martin-g commented on code in PR #2689: URL: https://github.com/apache/datafusion-comet/pull/2689#discussion_r2493761862
########## native/spark-expr/src/math_funcs/abs.rs: ########## @@ -0,0 +1,879 @@ +// 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 crate::arithmetic_overflow_error; +use arrow::array::*; +use arrow::datatypes::*; +use arrow::error::ArrowError; +use datafusion::common::{exec_err, DataFusionError, Result, ScalarValue}; +use datafusion::logical_expr::ColumnarValue; +use std::sync::Arc; + +macro_rules! legacy_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + let res: $RESULT = arrow::compute::kernels::arity::unary(array, |x| x.$FUNC()); + Ok(res) + } + _ => Err(DataFusionError::Internal(format!( + "Invalid data type for abs" + ))), + } + }}; +} + +macro_rules! ansi_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident, $NATIVE:ident, $FROM_TYPE:expr) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + match arrow::compute::kernels::arity::try_unary(array, |x| { + if x == $NATIVE::MIN { + Err(ArrowError::ArithmeticOverflow($FROM_TYPE.to_string())) + } else { + Ok(x.$FUNC()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::<PrimitiveArray<$RESULT>>::new( + res, + ))), + Err(_) => Err(arithmetic_overflow_error($FROM_TYPE).into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + }}; +} + +/// This function mimics SparkSQL's [Abs]: https://github.com/apache/spark/blob/v4.0.1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L148 +/// Spark's [ANSI-compliant]: https://spark.apache.org/docs/latest/sql-ref-ansi-compliance.html#arithmetic-operations dialect mode throws org.apache.spark.SparkArithmeticException +/// when abs causes overflow. +pub fn abs(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() > 2 { + return exec_err!("abs takes at most 2 arguments, but got: {}", args.len()); + } + + let fail_on_error = if args.len() == 2 { + match &args[1] { + ColumnarValue::Scalar(ScalarValue::Boolean(Some(fail_on_error))) => *fail_on_error, + _ => { + return exec_err!( + "The second argument must be boolean scalar, but got: {:?}", + args[1] + ); + } + } + } else { + false + }; + + match &args[0] { Review Comment: There is no check that the function is called with at least one argument. ########## native/spark-expr/src/math_funcs/abs.rs: ########## @@ -0,0 +1,879 @@ +// 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 crate::arithmetic_overflow_error; +use arrow::array::*; +use arrow::datatypes::*; +use arrow::error::ArrowError; +use datafusion::common::{exec_err, DataFusionError, Result, ScalarValue}; +use datafusion::logical_expr::ColumnarValue; +use std::sync::Arc; + +macro_rules! legacy_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + let res: $RESULT = arrow::compute::kernels::arity::unary(array, |x| x.$FUNC()); + Ok(res) + } + _ => Err(DataFusionError::Internal(format!( + "Invalid data type for abs" + ))), + } + }}; +} + +macro_rules! ansi_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident, $NATIVE:ident, $FROM_TYPE:expr) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + match arrow::compute::kernels::arity::try_unary(array, |x| { + if x == $NATIVE::MIN { + Err(ArrowError::ArithmeticOverflow($FROM_TYPE.to_string())) + } else { + Ok(x.$FUNC()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::<PrimitiveArray<$RESULT>>::new( + res, + ))), + Err(_) => Err(arithmetic_overflow_error($FROM_TYPE).into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + }}; +} + +/// This function mimics SparkSQL's [Abs]: https://github.com/apache/spark/blob/v4.0.1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L148 +/// Spark's [ANSI-compliant]: https://spark.apache.org/docs/latest/sql-ref-ansi-compliance.html#arithmetic-operations dialect mode throws org.apache.spark.SparkArithmeticException +/// when abs causes overflow. +pub fn abs(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() > 2 { + return exec_err!("abs takes at most 2 arguments, but got: {}", args.len()); + } + + let fail_on_error = if args.len() == 2 { + match &args[1] { + ColumnarValue::Scalar(ScalarValue::Boolean(Some(fail_on_error))) => *fail_on_error, + _ => { + return exec_err!( + "The second argument must be boolean scalar, but got: {:?}", + args[1] + ); + } + } + } else { + false + }; + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => Ok(args[0].clone()), + DataType::Int8 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int8Array, Int8Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int8Array, Int8Type, i8, "Int8") + } + } + DataType::Int16 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int16Array, Int16Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int16Array, Int16Type, i16, "Int16") + } + } + DataType::Int32 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int32Array, Int32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int32Array, Int32Type, i32, "Int32") + } + } + DataType::Int64 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int64Array, Int64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int64Array, Int64Type, i64, "Int64") + } + } + DataType::Float32 => { + let result = legacy_compute_op!