jsai28 commented on issue #15276: URL: https://github.com/apache/datafusion/issues/15276#issuecomment-2832788966
@eliaperantoni No worries! I had some follow-ups. > I think it would be the diagnose function itself that creates an error, because otherwise there's none Isn't there already a DataFusion error if incorrect arguments are supplied to a UDF? [This](https://github.com/apache/datafusion/blob/19a1e58071cd60e74faabab805d97fd269418925/datafusion/expr/src/expr_schema.rs#L428) one. My understanding is that the `data_types_with_scalar_udf` function is called during the logical planning of a Scalar UDF and checks whether the right arguments were supplied (or whether they can be coerced). My thinking was that the diagnose trait function would be called when there is an error and supply more detailed diagnostic information. Or is the `diagnose` trait function for errors that go beyond just incorrect function arguments and are more specific to the UDF? Maybe like a UDF that for example doesn't accept numbers larger than 50, then the `diagnose` function is where the implementor can produce that error and highlight the exact argument? If this is the case, then yes I agree with your other point that we should default to a standard error rather than have the implementor create their own `DataFusionError`. They should just return a `Vec<Diagnostic` which we can consume to make a `DataFusionError` for them, and attach the relevant diagnostics to it. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org For additional commands, e-mail: github-h...@datafusion.apache.org
