20006: feat(spark): Adds negative spark function

datafusion/spark/src/function/math/mod.rs

@@ -20,6 +20,7 @@ pub mod expm1;
 pub mod factorial;
 pub mod hex;
 pub mod modulus;
+pub mod negative;
 pub mod rint;
 pub mod trigonometry;
 pub mod unhex;
@@ -40,6 +41,7 @@ make_udf_function!(unhex::SparkUnhex, unhex);
 make_udf_function!(width_bucket::SparkWidthBucket, width_bucket);
 make_udf_function!(trigonometry::SparkCsc, csc);
 make_udf_function!(trigonometry::SparkSec, sec);
+make_udf_function!(negative::SparkNegative, negative);
 
 pub mod expr_fn {
     use datafusion_functions::export_functions;
@@ -63,6 +65,11 @@ pub mod expr_fn {
     export_functions!((width_bucket, "Returns the bucket number into which the value of this expression would fall after being evaluated.", arg1 arg2 arg3 arg4));
     export_functions!((csc, "Returns the cosecant of expr.", arg1));
     export_functions!((sec, "Returns the secant of expr.", arg1));
+    export_functions!((
+        negative,
+        "Returns the negation of expr (unary minus).",
+        arg1
+    ));
 }
 
 pub fn functions() -> Vec<Arc<ScalarUDF>> {
@@ -78,5 +85,6 @@ pub fn functions() -> Vec<Arc<ScalarUDF>> {
         width_bucket(),
         csc(),
         sec(),
+        negative(),
     ]
 }

