Jefffrey commented on code in PR #20006: URL: https://github.com/apache/datafusion/pull/20006#discussion_r2730358889
########## datafusion/spark/src/function/math/negative.rs: ########## @@ -0,0 +1,410 @@ +// 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::*; +use arrow::datatypes::DataType; +use datafusion_common::{DataFusionError, Result, ScalarValue, internal_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. +/// +/// TODOs: +/// - 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) + } +} + +/// Helper macro to generate wrapping negation for array types +macro_rules! wrapping_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| x.wrapping_neg()); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate simple negation for floating point array types +macro_rules! simple_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| -x); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate wrapping negation for scalar types +macro_rules! wrapping_negative_scalar { + ($INPUT:ident, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE(Some( + result, + )))) + }}; +} + +/// Helper macro to generate wrapping negation for decimal scalar types +macro_rules! wrapping_negative_decimal_scalar { + ($INPUT:ident, $PRECISION:expr, $SCALE:expr, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE( + Some(result), + $PRECISION, + $SCALE, + ))) + }}; +} + +/// Core implementation of Spark's negative function +pub fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() != 1 { + return internal_err!( + "negative takes exactly 1 argument, but got: {}", + args.len() + ); + } Review Comment: ```suggestion fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue> { let [arg] = take_function_args("negative", args)?; ``` - Function doesn't need to be pub - Can use `take_function_args` to destructure which handles errors for us ########## datafusion/spark/src/function/math/negative.rs: ########## @@ -0,0 +1,410 @@ +// 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::*; +use arrow::datatypes::DataType; +use datafusion_common::{DataFusionError, Result, ScalarValue, internal_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. +/// +/// TODOs: +/// - 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) + } +} + +/// Helper macro to generate wrapping negation for array types +macro_rules! wrapping_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| x.wrapping_neg()); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate simple negation for floating point array types +macro_rules! simple_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| -x); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate wrapping negation for scalar types +macro_rules! wrapping_negative_scalar { + ($INPUT:ident, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE(Some( + result, + )))) + }}; +} + +/// Helper macro to generate wrapping negation for decimal scalar types +macro_rules! wrapping_negative_decimal_scalar { + ($INPUT:ident, $PRECISION:expr, $SCALE:expr, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE( + Some(result), + $PRECISION, + $SCALE, + ))) + }}; +} + +/// Core implementation of Spark's negative function +pub fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() != 1 { + return internal_err!( + "negative takes exactly 1 argument, but got: {}", + args.len() + ); + } + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null => Ok(args[0].clone()), + + // Signed integers - use wrapping negation (Spark legacy mode behavior) + DataType::Int8 => wrapping_negative_array!(array, Int8Array), + DataType::Int16 => wrapping_negative_array!(array, Int16Array), + DataType::Int32 => wrapping_negative_array!(array, Int32Array), + DataType::Int64 => wrapping_negative_array!(array, Int64Array), + + // Floating point - simple negation (no overflow possible) + DataType::Float16 => simple_negative_array!(array, Float16Array), + DataType::Float32 => simple_negative_array!(array, Float32Array), + DataType::Float64 => simple_negative_array!(array, Float64Array), + + // Decimal types - wrapping negation + DataType::Decimal128(_, _) => { + wrapping_negative_array!(array, Decimal128Array) + } + DataType::Decimal256(_, _) => { + wrapping_negative_array!(array, Decimal256Array) + } Review Comment: ```suggestion // Decimal types - wrapping negation DataType::Decimal32(_, _) => { wrapping_negative_array!(array, Decimal32Array) } DataType::Decima64(_, _) => { wrapping_negative_array!(array, Decimal64Array) } DataType::Decimal128(_, _) => { wrapping_negative_array!(array, Decimal128Array) } DataType::Decimal256(_, _) => { wrapping_negative_array!(array, Decimal256Array) } ``` ########## datafusion/spark/src/function/math/negative.rs: ########## @@ -0,0 +1,410 @@ +// 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::*; +use arrow::datatypes::DataType; +use datafusion_common::{DataFusionError, Result, ScalarValue, internal_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. +/// +/// TODOs: +/// - 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) + } +} + +/// Helper macro to generate wrapping negation for array types +macro_rules! wrapping_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| x.wrapping_neg()); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate simple negation for floating point array types +macro_rules! simple_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| -x); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate wrapping negation for scalar types +macro_rules! wrapping_negative_scalar { + ($INPUT:ident, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE(Some( + result, + )))) + }}; +} + +/// Helper macro to generate wrapping negation for decimal scalar types +macro_rules! wrapping_negative_decimal_scalar { + ($INPUT:ident, $PRECISION:expr, $SCALE:expr, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE( + Some(result), + $PRECISION, + $SCALE, + ))) + }}; +} + +/// Core implementation of Spark's negative function +pub fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() != 1 { + return internal_err!