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union_array.rs
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union_array.rs
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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.
#![allow(clippy::enum_clike_unportable_variant)]
use crate::{make_array, Array, ArrayRef};
use arrow_buffer::bit_chunk_iterator::{BitChunkIterator, BitChunks};
use arrow_buffer::buffer::NullBuffer;
use arrow_buffer::{BooleanBuffer, MutableBuffer, ScalarBuffer};
use arrow_data::{ArrayData, ArrayDataBuilder};
use arrow_schema::{ArrowError, DataType, UnionFields, UnionMode};
/// Contains the `UnionArray` type.
///
use std::any::Any;
use std::collections::HashSet;
use std::sync::Arc;
/// An array of [values of varying types](https://arrow.apache.org/docs/format/Columnar.html#union-layout)
///
/// Each slot in a [UnionArray] can have a value chosen from a number
/// of types. Each of the possible types are named like the fields of
/// a [`StructArray`](crate::StructArray). A `UnionArray` can
/// have two possible memory layouts, "dense" or "sparse". For more
/// information on please see the
/// [specification](https://arrow.apache.org/docs/format/Columnar.html#union-layout).
///
/// [UnionBuilder](crate::builder::UnionBuilder) can be used to
/// create [UnionArray]'s of primitive types. `UnionArray`'s of nested
/// types are also supported but not via `UnionBuilder`, see the tests
/// for examples.
///
/// # Examples
/// ## Create a dense UnionArray `[1, 3.2, 34]`
/// ```
/// use arrow_buffer::ScalarBuffer;
/// use arrow_schema::*;
/// use std::sync::Arc;
/// use arrow_array::{Array, Int32Array, Float64Array, UnionArray};
///
/// let int_array = Int32Array::from(vec![1, 34]);
/// let float_array = Float64Array::from(vec![3.2]);
/// let type_ids = [0, 1, 0].into_iter().collect::<ScalarBuffer<i8>>();
/// let offsets = [0, 0, 1].into_iter().collect::<ScalarBuffer<i32>>();
///
/// let union_fields = [
/// (0, Arc::new(Field::new("A", DataType::Int32, false))),
/// (1, Arc::new(Field::new("B", DataType::Float64, false))),
/// ].into_iter().collect::<UnionFields>();
///
/// let children = vec![
/// Arc::new(int_array) as Arc<dyn Array>,
/// Arc::new(float_array),
/// ];
///
/// let array = UnionArray::try_new(
/// union_fields,
/// type_ids,
/// Some(offsets),
/// children,
/// ).unwrap();
///
/// let value = array.value(0).as_any().downcast_ref::<Int32Array>().unwrap().value(0);
/// assert_eq!(1, value);
///
/// let value = array.value(1).as_any().downcast_ref::<Float64Array>().unwrap().value(0);
/// assert!(3.2 - value < f64::EPSILON);
///
/// let value = array.value(2).as_any().downcast_ref::<Int32Array>().unwrap().value(0);
/// assert_eq!(34, value);
/// ```
///
/// ## Create a sparse UnionArray `[1, 3.2, 34]`
/// ```
/// use arrow_buffer::ScalarBuffer;
/// use arrow_schema::*;
/// use std::sync::Arc;
/// use arrow_array::{Array, Int32Array, Float64Array, UnionArray};
///
/// let int_array = Int32Array::from(vec![Some(1), None, Some(34)]);
/// let float_array = Float64Array::from(vec![None, Some(3.2), None]);
/// let type_ids = [0_i8, 1, 0].into_iter().collect::<ScalarBuffer<i8>>();
///
/// let union_fields = [
/// (0, Arc::new(Field::new("A", DataType::Int32, false))),
/// (1, Arc::new(Field::new("B", DataType::Float64, false))),
/// ].into_iter().collect::<UnionFields>();
///
/// let children = vec![
/// Arc::new(int_array) as Arc<dyn Array>,
/// Arc::new(float_array),
/// ];
///
/// let array = UnionArray::try_new(
/// union_fields,
/// type_ids,
/// None,
/// children,
/// ).unwrap();
///
/// let value = array.value(0).as_any().downcast_ref::<Int32Array>().unwrap().value(0);
/// assert_eq!(1, value);
///
/// let value = array.value(1).as_any().downcast_ref::<Float64Array>().unwrap().value(0);
/// assert!(3.2 - value < f64::EPSILON);
///
/// let value = array.value(2).as_any().downcast_ref::<Int32Array>().unwrap().value(0);
/// assert_eq!(34, value);
/// ```
#[derive(Clone)]
pub struct UnionArray {
data_type: DataType,
type_ids: ScalarBuffer<i8>,
offsets: Option<ScalarBuffer<i32>>,
fields: Vec<Option<ArrayRef>>,
}
impl UnionArray {
/// Creates a new `UnionArray`.
