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| 1 | +// Licensed to the Apache Software Foundation (ASF) under one |
| 2 | +// or more contributor license agreements. See the NOTICE file |
| 3 | +// distributed with this work for additional information |
| 4 | +// regarding copyright ownership. The ASF licenses this file |
| 5 | +// to you under the Apache License, Version 2.0 (the |
| 6 | +// "License"); you may not use this file except in compliance |
| 7 | +// with the License. You may obtain a copy of the License at |
| 8 | +// |
| 9 | +// http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | +// |
| 11 | +// Unless required by applicable law or agreed to in writing, |
| 12 | +// software distributed under the License is distributed on an |
| 13 | +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| 14 | +// KIND, either express or implied. See the License for the |
| 15 | +// specific language governing permissions and limitations |
| 16 | +// under the License. |
| 17 | + |
| 18 | +use arrow::array::Array; |
| 19 | +use arrow::buffer::NullBuffer; |
| 20 | +use arrow::datatypes::DataType; |
| 21 | +use datafusion_common::{Result, ScalarValue}; |
| 22 | +use datafusion_expr::{ |
| 23 | + ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, TypeSignature, |
| 24 | + Volatility, |
| 25 | +}; |
| 26 | +use datafusion_functions::string::concat::ConcatFunc; |
| 27 | +use std::any::Any; |
| 28 | +use std::sync::Arc; |
| 29 | + |
| 30 | +/// Spark-compatible `concat` expression |
| 31 | +/// <https://spark.apache.org/docs/latest/api/sql/index.html#concat> |
| 32 | +/// |
| 33 | +/// Concatenates multiple input strings into a single string. |
| 34 | +/// Returns NULL if any input is NULL. |
| 35 | +/// |
| 36 | +/// Differences with DataFusion concat: |
| 37 | +/// - Support 0 arguments |
| 38 | +/// - Return NULL if any input is NULL |
| 39 | +#[derive(Debug, PartialEq, Eq, Hash)] |
| 40 | +pub struct SparkConcat { |
| 41 | + signature: Signature, |
| 42 | +} |
| 43 | + |
| 44 | +impl Default for SparkConcat { |
| 45 | + fn default() -> Self { |
| 46 | + Self::new() |
| 47 | + } |
| 48 | +} |
| 49 | + |
| 50 | +impl SparkConcat { |
| 51 | + pub fn new() -> Self { |
| 52 | + Self { |
| 53 | + signature: Signature::one_of( |
| 54 | + vec![TypeSignature::UserDefined, TypeSignature::Nullary], |
| 55 | + Volatility::Immutable, |
| 56 | + ), |
| 57 | + } |
| 58 | + } |
| 59 | +} |
| 60 | + |
| 61 | +impl ScalarUDFImpl for SparkConcat { |
| 62 | + fn as_any(&self) -> &dyn Any { |
| 63 | + self |
| 64 | + } |
| 65 | + |
| 66 | + fn name(&self) -> &str { |
| 67 | + "concat" |
| 68 | + } |
| 69 | + |
| 70 | + fn signature(&self) -> &Signature { |
| 71 | + &self.signature |
| 72 | + } |
| 73 | + |
| 74 | + fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { |
| 75 | + Ok(DataType::Utf8) |
| 76 | + } |
| 77 | + |
| 78 | + fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> { |
| 79 | + spark_concat(args) |
| 80 | + } |
| 81 | + |
| 82 | + fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> { |
| 83 | + // Accept any string types, including zero arguments |
| 84 | + Ok(arg_types.to_vec()) |
| 85 | + } |
| 86 | +} |
| 87 | + |
| 88 | +/// Represents the null state for Spark concat |
| 89 | +enum NullMaskResolution { |
| 90 | + /// Return NULL as the result (e.g., scalar inputs with at least one NULL) |
| 91 | + ReturnNull, |
