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feat(function): add greatest function #12474
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// 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. | ||
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use arrow::array::{make_comparator, Array, ArrayRef, BooleanArray}; | ||
use arrow::compute::kernels::cmp; | ||
use arrow::compute::kernels::zip::zip; | ||
use arrow::compute::SortOptions; | ||
use arrow::datatypes::DataType; | ||
use arrow_buffer::BooleanBuffer; | ||
use datafusion_common::{exec_err, plan_err, Result, ScalarValue}; | ||
use datafusion_expr::binary::type_union_resolution; | ||
use datafusion_expr::scalar_doc_sections::DOC_SECTION_CONDITIONAL; | ||
use datafusion_expr::{ColumnarValue, Documentation}; | ||
use datafusion_expr::{ScalarUDFImpl, Signature, Volatility}; | ||
use std::any::Any; | ||
use std::sync::{Arc, OnceLock}; | ||
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const SORT_OPTIONS: SortOptions = SortOptions { | ||
// We want greatest first | ||
descending: false, | ||
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// NULL will be less than any other value | ||
nulls_first: true, | ||
}; | ||
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#[derive(Debug)] | ||
pub struct GreatestFunc { | ||
signature: Signature, | ||
} | ||
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impl Default for GreatestFunc { | ||
fn default() -> Self { | ||
GreatestFunc::new() | ||
} | ||
} | ||
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impl GreatestFunc { | ||
pub fn new() -> Self { | ||
Self { | ||
signature: Signature::user_defined(Volatility::Immutable), | ||
} | ||
} | ||
} | ||
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fn get_logical_null_count(arr: &dyn Array) -> usize { | ||
arr.logical_nulls() | ||
.map(|n| n.null_count()) | ||
.unwrap_or_default() | ||
} | ||
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/// Return boolean array where `arr[i] = lhs[i] >= rhs[i]` for all i, where `arr` is the result array | ||
/// Nulls are always considered smaller than any other value | ||
fn get_larger(lhs: &dyn Array, rhs: &dyn Array) -> Result<BooleanArray> { | ||
// Fast path: | ||
// If both arrays are not nested, have the same length and no nulls, we can use the faster vectorised kernel | ||
// - If both arrays are not nested: Nested types, such as lists, are not supported as the null semantics are not well-defined. | ||
// - both array does not have any nulls: cmp::gt_eq will return null if any of the input is null while we want to return false in that case | ||
if !lhs.data_type().is_nested() | ||
&& get_logical_null_count(lhs) == 0 | ||
&& get_logical_null_count(rhs) == 0 | ||
{ | ||
return cmp::gt_eq(&lhs, &rhs).map_err(|e| e.into()); | ||
} | ||
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let cmp = make_comparator(lhs, rhs, SORT_OPTIONS)?; | ||
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if lhs.len() != rhs.len() { | ||
return exec_err!( | ||
"All arrays should have the same length for greatest comparison" | ||
); | ||
} | ||
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let values = BooleanBuffer::collect_bool(lhs.len(), |i| cmp(i, i).is_ge()); | ||
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// No nulls as we only want to keep the values that are larger, its either true or false | ||
Ok(BooleanArray::new(values, None)) | ||
} | ||
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/// Return array where the largest value at each index is kept | ||
fn keep_larger(lhs: ArrayRef, rhs: ArrayRef) -> Result<ArrayRef> { | ||
// True for values that we should keep from the left array | ||
let keep_lhs = get_larger(lhs.as_ref(), rhs.as_ref())?; | ||
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let larger = zip(&keep_lhs, &lhs, &rhs)?; | ||
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Ok(larger) | ||
} | ||
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fn keep_larger_scalar<'a>( | ||
lhs: &'a ScalarValue, | ||
rhs: &'a ScalarValue, | ||
) -> Result<&'a ScalarValue> { | ||
if !lhs.data_type().is_nested() { | ||
return if lhs >= rhs { Ok(lhs) } else { Ok(rhs) }; | ||
} | ||
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// If complex type we can't compare directly as we want null values to be smaller | ||
let cmp = make_comparator( | ||
lhs.to_array()?.as_ref(), | ||
rhs.to_array()?.as_ref(), | ||
SORT_OPTIONS, | ||
)?; | ||
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if cmp(0, 0).is_ge() { | ||
Ok(lhs) | ||
} else { | ||
Ok(rhs) | ||
} | ||
} | ||
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fn find_coerced_type(data_types: &[DataType]) -> Result<DataType> { | ||
if data_types.is_empty() { | ||
plan_err!("greatest was called without any arguments. It requires at least 1.") | ||
} else if let Some(coerced_type) = type_union_resolution(data_types) { | ||
Ok(coerced_type) | ||
} else { | ||
plan_err!("Cannot find a common type for arguments") | ||
} | ||
} | ||
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impl ScalarUDFImpl for GreatestFunc { | ||
fn as_any(&self) -> &dyn Any { | ||
self | ||
} | ||
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fn name(&self) -> &str { | ||
"greatest" | ||
} | ||
