gstvg commented on code in PR #12116: URL: https://github.com/apache/datafusion/pull/12116#discussion_r1727606208
########## datafusion/functions/src/core/union_extract.rs: ########## @@ -0,0 +1,722 @@ +// 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 std::cmp::Ordering; +use std::sync::Arc; + +use arrow::array::{ + layout, make_array, new_empty_array, new_null_array, Array, ArrayRef, BooleanArray, + Int32Array, Scalar, UnionArray, +}; +use arrow::compute::take; +use arrow::datatypes::{DataType, FieldRef, UnionFields, UnionMode}; + +use arrow::buffer::{BooleanBuffer, MutableBuffer, NullBuffer, ScalarBuffer}; +use arrow::util::bit_util; +use datafusion_common::cast::as_union_array; +use datafusion_common::{ + exec_datafusion_err, exec_err, internal_err, ExprSchema, Result, ScalarValue, +}; +use datafusion_expr::{ColumnarValue, Expr}; +use datafusion_expr::{ScalarUDFImpl, Signature, Volatility}; + +#[derive(Debug)] +pub struct UnionExtractFun { + signature: Signature, +} + +impl Default for UnionExtractFun { + fn default() -> Self { + Self::new() + } +} + +impl UnionExtractFun { + pub fn new() -> Self { + Self { + signature: Signature::any(2, Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for UnionExtractFun { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn name(&self) -> &str { + "union_extract" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, _: &[DataType]) -> Result<DataType> { + // should be using return_type_from_exprs and not calling the default implementation + internal_err!("union_extract should return type from exprs") + } + + fn return_type_from_exprs( + &self, + args: &[Expr], + _: &dyn ExprSchema, + arg_types: &[DataType], + ) -> Result<DataType> { + if args.len() != 2 { + return exec_err!( + "union_extract expects 2 arguments, got {} instead", + args.len() + ); + } + + let fields = if let DataType::Union(fields, _) = &arg_types[0] { + fields + } else { + return exec_err!( + "union_extract first argument must be a union, got {} instead", + arg_types[0] + ); + }; + + let field_name = if let Expr::Literal(ScalarValue::Utf8(Some(field_name))) = + &args[1] + { + field_name + } else { + return exec_err!( + "union_extract second argument must be a non-null string literal, got {} instead", + arg_types[1] + ); + }; + + let field = find_field(fields, field_name)?.1; + + Ok(field.data_type().clone()) + } + + fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> { + if args.len() != 2 { + return exec_err!( + "union_extract expects 2 arguments, got {} instead", + args.len() + ); + } + + let union = &args[0]; + + let target_name = match &args[1] { + ColumnarValue::Scalar(ScalarValue::Utf8(Some(target_name))) => Ok(target_name), + ColumnarValue::Scalar(ScalarValue::Utf8(None)) => exec_err!("union_extract second argument must be a non-null string literal, got a null instead"), + _ => exec_err!("union_extract second argument must be a non-null string literal, got {} instead", &args[1].data_type()), + }; + + match union { + ColumnarValue::Array(array) => { + let union_array = as_union_array(&array).map_err(|_| { + exec_datafusion_err!( + "union_extract first argument must be a union, got {} instead", + array.data_type() + ) + })?; + + let (fields, mode) = match union_array.data_type() { + DataType::Union(fields, mode) => (fields, mode), + _ => unreachable!(), + }; + + let target_type_id = find_field(fields, target_name?)?.0; + + match mode { + UnionMode::Sparse => { + Ok(extract_sparse(union_array, fields, target_type_id)?) + } + UnionMode::Dense => { + Ok(extract_dense(union_array, fields, target_type_id)?) + } + } + } + ColumnarValue::Scalar(ScalarValue::Union(value, fields, _)) => { + let target_name = target_name?; + let (target_type_id, target) = find_field(fields, target_name)?; + + let result = match value { + Some((type_id, value)) if target_type_id == *type_id => { + *value.clone() + } + _ => ScalarValue::try_from(target.data_type())?, + }; + + Ok(ColumnarValue::Scalar(result)) + } + other => exec_err!( + "union_extract first argument must be a union, got {} instead", + other.data_type() + ), + } + } +} + +fn find_field<'a>(fields: &'a UnionFields, name: &str) -> Result<(i8, &'a FieldRef)> { + fields + .iter() + .find(|field| field.1.name() == name) + .ok_or_else(|| exec_datafusion_err!