///
/// Accepts type ids, child arrays and optionally offsets (for dense unions) to create
/// a new `UnionArray`. This method makes no attempt to validate the data provided by the
/// caller and assumes that each of the components are correct and consistent with each other.
/// See `try_new` for an alternative that validates the data provided.
///
/// # Safety
///
/// The `type_ids` values should be positive and must match one of the type ids of the fields provided in `fields`.
/// These values are used to index into the `children` arrays.
///
/// The `offsets` is provided in the case of a dense union, sparse unions should use `None`.
/// If provided the `offsets` values should be positive and must be less than the length of the
/// corresponding array.
///
/// In both cases above we use signed integer types to maintain compatibility with other
/// Arrow implementations.
pub unsafe fn new_unchecked(
fields: UnionFields,
type_ids: ScalarBuffer<i8>,
offsets: Option<ScalarBuffer<i32>>,
children: Vec<ArrayRef>,
) -> Self {
let mode = if offsets.is_some() {
UnionMode::Dense
} else {
UnionMode::Sparse
};
let len = type_ids.len();
let builder = ArrayData::builder(DataType::Union(fields, mode))
.add_buffer(type_ids.into_inner())
.child_data(children.into_iter().map(Array::into_data).collect())
.len(len);
let data = match offsets {
Some(offsets) => builder.add_buffer(offsets.into_inner()).build_unchecked(),
None => builder.build_unchecked(),
};
Self::from(data)
}
/// Attempts to create a new `UnionArray`, validating the inputs provided.
///
/// The order of child arrays child array order must match the fields order
pub fn try_new(
fields: UnionFields,
type_ids: ScalarBuffer<i8>,
offsets: Option<ScalarBuffer<i32>>,
children: Vec<ArrayRef>,
) -> Result<Self, ArrowError> {
// There must be a child array for every field.
if fields.len() != children.len() {
return Err(ArrowError::InvalidArgumentError(
"Union fields length must match child arrays length".to_string(),
));
}
if let Some(offsets) = &offsets {
// There must be an offset value for every type id value.
if offsets.len() != type_ids.len() {
return Err(ArrowError::InvalidArgumentError(
"Type Ids and Offsets lengths must match".to_string(),
));
}
} else {
// Sparse union child arrays must be equal in length to the length of the union
for child in &children {
if child.len() != type_ids.len() {
return Err(ArrowError::InvalidArgumentError(
"Sparse union child arrays must be equal in length to the length of the union".to_string(),
));
}
}
}
// Create mapping from type id to array lengths.
let max_id = fields.iter().map(|(i, _)| i).max().unwrap_or_default() as usize;
let mut array_lens = vec![i32::MIN; max_id + 1];
for (cd, (field_id, _)) in children.iter().zip(fields.iter()) {
array_lens[field_id as usize] = cd.len() as i32;
}
// Type id values must match one of the fields.
for id in &type_ids {
match array_lens.get(*id as usize) {
Some(x) if *x != i32::MIN => {}
_ => {
return Err(ArrowError::InvalidArgumentError(
"Type Ids values must match one of the field type ids".to_owned(),
))
}
}
}
// Check the value offsets are in bounds.
if let Some(offsets) = &offsets {
let mut iter = type_ids.iter().zip(offsets.iter());
if iter.any(|(type_id, &offset)| offset < 0 || offset >= array_lens[*type_id as usize])
{
return Err(ArrowError::InvalidArgumentError(
"Offsets must be positive and within the length of the Array".to_owned(),
));
}
}
// Safety:
// - Arguments validated above.
let union_array = unsafe { Self::new_unchecked(fields, type_ids, offsets, children) };
Ok(union_array)
}
/// Accesses the child array for `type_id`.