(array, abs, Float32Array, Float32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Float64 => { + let result = legacy_compute_op!(array, abs, Float64Array, Float64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Decimal128(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal128Array, Decimal128Array)?; + let result = result.with_data_type(DataType::Decimal128(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal128Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i128::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal128".to_string())) + } else { + Ok(x.abs()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal128Type>, + >::new( + res.with_data_type(DataType::Decimal128(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal128").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + DataType::Decimal256(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal256Array, Decimal256Array)?; + let result = result.with_data_type(DataType::Decimal256(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal256Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i256::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal256".to_string())) + } else { + Ok(x.wrapping_abs()) // i256 doesn't define abs() method + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal256Type>, + >::new( + res.with_data_type(DataType::Decimal256(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal256").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + dt => exec_err!("Not supported datatype for ABS: {dt}"), + }, + ColumnarValue::Scalar(sv) => match sv { + ScalarValue::Null + | ScalarValue::UInt8(_) + | ScalarValue::UInt16(_) + | ScalarValue::UInt32(_) + | ScalarValue::UInt64(_) => Ok(args[0].clone()), + ScalarValue::Int8(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int8").into()) + } + } + }, + }, + ScalarValue::Int16(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int16(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int16(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int16").into()) + } + } + }, + }, + ScalarValue::Int32(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int32(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int32(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int32").into()) + } + } + }, + }, + ScalarValue::Int64(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int64").into()) + } + } + }, + }, + ScalarValue::Float32(a) => Ok(ColumnarValue::Scalar(ScalarValue::Float32( + a.map(|x| x.abs()), + ))), + ScalarValue::Float64(a) => Ok(ColumnarValue::Scalar(ScalarValue::Float64( + a.map(|x| x.abs()), + ))), + ScalarValue::Decimal128(a, precision, scale) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Decimal128( + Some(abs_val), + *precision, + *scale, + ))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Decimal128( + Some(*v), + *precision, + *scale, + ))) + } else { + Err(arithmetic_overflow_error("Decimal128").into()) + } + } + }, + }, + ScalarValue::Decimal256(a, precision, scale) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Decimal256( + Some(abs_val), + *precision, + *scale, + ))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Decimal256( + Some(*v), + *precision, + *scale, + ))) + } else { + Err(arithmetic_overflow_error("Decimal256").into()) + } + } + }, + }, + dt => exec_err!("Not supported datatype for ABS: {dt}"), + }, + } +} + +#[cfg(test)] +mod tests { + use super::*; + use datafusion::common::cast::{ + as_decimal128_array, as_decimal256_array, as_float32_array, as_float64_array, + as_int16_array, as_int32_array, as_int64_array, as_int8_array, as_uint64_array, + }; + + fn with_fail_on_error<F: Fn(bool) -> Result<()>>(test_fn: F) { + for fail_on_error in [true, false] { + let _ = test_fn(fail_on_error); Review Comment: This would ignore the returned Result. ```suggestion let _ = test_fn(fail_on_error)?; ``` ########## native/spark-expr/src/math_funcs/abs.rs: ########## @@ -0,0 +1,879 @@ +// 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 crate::arithmetic_overflow_error; +use arrow::array::*; +use arrow::datatypes::*; +use arrow::error::ArrowError; +use datafusion::common::{exec_err, DataFusionError, Result, ScalarValue}; +use datafusion::logical_expr::ColumnarValue; +use std::sync::Arc; + +macro_rules! legacy_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + let res: $RESULT = arrow::compute::kernels::arity::unary(array, |x| x.