( + "negative takes exactly 1 argument, but got: {}", + args.len() + ); + } + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null => Ok(args[0].clone()), + + // Signed integers - use wrapping negation (Spark legacy mode behavior) + DataType::Int8 => wrapping_negative_array!(array, Int8Array), + DataType::Int16 => wrapping_negative_array!(array, Int16Array), + DataType::Int32 => wrapping_negative_array!(array, Int32Array), + DataType::Int64 => wrapping_negative_array!(array, Int64Array), + + // Floating point - simple negation (no overflow possible) + DataType::Float16 => simple_negative_array!(array, Float16Array), + DataType::Float32 => simple_negative_array!(array, Float32Array), + DataType::Float64 => simple_negative_array!(array, Float64Array), + + // Decimal types - wrapping negation + DataType::Decimal128(_, _) => { + wrapping_negative_array!(array, Decimal128Array) + } + DataType::Decimal256(_, _) => { + wrapping_negative_array!(array, Decimal256Array) + } + + dt => internal_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)) => wrapping_negative_scalar!(v, Int8), + ScalarValue::Int16(Some(v)) => wrapping_negative_scalar!(v, Int16), + ScalarValue::Int32(Some(v)) => wrapping_negative_scalar!(v, Int32), + ScalarValue::Int64(Some(v)) => wrapping_negative_scalar!(v, Int64), + + // 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::Decimal128(Some(v), precision, scale) => { + wrapping_negative_decimal_scalar!(v, *precision, *scale, Decimal128) + } + ScalarValue::Decimal256(Some(v), precision, scale) => { + wrapping_negative_decimal_scalar!(v, *precision, *scale, Decimal256) + } + + dt => internal_err!("Not supported datatype for Spark NEGATIVE: {dt}"), + }, + } +} + +#[cfg(test)] +mod tests { + use super::*; + use arrow::datatypes::i256; + + /// Helper macro for testing scalar values with wrapping behavior + macro_rules! test_negative_scalar { Review Comment: Could we move all these tests to SLTs? ########## datafusion/spark/src/function/math/negative.rs: ########## @@ -0,0 +1,410 @@ +// 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::*; +use arrow::datatypes::DataType; +use datafusion_common::{DataFusionError, Result, ScalarValue, internal_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. +/// +/// TODOs: +/// - 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) + } +} + +/// Helper macro to generate wrapping negation for array types +macro_rules! wrapping_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT Review Comment: We can use `as_primitive` here to simplify downcasting https://docs.rs/arrow/latest/arrow/array/trait.AsArray.html#method.as_primitive And personally we could just inline these macro, I don't see it saving us that much code to be worth a macro ########## datafusion/spark/src/function/math/negative.rs: ########## @@ -0,0 +1,410 @@ +// 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::*; +use arrow::datatypes::DataType; +use datafusion_common::{DataFusionError, Result, ScalarValue, internal_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. +/// +/// TODOs: +/// - 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) + } +} + +/// Helper macro to generate wrapping negation for array types +macro_rules! wrapping_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| x.wrapping_neg()); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate simple negation for floating point array types +macro_rules! simple_negative_array { + ($INPUT:expr, $ARRAY_TYPE:ident) => {{ + let array = $INPUT + .as_any() + .downcast_ref::<$ARRAY_TYPE>() + .ok_or_else(|| { + DataFusionError::Internal(format!( + "Expected {}, got different type", + stringify!($ARRAY_TYPE) + )) + })?; + let result: $ARRAY_TYPE = array.unary(|x| -x); + Ok(ColumnarValue::Array(Arc::new(result))) + }}; +} + +/// Helper macro to generate wrapping negation for scalar types +macro_rules! wrapping_negative_scalar { + ($INPUT:ident, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE(Some( + result, + )))) + }}; +} + +/// Helper macro to generate wrapping negation for decimal scalar types +macro_rules! wrapping_negative_decimal_scalar { + ($INPUT:ident, $PRECISION:expr, $SCALE:expr, $SCALAR_TYPE:ident) => {{ + let result = $INPUT.wrapping_neg(); + Ok(ColumnarValue::Scalar(ScalarValue::$SCALAR_TYPE( + Some(result), + $PRECISION, + $SCALE, + ))) + }}; +} + +/// Core implementation of Spark's negative function +pub fn spark_negative(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() != 1 { + return internal_err!( + "negative takes exactly 1 argument, but got: {}", + args.len() + ); + } + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null => Ok(args[0].clone()), Review Comment: ```suggestion DataType::Null | DataType::UInt8 | DataType::UInt16 | DataType::UInt32 | DataType::UInt64 => Ok(args[0].clone()), ``` Even though Spark doesn't use these, our numeric signature does so it's nice for coverage -- This is an automated message from the Apache Git Service. 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