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Discussion: make it easier for specify SQL --> function translation #10534

@alamb

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@alamb

I extracted this from a conversation between @jayzhan211 and myself

Basically the usecase is that the sql planner converts things like [1, 2, 3] to make_array(1, 2, 3) and the name make_array is hard coded

          > @alamb Do you think we should also rewrite the array operator to function in parser step? It is currently in optimizer step. I think the downside of moving array rewrite in parser step is that if any user expects different array function with the same syntax, then they can't do it since we don't have "user-defined" parser mechanism now. But the benefit is that we can eliminate intermediate binary expression.

I agree that changing the parser to insert a call to get field access directly is a good idea (and would be consistent and allow us to remove Expr::GetFieldAccess

Have you thought about "user-defined" parser idea before, the way that user can define their own expression to get from the syntax? Is it useful in production? 🤔

One thing I have thought about is changing the hard coded lookup of function names from a pattern like this

if let Some(udf) = self.context_provider.get_function_meta("make_array") {
Ok(Expr::ScalarFunction(ScalarFunction::new_udf(udf, values)))
} else {
not_impl_err!(
"array_expression featrue is disable, So should implement make_array UDF by yourself"
)
}

To be something more structured

pub trait PlannerFunctions {
  /// return the UDF to use for creating arrays ("make_array") by default:
  fn make_array(&self) -> Result<ScalarUDF>;
...
  // similar functions for other built in functions
}

And then instead of

 if let Some(udf) = self.context_provider.get_function_meta("make_array") { 
     Ok(Expr::ScalarFunction(ScalarFunction::new_udf(udf, values))) 
 } else { 
     not_impl_err!( 
         "array_expression featrue is disable, So should implement make_array UDF by yourself" 
     ) 
 } 

The planner might look like

  let udf = self.planner_functions.make_array()?;
  Ok(Expr::ScalarFunction(ScalarFunction::new_udf(udf, values))) 

But I haven't had a usecase to do that myself yet

Originally posted by @alamb in #10374 (comment)

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