| 92 | + /// No null mask needed (e.g., all scalar inputs are non-NULL) |
| 93 | + NoMask, |
| 94 | + /// Null mask to apply for arrays |
| 95 | + Apply(NullBuffer), |
| 96 | +} |
| 97 | + |
| 98 | +/// Concatenates strings, returning NULL if any input is NULL |
| 99 | +/// This is a Spark-specific wrapper around DataFusion's concat that returns NULL |
| 100 | +/// if any argument is NULL (Spark behavior), whereas DataFusion's concat ignores NULLs. |
| 101 | +fn spark_concat(args: ScalarFunctionArgs) -> Result<ColumnarValue> { |
| 102 | + let ScalarFunctionArgs { |
| 103 | + args: arg_values, |
| 104 | + arg_fields, |
| 105 | + number_rows, |
| 106 | + return_field, |
| 107 | + config_options, |
| 108 | + } = args; |
| 109 | + |
| 110 | + // Handle zero-argument case: return empty string |
| 111 | + if arg_values.is_empty() { |
| 112 | + return Ok(ColumnarValue::Scalar(ScalarValue::Utf8( |
| 113 | + Some(String::new()), |
| 114 | + ))); |
| 115 | + } |
| 116 | + |
| 117 | + // Step 1: Check for NULL mask in incoming args |
| 118 | + let null_mask = compute_null_mask(&arg_values, number_rows)?; |
| 119 | + |
| 120 | + // If all scalars and any is NULL, return NULL immediately |
| 121 | + if matches!(null_mask, NullMaskResolution::ReturnNull) { |
| 122 | + return Ok(ColumnarValue::Scalar(ScalarValue::Utf8(None))); |
| 123 | + } |
| 124 | + |
| 125 | + // Step 2: Delegate to DataFusion's concat |
| 126 | + let concat_func = ConcatFunc::new(); |
| 127 | + let func_args = ScalarFunctionArgs { |
| 128 | + args: arg_values, |
| 129 | + arg_fields, |
| 130 | + number_rows, |
| 131 | + return_field, |
| 132 | + config_options, |
| 133 | + }; |
| 134 | + let result = concat_func.invoke_with_args(func_args)?; |
| 135 | + |
| 136 | + // Step 3: Apply NULL mask to result |
| 137 | + apply_null_mask(result, null_mask) |
| 138 | +} |
| 139 | + |
| 140 | +/// Compute NULL mask for the arguments using NullBuffer::union |
| 141 | +fn compute_null_mask( |
| 142 | + args: &[ColumnarValue], |
| 143 | + number_rows: usize, |
| 144 | +) -> Result<NullMaskResolution> { |
| 145 | + // Check if all arguments are scalars |
| 146 | + let all_scalars = args |
| 147 | + .iter() |
| 148 | + .all(|arg| matches!(arg, ColumnarValue::Scalar(_))); |
| 149 | + |
| 150 | + if all_scalars { |
| 151 | + // For scalars, check if any is NULL |
| 152 | + for arg in args { |
| 153 | + if let ColumnarValue::Scalar(scalar) = arg { |
| 154 | + if scalar.is_null() { |
| 155 | + return Ok(NullMaskResolution::ReturnNull); |
| 156 | + } |
| 157 | + } |
| 158 | + } |
| 159 | + // No NULLs in scalars |
| 160 | + Ok(NullMaskResolution::NoMask) |
| 161 | + } else { |
| 162 | + // For arrays, compute NULL mask for each row using NullBuffer::union |
| 163 | + let array_len = args |
| 164 | + .iter() |
| 165 | + .find_map(|arg| match arg { |
| 166 | + ColumnarValue::Array(array) => Some(array.len()), |
| 167 | + _ => None, |
| 168 | + }) |
| 169 | + .unwrap_or(number_rows); |
| 170 | + |
| 171 | + // Convert all scalars to arrays for uniform processing |
| 172 | + let arrays: Result<Vec<_>> = args |
| 173 | + .iter() |
| 174 | + .map(|arg| match arg { |
| 175 | + ColumnarValue::Array(array) => Ok(Arc::clone(array)), |
| 176 | + ColumnarValue::Scalar(scalar) => scalar.to_array_of_size(array_len), |
| 177 | + }) |
| 178 | + .collect(); |
| 179 | + let arrays = arrays?; |
| 180 | + |