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fn signature(&self) -> &Signature { | ||
&self.signature | ||
} | ||
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fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { | ||
Ok(arg_types[0].clone()) | ||
} | ||
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fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> { | ||
if args.is_empty() { | ||
return exec_err!( | ||
"greatest was called with no arguments. It requires at least 1." | ||
); | ||
} | ||
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// Some engines (e.g. SQL Server) allow greatest with single arg, it's a noop | ||
if args.len() == 1 { | ||
return Ok(args[0].clone()); | ||
} | ||
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// Split to scalars and arrays for later optimization | ||
let (scalars, arrays): (Vec<_>, Vec<_>) = args.iter().partition(|x| match x { | ||
ColumnarValue::Scalar(_) => true, | ||
ColumnarValue::Array(_) => false, | ||
}); | ||
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let mut arrays_iter = arrays.iter().map(|x| match x { | ||
ColumnarValue::Array(a) => a, | ||
_ => unreachable!(), | ||
}); | ||
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let first_array = arrays_iter.next(); | ||
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let mut largest: ArrayRef; | ||
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// Optimization: merge all scalars into one to avoid recomputing | ||
if !scalars.is_empty() { | ||
let mut scalars_iter = scalars.iter().map(|x| match x { | ||
ColumnarValue::Scalar(s) => s, | ||
_ => unreachable!(), | ||
}); | ||
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// We have at least one scalar | ||
let mut largest_scalar = scalars_iter.next().unwrap(); | ||
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for scalar in scalars_iter { | ||
largest_scalar = keep_larger_scalar(largest_scalar, scalar)?; | ||
} | ||
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// If we only have scalars, return the largest one | ||
if arrays.is_empty() { | ||
return Ok(ColumnarValue::Scalar(largest_scalar.clone())); | ||
} | ||
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// We have at least one array | ||
let first_array = first_array.unwrap(); | ||
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// Start with the largest value | ||
largest = keep_larger( | ||
Arc::clone(first_array), | ||
largest_scalar.to_array_of_size(first_array.len())?, | ||
)?; | ||
} else { | ||
// If we only have arrays, start with the first array | ||
// (We must have at least one array) | ||
largest = Arc::clone(first_array.unwrap()); | ||
} | ||
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for array in arrays_iter { | ||
largest = keep_larger(Arc::clone(array), largest)?; | ||
} | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I'll do it in a separate PR, this PR is large enough There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Tried using |
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Ok(ColumnarValue::Array(largest)) | ||
} | ||
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fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> { | ||
let coerced_type = find_coerced_type(arg_types)?; | ||
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Ok(vec![coerced_type; arg_types.len()]) | ||
} | ||
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fn documentation(&self) -> Option<&Documentation> { | ||
Some(get_greatest_doc()) | ||
} | ||
} | ||
static DOCUMENTATION: OnceLock<Documentation> = OnceLock::new(); | ||
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fn get_greatest_doc() -> &'static Documentation { | ||
DOCUMENTATION.get_or_init(|| { | ||
Documentation::builder() | ||
.with_doc_section(DOC_SECTION_CONDITIONAL) | ||
.with_description("Returns the greatest value in a list of expressions. Returns _null_ if all expressions are _null_.") | ||
.with_syntax_example("greatest(expression1[, ..., expression_n])") | ||
.with_sql_example(r#"```sql | ||
> select greatest(4, 7, 5); | ||
+---------------------------+ | ||
| greatest(4,7,5) | | ||
+---------------------------+ | ||
| 7 | | ||
+---------------------------+ | ||
```"#, | ||
) | ||
.with_argument( | ||
"expression1, expression_n", | ||
"Expressions to compare and return the greatest value.. Can be a constant, column, or function, and any combination of arithmetic operators. Pass as many expression arguments as necessary." | ||
) | ||
.build() | ||
.unwrap() | ||
}) | ||
} | ||
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#[cfg(test)] | ||
mod test { | ||
use crate::core; | ||
use arrow::datatypes::DataType; | ||
use datafusion_expr::ScalarUDFImpl; | ||
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#[test] | ||
fn test_greatest_return_types_without_common_supertype_in_arg_type() { | ||
let greatest = core::greatest::GreatestFunc::new(); | ||
let return_type = greatest | ||
.coerce_types(&[DataType::Decimal128(10, 3), DataType::Decimal128(10, 4)]) | ||
.unwrap(); | ||
assert_eq!( | ||
return_type, | ||
vec![DataType::Decimal128(11, 4), DataType::Decimal128(11, 4)] | ||
); | ||
} | ||
} |
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please add a test with float NaN values.
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done