///
/// # Panics
///
/// Panics if the `type_id` provided is not present in the array's DataType
/// in the `Union`.
pub fn child(&self, type_id: i8) -> &ArrayRef {
assert!((type_id as usize) < self.fields.len());
let boxed = &self.fields[type_id as usize];
boxed.as_ref().expect("invalid type id")
}
/// Returns the `type_id` for the array slot at `index`.
///
/// # Panics
///
/// Panics if `index` is greater than or equal to the number of child arrays
pub fn type_id(&self, index: usize) -> i8 {
assert!(index < self.type_ids.len());
self.type_ids[index]
}
/// Returns the `type_ids` buffer for this array
pub fn type_ids(&self) -> &ScalarBuffer<i8> {
&self.type_ids
}
/// Returns the `offsets` buffer if this is a dense array
pub fn offsets(&self) -> Option<&ScalarBuffer<i32>> {
self.offsets.as_ref()
}
/// Returns the offset into the underlying values array for the array slot at `index`.
///
/// # Panics
///
/// Panics if `index` is greater than or equal the length of the array.
pub fn value_offset(&self, index: usize) -> usize {
assert!(index < self.len());
match &self.offsets {
Some(offsets) => offsets[index] as usize,
None => self.offset() + index,
}
}
/// Returns the array's value at index `i`.
/// # Panics
/// Panics if index `i` is out of bounds
pub fn value(&self, i: usize) -> ArrayRef {
let type_id = self.type_id(i);
let value_offset = self.value_offset(i);
let child = self.child(type_id);
child.slice(value_offset, 1)
}
/// Returns the names of the types in the union.
pub fn type_names(&self) -> Vec<&str> {
match self.data_type() {
DataType::Union(fields, _) => fields
.iter()
.map(|(_, f)| f.name().as_str())
.collect::<Vec<&str>>(),
_ => unreachable!("Union array's data type is not a union!"),
}
}
/// Returns whether the `UnionArray` is dense (or sparse if `false`).
fn is_dense(&self) -> bool {
match self.data_type() {
DataType::Union(_, mode) => mode == &UnionMode::Dense,
_ => unreachable!("Union array's data type is not a union!"),
}
}
/// Returns a zero-copy slice of this array with the indicated offset and length.
pub fn slice(&self, offset: usize, length: usize) -> Self {
let (offsets, fields) = match self.offsets.as_ref() {
// If dense union, slice offsets
Some(offsets) => (Some(offsets.slice(offset, length)), self.fields.clone()),
// Otherwise need to slice sparse children
None => {
let fields = self
.fields
.iter()
.map(|x| x.as_ref().map(|x| x.slice(offset, length)))
.collect();
(None, fields)
}
};
Self {
data_type: self.data_type.clone(),
type_ids: self.type_ids.slice(offset, length),
offsets,
fields,
}
}
/// Deconstruct this array into its constituent parts
///
/// # Example
///
/// ```
/// # use arrow_array::array::UnionArray;
/// # use arrow_array::types::Int32Type;
/// # use arrow_array::builder::UnionBuilder;
/// # use arrow_buffer::ScalarBuffer;
/// # fn main() -> Result<(), arrow_schema::ArrowError> {
/// let mut builder = UnionBuilder::new_dense();
/// builder.append::<Int32Type>("a", 1).unwrap();
/// let union_array = builder.build()?;
///
/// // Deconstruct into parts
/// let (union_fields, type_ids, offsets, children) = union_array.into_parts();
///
/// // Reconstruct from parts
/// let union_array = UnionArray::try_new(
/// union_fields,
/// type_ids,
/// offsets,
/// children,
/// );
/// # Ok(())
/// # }
/// ```
#[allow(clippy::type_complexity)]
pub fn into_parts(
self,
) -> (
