gengliangwang commented on PR #46947:
URL: https://github.com/apache/spark/pull/46947#issuecomment-2163943962

   @pan3793 I appreciate the feedback and concerns raised regarding the 
migration of variables in our new structured logging framework for Apache 
Spark. I'd like to address these concerns and provide some clarity on our 
approach.
   
   ## Why Migrate All Variables?
   The primary reason for migrating all variables in our log messages is to 
avoid ongoing debates about which specific keys should be included as log keys. 
By disallowing the inlining of variables in log messages and ensuring all 
variables are explicitly named and migrated, we achieve a consistent and 
comprehensive logging structure. This approach ensures uniformity across all 
log entries.
   
   ## Current Status of Migration
   The migration process is nearly complete, which means we've already invested 
significant effort into ensuring that our log entries adhere to the new 
framework. Moving forward, as long as we follow the new logging rules, we 
maintain control over all the variables in our logs. This control is crucial 
for effective log analysis and troubleshooting.
   
   ## Managing Variable Overload
   To address concerns about the potential overload of variables for users, we 
have a couple of solutions:
   
   * Configuration Allow List: We can build a configuration option to pass an 
allow list of log keys, enabling users to specify which variables should appear 
in their logs.
   * Partitioned Log Keys: We can partition the log keys so that only essential 
ones appear by default, while others show up in verbose logging mode. This way, 
users can choose the level of detail they want to see based on their needs.
   
   Our goal with these changes is to enhance the clarity, consistency, and 
utility of our logs. 


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