| 181 | + // Use NullBuffer::union to combine all null buffers |
| 182 | + let combined_nulls = arrays |
| 183 | + .iter() |
| 184 | + .map(|arr| arr.nulls()) |
| 185 | + .fold(None, |acc, nulls| NullBuffer::union(acc.as_ref(), nulls)); |
| 186 | + |
| 187 | + match combined_nulls { |
| 188 | + Some(nulls) => Ok(NullMaskResolution::Apply(nulls)), |
| 189 | + None => Ok(NullMaskResolution::NoMask), |
| 190 | + } |
| 191 | + } |
| 192 | +} |
| 193 | + |
| 194 | +/// Apply NULL mask to the result using NullBuffer::union |
| 195 | +fn apply_null_mask( |
| 196 | + result: ColumnarValue, |
| 197 | + null_mask: NullMaskResolution, |
| 198 | +) -> Result<ColumnarValue> { |
| 199 | + match (result, null_mask) { |
| 200 | + // Scalar with ReturnNull mask means return NULL |
| 201 | + (ColumnarValue::Scalar(_), NullMaskResolution::ReturnNull) => { |
| 202 | + Ok(ColumnarValue::Scalar(ScalarValue::Utf8(None))) |
| 203 | + } |
| 204 | + // Scalar without mask, return as-is |
| 205 | + (scalar @ ColumnarValue::Scalar(_), NullMaskResolution::NoMask) => Ok(scalar), |
| 206 | + // Array with NULL mask - use NullBuffer::union to combine nulls |
| 207 | + (ColumnarValue::Array(array), NullMaskResolution::Apply(null_mask)) => { |
| 208 | + // Combine the result's existing nulls with our computed null mask |
| 209 | + let combined_nulls = NullBuffer::union(array.nulls(), Some(&null_mask)); |
| 210 | + |
| 211 | + // Create new array with combined nulls |
| 212 | + let new_array = array |
| 213 | + .into_data() |
| 214 | + .into_builder() |
| 215 | + .nulls(combined_nulls) |
| 216 | + .build()?; |
| 217 | + |
| 218 | + Ok(ColumnarValue::Array(Arc::new(arrow::array::make_array( |
| 219 | + new_array, |
| 220 | + )))) |
| 221 | + } |
| 222 | + // Array without NULL mask, return as-is |
| 223 | + (array @ ColumnarValue::Array(_), NullMaskResolution::NoMask) => Ok(array), |
| 224 | + // Edge cases that shouldn't happen in practice |
| 225 | + (scalar, _) => Ok(scalar), |
| 226 | + } |
| 227 | +} |
| 228 | + |
| 229 | +#[cfg(test)] |
| 230 | +mod tests { |
| 231 | + use super::*; |
| 232 | + use crate::function::utils::test::test_scalar_function; |
| 233 | + use arrow::array::StringArray; |
| 234 | + use arrow::datatypes::DataType; |
| 235 | + use datafusion_common::Result; |
| 236 | + |
| 237 | + #[test] |
| 238 | + fn test_concat_basic() -> Result<()> { |
| 239 | + test_scalar_function!( |
| 240 | + SparkConcat::new(), |
| 241 | + vec![ |
| 242 | + ColumnarValue::Scalar(ScalarValue::Utf8(Some("Spark".to_string()))), |
| 243 | + ColumnarValue::Scalar(ScalarValue::Utf8(Some("SQL".to_string()))), |
| 244 | + ], |
| 245 | + Ok(Some("SparkSQL")), |
| 246 | + &str, |
| 247 | + DataType::Utf8, |
| 248 | + StringArray |
| 249 | + ); |
| 250 | + Ok(()) |
| 251 | + } |
| 252 | + |
| 253 | + #[test] |
| 254 | + fn test_concat_with_null() -> Result<()> { |
| 255 | + test_scalar_function!( |
| 256 | + SparkConcat::new(), |
| 257 | + vec![ |
| 258 | + ColumnarValue::Scalar(ScalarValue::Utf8(Some("Spark".to_string()))), |
| 259 | + ColumnarValue::Scalar(ScalarValue::Utf8(Some("SQL".to_string()))), |
| 260 | + ColumnarValue::Scalar(ScalarValue::Utf8(None)), |
| 261 | + ], |
| 262 | + Ok(None), |
| 263 | + &str, |
| 264 | + DataType::Utf8, |
| 265 | + StringArray |
| 266 | + ); |
| 267 | + Ok(()) |
| 268 | + } |
| 269 | +} |
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