UnionFields,
ScalarBuffer<i8>,
Option<ScalarBuffer<i32>>,
Vec<ArrayRef>,
) {
let Self {
data_type,
type_ids,
offsets,
mut fields,
} = self;
match data_type {
DataType::Union(union_fields, _) => {
let children = union_fields
.iter()
.map(|(type_id, _)| fields[type_id as usize].take().unwrap())
.collect();
(union_fields, type_ids, offsets, children)
}
_ => unreachable!(),
}
}
/// Computes the logical nulls for a sparse union, optimized for when there's a lot of fields without nulls
fn mask_sparse_skip_without_nulls(&self, nulls: Vec<(i8, NullBuffer)>) -> BooleanBuffer {
// Example logic for a union with 5 fields, a, b & c with nulls, d & e without nulls:
// let [a_nulls, b_nulls, c_nulls] = nulls;
// let [is_a, is_b, is_c] = masks;
// let is_d_or_e = !(is_a | is_b)
// let union_chunk_nulls = is_d_or_e | (is_b & b_nulls) | (is_c & c_nulls)
let fold = |(any_nullable_selected, union_nulls), (is_field, field_nulls)| {
(
any_nullable_selected | is_field,
union_nulls | (is_field & field_nulls),
)
};
self.mask_sparse_helper(
nulls,
|chunk, nulls_masks_iters| {
let (any_nullable_selected, union_nulls) = nulls_masks_iters
.iter_mut()
.map(|(field_type_id, field_nulls)| {
let field_nulls = field_nulls.next().unwrap();
let is_field = selection_mask(chunk, *field_type_id);
(is_field, field_nulls)
})
.fold((0, 0), fold);
!any_nullable_selected | union_nulls
},
|remainder, bit_chunks| {
let (any_nullable_selected, union_nulls) = bit_chunks
.iter()
.map(|(field_type_id, field_bit_chunks)| {
let field_nulls = field_bit_chunks.remainder_bits();
let is_field = selection_mask(remainder, *field_type_id);
(is_field, field_nulls)
})
.fold((0, 0), fold);
!any_nullable_selected | union_nulls
},
)
}
/// Computes the logical nulls for a sparse union, optimized for when there's a lot of fields fully null
fn mask_sparse_skip_fully_null(&self, mut nulls: Vec<(i8, NullBuffer)>) -> BooleanBuffer {
let fields = match self.data_type() {
DataType::Union(fields, _) => fields,
_ => unreachable!("Union array's data type is not a union!"),
};
let type_ids = fields.iter().map(|(id, _)| id).collect::<HashSet<_>>();
let with_nulls = nulls.iter().map(|(id, _)| *id).collect::<HashSet<_>>();
let without_nulls_ids = type_ids
.difference(&with_nulls)
.copied()
.collect::<Vec<_>>();
nulls.retain(|(_, nulls)| nulls.null_count() < nulls.len());
// Example logic for a union with 6 fields, a, b & c with nulls, d & e without nulls, and f fully_null:
// let [a_nulls, b_nulls, c_nulls] = nulls;
// let [is_a, is_b, is_c, is_d, is_e] = masks;
// let union_chunk_nulls = is_d | is_e | (is_a & a_nulls) | (is_b & b_nulls) | (is_c & c_nulls)
self.mask_sparse_helper(
nulls,
|chunk, nulls_masks_iters| {
let union_nulls = nulls_masks_iters.iter_mut().fold(
0,
|union_nulls, (field_type_id, nulls_iter)| {
let field_nulls = nulls_iter.next().unwrap();
if field_nulls == 0 {
union_nulls
} else {
let is_field = selection_mask(chunk, *field_type_id);
union_nulls | (is_field & field_nulls)
}
},
);
union_nulls | without_nulls_selected(chunk, &without_nulls_ids)
},
|remainder, bit_chunks| {
let union_nulls =
bit_chunks
.iter()
.fold(0, |union_nulls, (field_type_id, field_bit_chunks)| {
let is_field = selection_mask(remainder, *field_type_id);
let field_nulls = field_bit_chunks.remainder_bits();
union_nulls | is_field & field_nulls
});
union_nulls | without_nulls_selected(remainder, &without_nulls_ids)