$FUNC()); + Ok(res) + } + _ => Err(DataFusionError::Internal(format!( + "Invalid data type for abs" + ))), + } + }}; +} + +macro_rules! ansi_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident, $NATIVE:ident, $FROM_TYPE:expr) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + match arrow::compute::kernels::arity::try_unary(array, |x| { + if x == $NATIVE::MIN { + Err(ArrowError::ArithmeticOverflow($FROM_TYPE.to_string())) + } else { + Ok(x.$FUNC()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::<PrimitiveArray<$RESULT>>::new( + res, + ))), + Err(_) => Err(arithmetic_overflow_error($FROM_TYPE).into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + }}; +} + +/// This function mimics SparkSQL's [Abs]: https://github.com/apache/spark/blob/v4.0.1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L148 +/// Spark's [ANSI-compliant]: https://spark.apache.org/docs/latest/sql-ref-ansi-compliance.html#arithmetic-operations dialect mode throws org.apache.spark.SparkArithmeticException +/// when abs causes overflow. +pub fn abs(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() > 2 { + return exec_err!("abs takes at most 2 arguments, but got: {}", args.len()); + } + + let fail_on_error = if args.len() == 2 { + match &args[1] { + ColumnarValue::Scalar(ScalarValue::Boolean(Some(fail_on_error))) => *fail_on_error, + _ => { + return exec_err!( + "The second argument must be boolean scalar, but got: {:?}", + args[1] + ); + } + } + } else { + false + }; + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => Ok(args[0].clone()), + DataType::Int8 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int8Array, Int8Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int8Array, Int8Type, i8, "Int8") + } + } + DataType::Int16 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int16Array, Int16Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int16Array, Int16Type, i16, "Int16") + } + } + DataType::Int32 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int32Array, Int32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int32Array, Int32Type, i32, "Int32") + } + } + DataType::Int64 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int64Array, Int64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int64Array, Int64Type, i64, "Int64") + } + } + DataType::Float32 => { + let result = legacy_compute_op!(array, abs, Float32Array, Float32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Float64 => { + let result = legacy_compute_op!(array, abs, Float64Array, Float64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Decimal128(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal128Array, Decimal128Array)?; + let result = result.with_data_type(DataType::Decimal128(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal128Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i128::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal128".to_string())) + } else { + Ok(x.abs()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal128Type>, + >::new( + res.with_data_type(DataType::Decimal128(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal128").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + DataType::Decimal256(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal256Array, Decimal256Array)?; + let result = result.with_data_type(DataType::Decimal256(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal256Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i256::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal256".to_string())) + } else { + Ok(x.wrapping_abs()) // i256 doesn't define abs() method + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal256Type>, + >::new( + res.with_data_type(DataType::Decimal256(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal256").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + dt => exec_err!("Not supported datatype for ABS: {dt}"), + }, + ColumnarValue::Scalar(sv) => match sv { + ScalarValue::Null + | ScalarValue::UInt8(_) + | ScalarValue::UInt16(_) + | ScalarValue::UInt32(_) + | ScalarValue::UInt64(_) => Ok(args[0].clone()), + ScalarValue::Int8(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int8").into()) + } + } + }, + }, + ScalarValue::Int16(a) => match a { + None => Ok(args[0].clone()), Review Comment: nit: The code for handling Int16/32/64 is very similar to Int8. It could be extracted to a declarative macro. ########## spark/src/test/scala/org/apache/comet/CometMathExpressionSuite.scala: ########## @@ -0,0 +1,93 @@ +/* + * 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. + */ + +package org.apache.comet + +import scala.util.Random + +import org.apache.spark.sql.CometTestBase +import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{DataTypes, StructField, StructType} + +import org.apache.comet.testing.