gstvg commented on code in PR #12116:
URL: https://github.com/apache/datafusion/pull/12116#discussion_r1755878566


##########
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:
   Removed on arrow-rs PR



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