},
)
}
/// Computes the logical nulls for a sparse union, optimized for when all fields contains nulls
fn mask_sparse_all_with_nulls_skip_one(&self, nulls: Vec<(i8, NullBuffer)>) -> BooleanBuffer {
// Example logic for a union with 3 fields, a, b & c, all containing nulls:
// let [a_nulls, b_nulls, c_nulls] = nulls;
// We can skip the first field: it's selection mask is the negation of all others selection mask
// let [is_b, is_c] = selection_masks;
// let is_a = !(is_b | is_c)
// let union_chunk_nulls = (is_a & a_nulls) | (is_b & b_nulls) | (is_c & c_nulls)
self.mask_sparse_helper(
nulls,
|chunk, nulls_masks_iters| {
let (is_not_first, union_nulls) = nulls_masks_iters[1..] // skip first
.iter_mut()
.fold(
(0, 0),
|(is_not_first, union_nulls), (field_type_id, nulls_iter)| {
let field_nulls = nulls_iter.next().unwrap();
let is_field = selection_mask(chunk, *field_type_id);
(
is_not_first | is_field,
union_nulls | (is_field & field_nulls),
)
},
);
let is_first = !is_not_first;
let first_nulls = nulls_masks_iters[0].1.next().unwrap();
(is_first & first_nulls) | union_nulls
},
|remainder, bit_chunks| {
bit_chunks
.iter()
.fold(0, |union_nulls, (field_type_id, field_bit_chunks)| {
let field_nulls = field_bit_chunks.remainder_bits();
// The same logic as above, except that since this runs at most once,
// it doesn't make difference to speed-up the first selection mask
let is_field = selection_mask(remainder, *field_type_id);
union_nulls | (is_field & field_nulls)
})
},
)
}
/// Maps `nulls` to `BitChunk's` and then to `BitChunkIterator's`, then divides `self.type_ids` into exact chunks of 64 values,
/// calling `mask_chunk` for every exact chunk, and `mask_remainder` for the remainder, if any, collecting the result in a `BooleanBuffer`
fn mask_sparse_helper(
&self,
nulls: Vec<(i8, NullBuffer)>,
mut mask_chunk: impl FnMut(&[i8; 64], &mut [(i8, BitChunkIterator)]) -> u64,
mask_remainder: impl FnOnce(&[i8], &[(i8, BitChunks)]) -> u64,
) -> BooleanBuffer {
let bit_chunks = nulls
.iter()
.map(|(type_id, nulls)| (*type_id, nulls.inner().bit_chunks()))
.collect::<Vec<_>>();
let mut nulls_masks_iter = bit_chunks
.iter()
.map(|(type_id, bit_chunks)| (*type_id, bit_chunks.iter()))
.collect::<Vec<_>>();
let chunks_exact = self.type_ids.chunks_exact(64);
let remainder = chunks_exact.remainder();
let chunks = chunks_exact.map(|chunk| {
let chunk_array = <&[i8; 64]>::try_from(chunk).unwrap();
mask_chunk(chunk_array, &mut nulls_masks_iter)
});
// SAFETY:
// chunks is a ChunksExact iterator, which implements TrustedLen, and correctly reports its length
let mut buffer = unsafe { MutableBuffer::from_trusted_len_iter(chunks) };
if !remainder.is_empty() {
buffer.push(mask_remainder(remainder, &bit_chunks));
}
BooleanBuffer::new(buffer.into(), 0, self.type_ids.len())
}
/// Computes the logical nulls for a sparse or dense union, by gathering individual bits from the null buffer of the selected field
fn gather_nulls(&self, nulls: Vec<(i8, NullBuffer)>) -> BooleanBuffer {
let one_null = NullBuffer::new_null(1);
let one_valid = NullBuffer::new_valid(1);
// Unsafe code below depend on it:
// To remove one branch from the loop, if the a type_id is not utilized, or it's logical_nulls is None/all set,
// we use a null buffer of len 1 and a index_mask of 0, or the true null buffer and usize::MAX otherwise.
