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fix(concat): correct nullability inference (nullable only if all arguments nullable) (#19189)
Fixes #19171 ### Problem The `concat` scalar function currently reports its result as **always nullable** at planning/schema time. However, its **runtime semantics** are: - `concat` ignores NULL inputs. - The result becomes NULL **only if all input arguments are NULL**. This mismatch causes incorrect nullability in inferred schemas and can affect optimizer behavior. ### What this PR does Implements `return_field_from_args` for `ConcatFunc` so that: 1. The return `DataType` is derived using the existing `return_type` logic. 2. The return field’s nullability is computed as: (If there are no argument fields, the return is considered nullable defensively.) This aligns schema-time nullability with runtime behavior and matches the semantics used by other SQL engines. ### Tests - All existing unit tests for `concat` pass. - Parquet & CSV tests verified locally after initializing test submodules. - No behavior changes to runtime concatenation: only planner-side metadata improved. ### Notes If CI finds any compatibility adjustments required across DataFusion crates, I will update this PR accordingly.
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datafusion/spark/src/function/string/concat.rs

Lines changed: 63 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -17,8 +17,10 @@
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use arrow::array::Array;
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use arrow::buffer::NullBuffer;
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use arrow::datatypes::DataType;
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use arrow::datatypes::{DataType, Field};
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use datafusion_common::arrow::datatypes::FieldRef;
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use datafusion_common::{Result, ScalarValue};
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use datafusion_expr::ReturnFieldArgs;
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use datafusion_expr::{
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ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, TypeSignature,
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Volatility,
@@ -71,10 +73,6 @@ impl ScalarUDFImpl for SparkConcat {
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&self.signature
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}
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fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
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Ok(DataType::Utf8)
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}
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fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
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spark_concat(args)
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}
@@ -83,6 +81,17 @@ impl ScalarUDFImpl for SparkConcat {
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// Accept any string types, including zero arguments
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Ok(arg_types.to_vec())
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}
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fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
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datafusion_common::internal_err!(
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"return_type should not be called for Spark concat"
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)
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}
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fn return_field_from_args(&self, args: ReturnFieldArgs<'_>) -> Result<FieldRef> {
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// Spark semantics: concat returns NULL if ANY input is NULL
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let nullable = args.arg_fields.iter().any(|f| f.is_nullable());
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Ok(Arc::new(Field::new("concat", DataType::Utf8, nullable)))
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}
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}
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/// Represents the null state for Spark concat
@@ -231,8 +240,10 @@ mod tests {
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use super::*;
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use crate::function::utils::test::test_scalar_function;
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use arrow::array::StringArray;
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use arrow::datatypes::DataType;
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use arrow::datatypes::{DataType, Field};
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use datafusion_common::Result;
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use datafusion_expr::ReturnFieldArgs;
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use std::sync::Arc;
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#[test]
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fn test_concat_basic() -> Result<()> {
@@ -266,4 +277,50 @@ mod tests {
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);
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Ok(())
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}
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#[test]
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fn test_spark_concat_return_field_non_nullable() -> Result<()> {
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let func = SparkConcat::new();
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let fields = vec![
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Arc::new(Field::new("a", DataType::Utf8, false)),
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Arc::new(Field::new("b", DataType::Utf8, false)),
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];
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let args = ReturnFieldArgs {
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arg_fields: &fields,
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scalar_arguments: &[],
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};
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let field = func.return_field_from_args(args)?;
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assert!(
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!field.is_nullable(),
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"Expected concat result to be non-nullable when all inputs are non-nullable"
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);
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Ok(())
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}
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#[test]
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fn test_spark_concat_return_field_nullable() -> Result<()> {
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let func = SparkConcat::new();
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let fields = vec![
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Arc::new(Field::new("a", DataType::Utf8, false)),
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Arc::new(Field::new("b", DataType::Utf8, true)),
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];
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let args = ReturnFieldArgs {
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arg_fields: &fields,
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scalar_arguments: &[],
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};
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let field = func.return_field_from_args(args)?;
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assert!(
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field.is_nullable(),
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"Expected concat result to be nullable when any input is nullable"
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);
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Ok(())
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}
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}

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