("field {name} not found on union")) +} + +fn extract_sparse( + union_array: &UnionArray, + fields: &UnionFields, + target_type_id: i8, +) -> Result<ColumnarValue> { + let target = union_array.child(target_type_id); + + if fields.len() == 1 // case 1.1: if there is a single field, all type ids are the same, and since union doesn't have a null mask, the result array is exactly the same as it only child + || union_array.is_empty() // case 1.2: sparse union length and childrens length must match, if the union is empty, so is any children + || target.null_count() == target.len() || target.data_type().is_null() + // case 1.3: if all values of the target children are null, regardless of selected type ids, the result will also be completely null + { + Ok(ColumnarValue::Array(Arc::clone(target))) + } else { + match eq_scalar(union_array.type_ids(), target_type_id) { + // case 2: all type ids equals our target, and since unions doesn't have a null mask, the result array is exactly the same as our target + BoolValue::Scalar(true) => Ok(ColumnarValue::Array(Arc::clone(target))), + // case 3: none type_id matches our target, the result is a null array + BoolValue::Scalar(false) => { + if layout(target.data_type()).can_contain_null_mask { + // case 3.1: target array can contain a null mask + //SAFETY: The only change to the array data is the addition of a null mask, and if the target data type can contain a null mask was just checked above + let data = unsafe { + target + .into_data() + .into_builder() + .nulls(Some(NullBuffer::new_null(target.len()))) + .build_unchecked() + }; + + Ok(ColumnarValue::Array(make_array(data))) + } else { + // case 3.2: target can't contain a null mask + Ok(new_null_columnar_value(target.data_type(), target.len())) + } + } + // case 4: some but not all type_id matches our target + BoolValue::Buffer(selected) => { + if layout(target.data_type()).can_contain_null_mask { + // case 4.1: target array can contain a null mask + let nulls = match target.nulls().filter(|n| n.null_count() > 0) { + // case 4.1.1: our target child has nulls and types other than our target are selected, union the masks + // the case where n.null_count() == n.len() is cheaply handled at case 1.3 + Some(nulls) => &selected & nulls.inner(), + // case 4.1.2: target child has no nulls, but types other than our target are selected, use the selected mask as a null mask + None => selected, + }; + + //SAFETY: The only change to the array data is the addition of a null mask, and if the target data type can contain a null mask was just checked above + let data = unsafe { + assert_eq!(nulls.len(), target.len()); + + target + .into_data() + .into_builder() + .nulls(Some(nulls.into())) + .build_unchecked() + }; + + Ok(ColumnarValue::Array(make_array(data))) + } else { + // case 4.2: target can't containt a null mask, zip the values that match with a null value + Ok(ColumnarValue::Array(arrow::compute::kernels::zip::zip( + &BooleanArray::new(selected, None), + target, + &Scalar::new(new_null_array(target.data_type(), 1)), + )?)) + } + } + } + } +} + +fn extract_dense( + union_array: &UnionArray, + fields: &UnionFields, + target_type_id: i8, +) -> Result<ColumnarValue> { + let target = union_array.child(target_type_id); + let offsets = union_array.offsets().unwrap(); + + if union_array.is_empty() { + // case 1: the union is empty + if target.is_empty() { + // case 1.1: the target is also empty, do a cheap Arc::clone instead of allocating a new empty array + Ok(ColumnarValue::Array(Arc::clone(target))) + } else { + // case 1.2: the target is not empty, allocate a new empty array + Ok(ColumnarValue::Array(new_empty_array(target.data_type()))) + } + } else if target.is_empty() { + // case 2: the union is not empty but the target is, which implies that none type_id points to it. The result is a null array + Ok(new_null_columnar_value( + target.data_type(), + union_array.len(), + )) + } else if target.null_count() == target.len() || target.data_type().is_null() { + // case 3: since all values on our target are null, regardless of selected type ids and offsets, the result is a null array + match target.len().cmp(&union_array.len()) { + // case 3.1: since the target is smaller than the union, allocate a new correclty sized null array + Ordering::Less => Ok(new_null_columnar_value( + target.data_type(), + union_array.len(), + )), + // case 3.2: target equals the union len, return