// We then unconditionally access the null buffer with index & index_mask,
// which always return 0 for the 1-len buffer, or the true index unchanged otherwise
// We also use a 256 array, so llvm knows that `type_id as u8 as usize` is always in bounds
let mut logical_nulls_array = [(&one_valid, Mask::Zero); 256];
for (type_id, nulls) in &nulls {
if nulls.null_count() == nulls.len() {
// Similarly, if all values are null, use a 1-null null-buffer to reduce cache pressure a bit
logical_nulls_array[*type_id as u8 as usize] = (&one_null, Mask::Zero);
} else {
logical_nulls_array[*type_id as u8 as usize] = (nulls, Mask::Max);
}
}
match &self.offsets {
Some(offsets) => {
assert_eq!(self.type_ids.len(), offsets.len());
BooleanBuffer::collect_bool(self.type_ids.len(), |i| unsafe {
// SAFETY: BooleanBuffer::collect_bool calls us 0..self.type_ids.len()
let type_id = *self.type_ids.get_unchecked(i);
// SAFETY: We asserted that offsets len and self.type_ids len are equal
let offset = *offsets.get_unchecked(i);
let (nulls, offset_mask) = &logical_nulls_array[type_id as u8 as usize];
// SAFETY:
// If offset_mask is Max
// 1. Offset validity is checked at union creation
// 2. If the null buffer len equals it's array len is checked at array creation
// If offset_mask is Zero, the null buffer len is 1
nulls
.inner()
.value_unchecked(offset as usize & *offset_mask as usize)
})
}
None => {
BooleanBuffer::collect_bool(self.type_ids.len(), |index| unsafe {
// SAFETY: BooleanBuffer::collect_bool calls us 0..self.type_ids.len()
let type_id = *self.type_ids.get_unchecked(index);
let (nulls, index_mask) = &logical_nulls_array[type_id as u8 as usize];
// SAFETY:
// If index_mask is Max
// 1. On sparse union, every child len match it's parent, this is checked at union creation
// 2. If the null buffer len equals it's array len is checked at array creation
// If index_mask is Zero, the null buffer len is 1
nulls.inner().value_unchecked(index & *index_mask as usize)
})
}
}
}
}
impl From<ArrayData> for UnionArray {
fn from(data: ArrayData) -> Self {
let (fields, mode) = match data.data_type() {
DataType::Union(fields, mode) => (fields, *mode),
d => panic!("UnionArray expected ArrayData with type Union got {d}"),
};
let (type_ids, offsets) = match mode {
UnionMode::Sparse => (
ScalarBuffer::new(data.buffers()[0].clone(), data.offset(), data.len()),
None,
),
UnionMode::Dense => (
ScalarBuffer::new(data.buffers()[0].clone(), data.offset(), data.len()),
Some(ScalarBuffer::new(
data.buffers()[1].clone(),
data.offset(),
data.len(),
)),
),
};
let max_id = fields.iter().map(|(i, _)| i).max().unwrap_or_default() as usize;
let mut boxed_fields = vec![None; max_id + 1];
for (cd, (field_id, _)) in data.child_data().iter().zip(fields.iter()) {
boxed_fields[field_id as usize] = Some(make_array(cd.clone()));
}
Self {
data_type: data.data_type().clone(),
type_ids,
offsets,
fields: boxed_fields,
}
}
}
impl From<UnionArray> for ArrayData {
fn from(array: UnionArray) -> Self {
let len = array.len();
let f = match &array.data_type {
DataType::Union(f, _) => f,
_ => unreachable!(),
};
let buffers = match array.offsets {
Some(o) => vec![array.type_ids.into_inner(), o.into_inner()],
None => vec![array.type_ids.into_inner()],
};
let child = f
.iter()
.map(|(i, _)| array.fields[i as usize].as_ref().unwrap().to_data())
.collect();
