fix: Fix Spark slice function Null type to GenericListArray casting issue

Author: erenavsarogullari

datafusion/spark/src/function/array/slice.rs

@@ -19,7 +19,9 @@ use arrow::array::{Array, ArrayRef, Int64Builder};
 use arrow::datatypes::{DataType, Field, FieldRef};
 use datafusion_common::cast::{as_int64_array, as_list_array};
 use datafusion_common::utils::ListCoercion;
-use datafusion_common::{Result, exec_err, internal_err, utils::take_function_args};
+use datafusion_common::{
+    DataFusionError, Result, exec_err, internal_err, utils::take_function_args,
+};
 use datafusion_expr::{
     ArrayFunctionArgument, ArrayFunctionSignature, ColumnarValue, ReturnFieldArgs,
     ScalarFunctionArgs, ScalarUDFImpl, Signature, TypeSignature, Volatility,
@@ -94,6 +96,10 @@ impl ScalarUDFImpl for SparkSlice {
         &self,
         mut func_args: ScalarFunctionArgs,
     ) -> Result<ColumnarValue> {
+        if func_args.args[0].data_type() == DataType::Null {
+            return Ok::<ColumnarValue, DataFusionError>(func_args.args[0].clone());
+        };
+
         let array_len = func_args
             .args
             .iter()
@@ -170,3 +176,40 @@ fn calculate_start_end(args: &[ArrayRef]) -> Result<(ArrayRef, ArrayRef)> {
 
     Ok((Arc::new(adjusted_start.finish()), Arc::new(end.finish())))
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use arrow::array::NullArray;
+    use arrow::datatypes::DataType::List;
+    use arrow::datatypes::Field;
+    use datafusion_common::ScalarValue;
+
+    #[test]
+    fn test_spark_slice_function_when_input_array_is_null() {
+        let input_args = vec![
+            ColumnarValue::Array(Arc::new(NullArray::new(1))),
+            ColumnarValue::Scalar(ScalarValue::Int64(Some(1))),
+            ColumnarValue::Scalar(ScalarValue::Int64(Some(3))),
+        ];
+
+        let args = ScalarFunctionArgs {
+            args: input_args.to_owned(),
+            arg_fields: vec![Arc::new(Field::new(
+                "item",
+                List(FieldRef::new(Field::new("", DataType::Int64, true))),
+                false,
+            ))],
+            number_rows: 0,
+            return_field: Arc::new(Field::new(
+                "item",
+                List(FieldRef::new(Field::new_list_field(DataType::Int64, true))),
+                false,
+            )),
+            config_options: Arc::new(Default::default()),
+        };
+        let slice = SparkSlice::new();
+        let result = slice.invoke_with_args(args).unwrap();
+        assert!(result.to_array(1).unwrap() == Arc::new(NullArray::new(1)));
+    }
+}

datafusion/sqllogictest/test_files/spark/array/slice.slt

@@ -114,3 +114,21 @@ query ?
 SELECT slice([1, 2, 3, 4], CAST('2' AS INT), 4);
 ----
 [2, 3, 4]
+
+query ?
+SELECT slice(column1, column2, column3)
+FROM VALUES
+(NULL, 1, 2),
+(NULL, 1, -2),
+(NULL, -1, 2),
+(NULL, 0, 2);
+----
+NULL
+NULL
+NULL
+NULL
+
+query ?
+SELECT slice(slice(NULL, 1, 2), 1, 2)
+----
+NULL