logic / 
abstraction to read and interpret configuration files, but with complete 
autonomy over what configuration values they actually contain.


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