{DataGenOptions, FuzzDataGenerator} + +class CometMathExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelper { + + test("abs") { + val df = createTestData(generateNegativeZero = false) + df.createOrReplaceTempView("tbl") + for (field <- df.schema.fields) { + val col = field.name + checkSparkAnswerAndOperator(s"SELECT $col, abs($col) FROM tbl ORDER BY $col") + } + } + + test("abs - negative zero") { + val df = createTestData(generateNegativeZero = true) + df.createOrReplaceTempView("tbl") + for (field <- df.schema.fields.filter(f => + f.dataType == DataTypes.FloatType || f.dataType == DataTypes.DoubleType)) { + val col = field.name + checkSparkAnswerAndOperator( + s"SELECT $col, abs($col) FROM tbl WHERE signum($col) < 0 ORDER BY $col") Review Comment: If the value of `$col` is `-0.0` (a negative zero) then `signum($col)` would return `0`. Just checking whether this is the intended behavior because the test is about negative zero specifically. ########## native/spark-expr/src/math_funcs/abs.rs: ########## @@ -0,0 +1,879 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use crate::arithmetic_overflow_error; +use arrow::array::*; +use arrow::datatypes::*; +use arrow::error::ArrowError; +use datafusion::common::{exec_err, DataFusionError, Result, ScalarValue}; +use datafusion::logical_expr::ColumnarValue; +use std::sync::Arc; + +macro_rules! legacy_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + let res: $RESULT = arrow::compute::kernels::arity::unary(array, |x| x.$FUNC()); + Ok(res) + } + _ => Err(DataFusionError::Internal(format!( + "Invalid data type for abs" + ))), + } + }}; +} + +macro_rules! ansi_compute_op { + ($ARRAY:expr, $FUNC:ident, $TYPE:ident, $RESULT:ident, $NATIVE:ident, $FROM_TYPE:expr) => {{ + let n = $ARRAY.as_any().downcast_ref::<$TYPE>(); + match n { + Some(array) => { + match arrow::compute::kernels::arity::try_unary(array, |x| { + if x == $NATIVE::MIN { + Err(ArrowError::ArithmeticOverflow($FROM_TYPE.to_string())) + } else { + Ok(x.$FUNC()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::<PrimitiveArray<$RESULT>>::new( + res, + ))), + Err(_) => Err(arithmetic_overflow_error($FROM_TYPE).into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + }}; +} + +/// This function mimics SparkSQL's [Abs]: https://github.com/apache/spark/blob/v4.0.1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L148 +/// Spark's [ANSI-compliant]: https://spark.apache.org/docs/latest/sql-ref-ansi-compliance.html#arithmetic-operations dialect mode throws org.apache.spark.SparkArithmeticException +/// when abs causes overflow. +pub fn abs(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> { + if args.len() > 2 { + return exec_err!("abs takes at most 2 arguments, but got: {}", args.len()); + } + + let fail_on_error = if args.len() == 2 { + match &args[1] { + ColumnarValue::Scalar(ScalarValue::Boolean(Some(fail_on_error))) => *fail_on_error, + _ => { + return exec_err!( + "The second argument must be boolean scalar, but got: {:?}", + args[1] + ); + } + } + } else { + false + }; + + match &args[0] { + ColumnarValue::Array(array) => match array.data_type() { + DataType::Null + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => Ok(args[0].clone()), + DataType::Int8 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int8Array, Int8Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int8Array, Int8Type, i8, "Int8") + } + } + DataType::Int16 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int16Array, Int16Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int16Array, Int16Type, i16, "Int16") + } + } + DataType::Int32 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int32Array, Int32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int32Array, Int32Type, i32, "Int32") + } + } + DataType::Int64 => { + if !fail_on_error { + let result = legacy_compute_op!(array, wrapping_abs, Int64Array, Int64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } else { + ansi_compute_op!(array, abs, Int64Array, Int64Type, i64, "Int64") + } + } + DataType::Float32 => { + let result = legacy_compute_op!(array, abs, Float32Array, Float32Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Float64 => { + let result = legacy_compute_op!