datafusion/spark/src/function/math/negative.rs

@@ -0,0 +1,280 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use arrow::array::types::*;
+use arrow::array::*;
+use arrow::datatypes::{DataType, IntervalDayTime, IntervalMonthDayNano};
+use bigdecimal::num_traits::WrappingNeg;
+use datafusion_common::utils::take_function_args;
+use datafusion_common::{Result, ScalarValue, not_impl_err};
+use datafusion_expr::{
+    ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
+};
+use std::any::Any;
+use std::sync::Arc;
+
+/// Spark-compatible `negative` expression
+/// <https://spark.apache.org/docs/latest/api/sql/index.html#negative>
+///
+/// Returns the negation of input (equivalent to unary minus)
+/// Returns NULL if input is NULL, returns NaN if input is NaN.
+///
+/// ANSI mode support see (<https://github.com/apache/datafusion/issues/20034>):
+///  - Spark's ANSI-compliant dialect, when off (i.e. `spark.sql.ansi.enabled=false`),
+///    negating the minimal value of a signed integer wraps around.
+///    For example: negative(i32::MIN) returns i32::MIN (wraps instead of error).
+///    This is the current implementation (legacy mode only).
+///  - Spark's ANSI mode (when `spark.sql.ansi.enabled=true`) should throw an
+///    ARITHMETIC_OVERFLOW error on integer overflow instead of wrapping.
+///    This is not yet implemented - all operations currently use wrapping behavior.
+///
+#[derive(Debug, PartialEq, Eq, Hash)]
+pub struct SparkNegative {
+    signature: Signature,
+}
+
+impl Default for SparkNegative {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl SparkNegative {
+    pub fn new() -> Self {
+        Self {
+            signature: Signature::numeric(1, Volatility::Immutable),
+        }
+    }
+}
+
+impl ScalarUDFImpl for SparkNegative {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn name(&self) -> &str {
+        "negative"
+    }
+
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+
+    fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
+        Ok(arg_types[0].clone())
+    }
+
+    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
+        spark_negative(&args.args)
+    }
+}
+
+/// Core implementation of Spark's negative function
+fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue> {
+    let [_] = take_function_args("negative", args)?;
+
+    match &args[0] {
+        ColumnarValue::Array(array) => match array.data_type() {
+            DataType::Null
+            | DataType::UInt8
+            | DataType::UInt16
+            | DataType::UInt32
+            | DataType::UInt64 => Ok(args[0].clone()),
+
+            // Signed integers - use wrapping negation (Spark legacy mode behavior)
+            DataType::Int8 => {
+                let array = array.as_primitive::<Int8Type>();
+                let result: PrimitiveArray<Int8Type> = array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Int16 => {
+                let array = array.as_primitive::<Int16Type>();
+                let result: PrimitiveArray<Int16Type> = array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Int32 => {
+                let array = array.as_primitive::<Int32Type>();
+                let result: PrimitiveArray<Int32Type> = array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Int64 => {
+                let array = array.as_primitive::<Int64Type>();
+                let result: PrimitiveArray<Int64Type> = array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+
+            // Floating point - simple negation (no overflow possible)
+            DataType::Float16 => {
+                let array = array.as_primitive::<Float16Type>();
+                let result: PrimitiveArray<Float16Type> = array.unary(|x| -x);
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Float32 => {
+                let array = array.as_primitive::<Float32Type>();
+                let result: PrimitiveArray<Float32Type> = array.unary(|x| -x);
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Float64 => {
+                let array = array.as_primitive::<Float64Type>();
+                let result: PrimitiveArray<Float64Type> = array.unary(|x| -x);
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+
+            // Decimal types - wrapping negation
+            DataType::Decimal32(_, _) => {
+                let array = array.as_primitive::<Decimal32Type>();
+                let result: PrimitiveArray<Decimal32Type> =
+                    array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Decimal64(_, _) => {
+                let array = array.as_primitive::<Decimal64Type>();
+                let result: PrimitiveArray<Decimal64Type> =
+                    array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Decimal128(_, _) => {
+                let array = array.as_primitive::<Decimal128Type>();
+                let result: PrimitiveArray<Decimal128Type> =
+                    array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Decimal256(_, _) => {
+                let array = array.as_primitive::<Decimal256Type>();
+                let result: PrimitiveArray<Decimal256Type> =
+                    array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+
+            // interval type
+            DataType::Interval(arrow::datatypes::IntervalUnit::YearMonth) => {
+                let array = array.as_primitive::<IntervalYearMonthType>();
+                let result: PrimitiveArray<IntervalYearMonthType> =
+                    array.unary(|x| x.wrapping_neg());
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Interval(arrow::datatypes::IntervalUnit::DayTime) => {
+                let array = array.as_primitive::<IntervalDayTimeType>();
+                let result: PrimitiveArray<IntervalDayTimeType> =
+                    array.unary(|x| IntervalDayTime {
+                        days: x.days.wrapping_neg(),
+                        milliseconds: x.milliseconds.wrapping_neg(),
+                    });
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            DataType::Interval(arrow::datatypes::IntervalUnit::MonthDayNano) => {
+                let array = array.as_primitive::<IntervalMonthDayNanoType>();
+                let result: PrimitiveArray<IntervalMonthDayNanoType> =
+                    array.unary(|x| IntervalMonthDayNano {
+                        months: x.months.wrapping_neg(),
+                        days: x.days.wrapping_neg(),
+                        nanoseconds: x.nanoseconds.wrapping_neg(),
+                    });
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+
+            dt => not_impl_err!("Not supported datatype for Spark NEGATIVE: {dt}"),
+        },
+        ColumnarValue::Scalar(sv) => match sv {
+            ScalarValue::Null => Ok(args[0].clone()),
+            sv if sv.is_null() => Ok(args[0].clone()),
+
+            // Signed integers - wrapping negation
+            ScalarValue::Int8(Some(v)) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(result))))
+            }
+            ScalarValue::Int16(Some(v)) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Int16(Some(result))))
+            }
+            ScalarValue::Int32(Some(v)) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Int32(Some(result))))
+            }
+            ScalarValue::Int64(Some(v)) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(result))))
+            }
+
+            // Floating point - simple negation
+            ScalarValue::Float16(Some(v)) => {
+                Ok(ColumnarValue::Scalar(ScalarValue::Float16(Some(-v))))
+            }
+            ScalarValue::Float32(Some(v)) => {
+                Ok(ColumnarValue::Scalar(ScalarValue::Float32(Some(-v))))
+            }
+            ScalarValue::Float64(Some(v)) => {
+                Ok(ColumnarValue::Scalar(ScalarValue::Float64(Some(-v))))
+            }
+
+            // Decimal types - wrapping negation
+            ScalarValue::Decimal32(Some(v), precision, scale) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Decimal32(
+                    Some(result),
+                    *precision,
+                    *scale,
+                )))
+            }
+            ScalarValue::Decimal64(Some(v), precision, scale) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Decimal64(
+                    Some(result),
+                    *precision,
+                    *scale,
+                )))
+            }
+            ScalarValue::Decimal128(Some(v), precision, scale) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Decimal128(
+                    Some(result),
+                    *precision,
+                    *scale,
+                )))
+            }
+            ScalarValue::Decimal256(Some(v), precision, scale) => {
+                let result = v.wrapping_neg();
+                Ok(ColumnarValue::Scalar(ScalarValue::Decimal256(
+                    Some(result),
+                    *precision,
+                    *scale,
+                )))
+            }
+
+            //interval type
+            ScalarValue::IntervalYearMonth(Some(v)) => Ok(ColumnarValue::Scalar(
+                ScalarValue::IntervalYearMonth(Some(v.wrapping_neg())),
+            )),
+            ScalarValue::IntervalDayTime(Some(v)) => Ok(ColumnarValue::Scalar(
+                ScalarValue::IntervalDayTime(Some(IntervalDayTime {
+                    days: v.days.wrapping_neg(),
+                    milliseconds: v.milliseconds.wrapping_neg(),
+                })),
+            )),
+            ScalarValue::IntervalMonthDayNano(Some(v)) => Ok(ColumnarValue::Scalar(
+                ScalarValue::IntervalMonthDayNano(Some(IntervalMonthDayNano {
+                    months: v.months.wrapping_neg(),
+                    days: v.days.wrapping_neg(),
+                    nanoseconds: v.nanoseconds.wrapping_neg(),
+                })),
+            )),
+
+            dt => not_impl_err!("Not supported datatype for Spark NEGATIVE: {dt}"),
+        },
+    }
+}