it direcly + Ordering::Equal => Ok(ColumnarValue::Array(Arc::clone(target))), + // case 3.3: target len is bigger than the union len, slice it + Ordering::Greater => { + Ok(ColumnarValue::Array(target.slice(0, union_array.len()))) + } + } + } else if fields.len() == 1 // case A: since there's a single field, our target, every type id must matches our target + || fields + .iter() + .filter(|(field_type_id, _)| *field_type_id != target_type_id) + .all(|(sibling_type_id, _)| union_array.child(sibling_type_id).is_empty()) + // case B: since siblings are empty, every type id must matches our target + { + // case 4: every type id matches our target + Ok(ColumnarValue::Array(extract_dense_all_selected( + union_array, + target, + offsets, + )?)) + } else { + match eq_scalar(union_array.type_ids(), target_type_id) { + // case 4C: all type ids matches our target. + // Non empty sibling without any selected value may happen after slicing the parent union, + // since only type_ids and offsets are sliced, not the children + BoolValue::Scalar(true) => Ok(ColumnarValue::Array( + extract_dense_all_selected(union_array, target, offsets)?, + )), + BoolValue::Scalar(false) => { + // case 5: none type_id matches our target, so the result array will be completely null + // Non empty target without any selected value may happen after slicing the parent union, + // since only type_ids and offsets are sliced, not the children + match (target.len().cmp(&union_array.len()), layout(target.data_type()).can_contain_null_mask) { + (Ordering::Less, _) // case 5.1A: our target is smaller than the parent union, allocate a new correclty sized null array + | (_, false) => { // case 5.1B: target array can't contain a null mask + Ok(new_null_columnar_value(target.data_type(), union_array.len())) + } + // case 5.2: target and parent union lengths are equal, and the target can contain a null mask, let's set it to a all-null null-buffer + (Ordering::Equal, true) => { + //SAFETY: The only change to the array data is the addition of a null mask, and if the target data type can contain a null mask was just checked above + let data = unsafe { + target + .into_data() + .into_builder() + .nulls(Some(NullBuffer::new_null(union_array.len()))) + .build_unchecked() + }; + + Ok(ColumnarValue::Array(make_array(data))) + } + // case 5.3: target is bigger than it's parent union and can contain a null mask, let's slice it, and set it's nulls to a all-null null-buffer + (Ordering::Greater, true) => { + //SAFETY: The only change to the array data is the addition of a null mask, and if the target data type can contain a null mask was just checked above + let data = unsafe { + target + .into_data() + .slice(0, union_array.len()) + .into_builder() + .nulls(Some(NullBuffer::new_null(union_array.len()))) + .build_unchecked() + }; + + Ok(ColumnarValue::Array(make_array(data))) + } + } + } + BoolValue::Buffer(selected) => { + //case 6: some type_ids matches our target, but not all. For selected values, take the value pointed by the offset. For unselected, take a valid null + Ok(ColumnarValue::Array(take( + target, + &Int32Array::new(offsets.clone(), Some(selected.into())), + None, + )?)) + } + } + } +} + +fn extract_dense_all_selected( + union_array: &UnionArray, + target: &Arc<dyn Array>, + offsets: &ScalarBuffer<i32>, +) -> Result<ArrayRef> { + let sequential = + target.len() - offsets[0] as usize >= union_array.len() && is_sequential(offsets); + + if sequential && target.len() == union_array.len() { + // case 1: all offsets are sequential and both lengths match, return the array directly + Ok(Arc::clone(target)) + } else if sequential && target.len() > union_array.len() { + // case 2: All offsets are sequential, but our target is bigger than our union, slice it, starting at the first offset + Ok(target.slice(offsets[0] as usize, union_array.len())) + } else { + // case 3: Since offsets are not sequential, take them from the child to a new sequential and correcly sized array + let indices = Int32Array::try_new(offsets.clone(), None)?; + + Ok(take(target, &indices, None)?) + } +} + +const EQ_SCALAR_CHUNK_SIZE: usize = 512; + Review Comment: Those `pub` items with `#[doc(hidden)] ` are there to be benchmarked, in case any reviewer wants to, but maybe it can be removed before merge. 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