let builder = ArrayDataBuilder::new(array.data_type)
.len(len)
.buffers(buffers)
.child_data(child);
unsafe { builder.build_unchecked() }
}
}
impl Array for UnionArray {
fn as_any(&self) -> &dyn Any {
self
}
fn to_data(&self) -> ArrayData {
self.clone().into()
}
fn into_data(self) -> ArrayData {
self.into()
}
fn data_type(&self) -> &DataType {
&self.data_type
}
fn slice(&self, offset: usize, length: usize) -> ArrayRef {
Arc::new(self.slice(offset, length))
}
fn len(&self) -> usize {
self.type_ids.len()
}
fn is_empty(&self) -> bool {
self.type_ids.is_empty()
}
fn offset(&self) -> usize {
0
}
fn nulls(&self) -> Option<&NullBuffer> {
None
}
fn logical_nulls(&self) -> Option<NullBuffer> {
let fields = match self.data_type() {
DataType::Union(fields, _) => fields,
_ => unreachable!(),
};
if fields.len() <= 1 {
return self
.fields
.iter()
.flatten()
.map(Array::logical_nulls)
.next()
.flatten();
}
let logical_nulls = fields
.iter()
.filter_map(|(type_id, _)| Some((type_id, self.child(type_id).logical_nulls()?)))
.filter(|(_, nulls)| nulls.null_count() > 0)
.collect::<Vec<_>>();
if logical_nulls.is_empty() {
return None;
}
let fully_null_count = logical_nulls
.iter()
.filter(|(_, nulls)| nulls.null_count() == nulls.len())
.count();
if fully_null_count == fields.len() {
if let Some((_, exactly_sized)) = logical_nulls
.iter()
.find(|(_, nulls)| nulls.len() == self.len())
{
return Some(exactly_sized.clone());
}
if let Some((_, bigger)) = logical_nulls
.iter()
.find(|(_, nulls)| nulls.len() > self.len())
{
return Some(bigger.slice(0, self.len()));
}
return Some(NullBuffer::new_null(self.len()));
}
let boolean_buffer = match &self.offsets {
Some(_) => self.gather_nulls(logical_nulls),
None => {
enum SparseStrategy {
Gather,
AllNullsSkipOne,
MixedSkipWithoutNulls,
MixedSkipFullyNull,
}
// This is the threshold where masking becomes slower than gather
// TODO: test on avx512f(feature is still unstable)
let gather_relative_cost = if cfg!(target_feature = "avx2") {
10
} else if cfg!(target_feature = "sse4.1") {
3
} else {
2
};
// For masking strategies, the cost is the number of selection masks computed per chunk of 64 type ids
let strategies = [
(SparseStrategy::Gather, gather_relative_cost, true),
(
SparseStrategy::AllNullsSkipOne,
fields.len() - 1,
fields.len() == logical_nulls.len(),
),
(
SparseStrategy::MixedSkipWithoutNulls,
logical_nulls.len(),
true,
),
(
SparseStrategy::MixedSkipFullyNull,
fields.len() - fully_null_count,
true,
),
];
let (strategy, _, _) = strategies
.iter()
.filter(|(_, _, applicable)| *applicable)
.min_by_key(|(_, cost, _)| cost)
.unwrap();
match strategy {
SparseStrategy::Gather => self.gather_nulls(logical_nulls),
SparseStrategy::AllNullsSkipOne => {
self.mask_sparse_all_with_nulls_skip_one(logical_nulls)
}
SparseStrategy::MixedSkipWithoutNulls => {
self.mask_sparse_skip_without_nulls(logical_nulls)
}
SparseStrategy::MixedSkipFullyNull => {
self.mask_sparse_skip_fully_null(logical_nulls)
}
}
}
};
let null_buffer = NullBuffer::from(boolean_buffer);
if null_buffer.null_count() > 0 {
Some(null_buffer)
} else {
None
}
}
/// Union types always return non null as there is no validity buffer.
/// To check validity correctly you must check the logical nulls.
fn is_null(&self, _index: usize) -> bool {
false
}
/// Union types always return non null as there is no validity buffer.