(array, abs, Float64Array, Float64Array); + Ok(ColumnarValue::Array(Arc::new(result?))) + } + DataType::Decimal128(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal128Array, Decimal128Array)?; + let result = result.with_data_type(DataType::Decimal128(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal128Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i128::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal128".to_string())) + } else { + Ok(x.abs()) + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal128Type>, + >::new( + res.with_data_type(DataType::Decimal128(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal128").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + DataType::Decimal256(precision, scale) => { + if !fail_on_error { + let result = + legacy_compute_op!(array, wrapping_abs, Decimal256Array, Decimal256Array)?; + let result = result.with_data_type(DataType::Decimal256(*precision, *scale)); + Ok(ColumnarValue::Array(Arc::new(result))) + } else { + // Need to pass precision and scale from input, so not using ansi_compute_op + let input = array.as_any().downcast_ref::<Decimal256Array>(); + match input { + Some(i) => { + match arrow::compute::kernels::arity::try_unary(i, |x| { + if x == i256::MIN { + Err(ArrowError::ArithmeticOverflow("Decimal256".to_string())) + } else { + Ok(x.wrapping_abs()) // i256 doesn't define abs() method + } + }) { + Ok(res) => Ok(ColumnarValue::Array(Arc::< + PrimitiveArray<Decimal256Type>, + >::new( + res.with_data_type(DataType::Decimal256(*precision, *scale)), + ))), + Err(_) => Err(arithmetic_overflow_error("Decimal256").into()), + } + } + _ => Err(DataFusionError::Internal("Invalid data type".to_string())), + } + } + } + dt => exec_err!("Not supported datatype for ABS: {dt}"), + }, + ColumnarValue::Scalar(sv) => match sv { + ScalarValue::Null + | ScalarValue::UInt8(_) + | ScalarValue::UInt16(_) + | ScalarValue::UInt32(_) + | ScalarValue::UInt64(_) => Ok(args[0].clone()), + ScalarValue::Int8(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int8(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int8").into()) + } + } + }, + }, + ScalarValue::Int16(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int16(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int16(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int16").into()) + } + } + }, + }, + ScalarValue::Int32(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int32(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int32(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int32").into()) + } + } + }, + }, + ScalarValue::Int64(a) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(abs_val)))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Int64(Some(*v)))) + } else { + Err(arithmetic_overflow_error("Int64").into()) + } + } + }, + }, + ScalarValue::Float32(a) => Ok(ColumnarValue::Scalar(ScalarValue::Float32( + a.map(|x| x.abs()), + ))), + ScalarValue::Float64(a) => Ok(ColumnarValue::Scalar(ScalarValue::Float64( + a.map(|x| x.abs()), + ))), + ScalarValue::Decimal128(a, precision, scale) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Decimal128( + Some(abs_val), + *precision, + *scale, + ))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Decimal128( + Some(*v), + *precision, + *scale, + ))) + } else { + Err(arithmetic_overflow_error("Decimal128").into()) + } + } + }, + }, + ScalarValue::Decimal256(a, precision, scale) => match a { + None => Ok(args[0].clone()), + Some(v) => match v.checked_abs() { + Some(abs_val) => Ok(ColumnarValue::Scalar(ScalarValue::Decimal256( + Some(abs_val), + *precision, + *scale, + ))), + None => { + if !fail_on_error { + // return the original value + Ok(ColumnarValue::Scalar(ScalarValue::Decimal256( + Some(*v), + *precision, + *scale, + ))) + } else { + Err(arithmetic_overflow_error("Decimal256").into()) + } + } + }, + }, + dt => exec_err!("Not supported datatype for ABS: {dt}"), + }, + } +} + +#[cfg(test)] +mod tests { + use super::*; + use datafusion::common::cast::{ + as_decimal128_array, as_decimal256_array, as_float32_array, as_float64_array, + as_int16_array, as_int32_array, as_int64_array, as_int8_array, as_uint64_array, + }; + + fn with_fail_on_error<F: Fn(bool) -> Result<()>>(test_fn: F) { + for fail_on_error in [true, false] { + let _ = test_fn(fail_on_error); + } + } + + // Unsigned types, return as is + #[test] + fn test_abs_u8_scalar() { + with_fail_on_error(|fail_on_error| { + let args = ColumnarValue::Scalar(ScalarValue::UInt8(Some(u8::MAX))); + let fail_on_error_arg = + ColumnarValue::Scalar(ScalarValue::Boolean(Some(fail_on_error))); + match abs(&[args, fail_on_error_arg]) { + Ok(ColumnarValue::Scalar(ScalarValue::UInt8(Some(result)))) => { + assert_eq!(result, u8::MAX); + Ok(()) + } + Err(e) => { + if fail_on_error { Review Comment: Is it possible that it would overflow for unsigned integers ? IMO it should always panic here, i.e. leave only the `else` clause body. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