/// To check validity correctly you must check the logical nulls.
fn is_valid(&self, _index: usize) -> bool {
true
}
/// Union types always return 0 null count as there is no validity buffer.
/// To get null count correctly you must check the logical nulls.
fn null_count(&self) -> usize {
0
}
fn get_buffer_memory_size(&self) -> usize {
let mut sum = self.type_ids.inner().capacity();
if let Some(o) = self.offsets.as_ref() {
sum += o.inner().capacity()
}
self.fields
.iter()
.flat_map(|x| x.as_ref().map(|x| x.get_buffer_memory_size()))
.sum::<usize>()
+ sum
}
fn get_array_memory_size(&self) -> usize {
let mut sum = self.type_ids.inner().capacity();
if let Some(o) = self.offsets.as_ref() {
sum += o.inner().capacity()
}
std::mem::size_of::<Self>()
+ self
.fields
.iter()
.flat_map(|x| x.as_ref().map(|x| x.get_array_memory_size()))
.sum::<usize>()
+ sum
}
}
impl std::fmt::Debug for UnionArray {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
let header = if self.is_dense() {
"UnionArray(Dense)\n["
} else {
"UnionArray(Sparse)\n["
};
writeln!(f, "{header}")?;
writeln!(f, "-- type id buffer:")?;
writeln!(f, "{:?}", self.type_ids)?;
if let Some(offsets) = &self.offsets {
writeln!(f, "-- offsets buffer:")?;
writeln!(f, "{:?}", offsets)?;
}
let fields = match self.data_type() {
DataType::Union(fields, _) => fields,
_ => unreachable!(),
};
for (type_id, field) in fields.iter() {
let child = self.child(type_id);
writeln!(
f,
"-- child {}: \"{}\" ({:?})",
type_id,
field.name(),
field.data_type()
)?;
std::fmt::Debug::fmt(child, f)?;
writeln!(f)?;
}
writeln!(f, "]")
}
}
#[derive(Copy, Clone)]
#[repr(usize)]
enum Mask {
Zero = 0,
// false positive, see https://github.com/rust-lang/rust-clippy/issues/8043
#[allow(clippy::enum_clike_unportable_variant)]
Max = usize::MAX,
}
fn selection_mask(chunk: &[i8], type_id: i8) -> u64 {
chunk
.iter()
.copied()
.enumerate()
.fold(0, |packed, (bit_idx, v)| {
packed | ((v == type_id) as u64) << bit_idx
})
}
fn without_nulls_selected(chunk: &[i8], without_nulls_ids: &[i8]) -> u64 {
without_nulls_ids
.iter()
.fold(0, |fully_valid_selected, field_type_id| {
fully_valid_selected | selection_mask(chunk, *field_type_id)
})
}
#[cfg(test)]
mod tests {
use super::*;
use std::collections::HashSet;
use crate::array::Int8Type;
use crate::builder::UnionBuilder;
use crate::cast::AsArray;
use crate::types::{Float32Type, Float64Type, Int32Type, Int64Type};
use crate::{Float64Array, Int32Array, Int64Array, StringArray};
use crate::{Int8Array, RecordBatch};
use arrow_buffer::Buffer;
use arrow_schema::{Field, Schema};
#[test]
fn test_dense_i32() {
let mut builder = UnionBuilder::new_dense();
builder.append::<Int32Type>("a", 1).unwrap();
builder.append::<Int32Type>("b", 2).unwrap();
builder.append::<Int32Type>("c", 3).unwrap();
builder.append::<Int32Type>("a", 4).unwrap();
builder.append::<Int32Type>("c", 5).unwrap();
builder.append::<Int32Type>("a", 6).unwrap();
builder.append::<Int32Type>("b", 7).unwrap();
let union = builder.build().unwrap();
let expected_type_ids = vec![0_i8, 1, 2, 0, 2, 0, 1];
let expected_offsets = vec![0_i32, 0, 0, 1, 1, 2, 1];
let expected_array_values = [1_i32, 2, 3, 4, 5, 6, 7];