feat: support arrays_zip function (#20440)

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- Closes #.

## Rationale for this change

Summary
- Adds a new arrays_zip scalar function that combines multiple arrays
into a single array of structs, where each struct field corresponds to
an input array
- Shorter arrays within a row are padded with NULLs to match the longest
array's length
- Compatible with Spark's arrays_zip behavior

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## What changes are included in this PR?

```

  arrays_zip takes N list arrays and produces a List<Struct<c0, c1, ..., cN>> where each struct contains the elements at the same index from each input array.

  > SELECT arrays_zip([1, 2, 3], ['a', 'b', 'c']);
  [{c0: 1, c1: a}, {c0: 2, c1: b}, {c0: 3, c1: c}]

  > SELECT arrays_zip([1, 2], [3, 4, 5]);
  [{c0: 1, c1: 3}, {c0: 2, c1: 4}, {c0: NULL, c1: 5}]

  Implementation details:
  - Implemented in set_ops.rs following existing array function patterns
  - Uses MutableArrayData builders per column with row-by-row processing for efficient memory handling
  - For each row, computes the max array length, copies values from each input array, and pads shorter arrays with NULLs
  - Supports variadic arguments (2 or more arrays)
  - Handles NULL list entries, NULL elements, empty arrays, mixed types, and Null-typed arguments
  - Registered as arrays_zip with alias list_zip
  - Uses Signature::variadic_any with Volatility::Immutable
```

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## Are there any user-facing changes?

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Author: comphead

datafusion/functions-nested/src/arrays_zip.rs

@@ -0,0 +1,336 @@
+// 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.
+
+//! [`ScalarUDFImpl`] definitions for arrays_zip function.
+
+use crate::utils::make_scalar_function;
+use arrow::array::{
+    Array, ArrayRef, Capacities, ListArray, MutableArrayData, StructArray, new_null_array,
+};
+use arrow::buffer::{NullBuffer, OffsetBuffer};
+use arrow::datatypes::DataType::{FixedSizeList, LargeList, List, Null};
+use arrow::datatypes::{DataType, Field, Fields};
+use datafusion_common::cast::{
+    as_fixed_size_list_array, as_large_list_array, as_list_array,
+};
+use datafusion_common::{Result, exec_err};
+use datafusion_expr::{
+    ColumnarValue, Documentation, ScalarUDFImpl, Signature, Volatility,
+};
+use datafusion_macros::user_doc;
+use std::any::Any;
+use std::sync::Arc;
+
+/// Type-erased view of a list column (works for both List and LargeList).
+/// Stores the information needed to iterate rows without re-downcasting.
+struct ListColumnView {
+    /// The flat values array backing this list column.
+    values: ArrayRef,
+    /// Pre-computed per-row start offsets (length = num_rows + 1).
+    offsets: Vec<usize>,
+    /// Pre-computed null bitmap: true means the row is null.
+    is_null: Vec<bool>,
+}
+
+make_udf_expr_and_func!(
+    ArraysZip,
+    arrays_zip,
+    "combines multiple arrays into a single array of structs.",
+    arrays_zip_udf
+);
+
+#[user_doc(
+    doc_section(label = "Array Functions"),
+    description = "Returns an array of structs created by combining the elements of each input array at the same index. If the arrays have different lengths, shorter arrays are padded with NULLs.",
+    syntax_example = "arrays_zip(array1, array2[, ..., array_n])",
+    sql_example = r#"```sql
+> select arrays_zip([1, 2, 3], ['a', 'b', 'c']);
++---------------------------------------------------+
+| arrays_zip([1, 2, 3], ['a', 'b', 'c'])             |
++---------------------------------------------------+
+| [{c0: 1, c1: a}, {c0: 2, c1: b}, {c0: 3, c1: c}] |
++---------------------------------------------------+
+> select arrays_zip([1, 2], [3, 4, 5]);
++---------------------------------------------------+
+| arrays_zip([1, 2], [3, 4, 5])                       |
++---------------------------------------------------+
+| [{c0: 1, c1: 3}, {c0: 2, c1: 4}, {c0: , c1: 5}]  |
++---------------------------------------------------+
+```"#,
+    argument(name = "array1", description = "First array expression."),
+    argument(name = "array2", description = "Second array expression."),
+    argument(name = "array_n", description = "Subsequent array expressions.")
+)]
+#[derive(Debug, PartialEq, Eq, Hash)]
+pub struct ArraysZip {
+    signature: Signature,
+    aliases: Vec<String>,
+}
+
+impl Default for ArraysZip {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl ArraysZip {
+    pub fn new() -> Self {
+        Self {
+            signature: Signature::variadic_any(Volatility::Immutable),
+            aliases: vec![String::from("list_zip")],
+        }
+    }
+}
+
+impl ScalarUDFImpl for ArraysZip {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn name(&self) -> &str {
+        "arrays_zip"
+    }
+
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+
+    fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
+        if arg_types.is_empty() {
+            return exec_err!("arrays_zip requires at least two arguments");
+        }
+
+        let mut fields = Vec::with_capacity(arg_types.len());
+        for (i, arg_type) in arg_types.iter().enumerate() {
+            let element_type = match arg_type {
+                List(field) | LargeList(field) | FixedSizeList(field, _) => {
+                    field.data_type().clone()
+                }
+                Null => Null,
+                dt => {
+                    return exec_err!("arrays_zip expects array arguments, got {dt}");
+                }
+            };
+            fields.push(Field::new(format!("c{i}"), element_type, true));
+        }
+
+        Ok(List(Arc::new(Field::new_list_field(
+            DataType::Struct(Fields::from(fields)),
+            true,
+        ))))
+    }
+
+    fn invoke_with_args(
+        &self,
+        args: datafusion_expr::ScalarFunctionArgs,
+    ) -> Result<ColumnarValue> {
+        make_scalar_function(arrays_zip_inner)(&args.args)
+    }
+
+    fn aliases(&self) -> &[String] {
+        &self.aliases
+    }
+
+    fn documentation(&self) -> Option<&Documentation> {
+        self.doc()
+    }
+}
+
+/// Core implementation for arrays_zip.
+///
+/// Takes N list arrays and produces a list of structs where each struct
+/// has one field per input array. If arrays within a row have different
+/// lengths, shorter arrays are padded with NULLs.
+/// Supports List, LargeList, and Null input types.
+fn arrays_zip_inner(args: &[ArrayRef]) -> Result<ArrayRef> {
+    if args.len() < 2 {
+        return exec_err!("arrays_zip requires at least two arguments");
+    }
+
+    let num_rows = args[0].len();
+
+    // Build a type-erased ListColumnView for each argument.
+    // None means the argument is Null-typed (all nulls, no backing data).
+    let mut views: Vec<Option<ListColumnView>> = Vec::with_capacity(args.len());
+    let mut element_types: Vec<DataType> = Vec::with_capacity(args.len());
+
+    for (i, arg) in args.iter().enumerate() {
+        match arg.data_type() {
+            List(field) => {
+                let arr = as_list_array(arg)?;
+                let raw_offsets = arr.value_offsets();
+                let offsets: Vec<usize> =
+                    raw_offsets.iter().map(|&o| o as usize).collect();
+                let is_null = (0..num_rows).map(|row| arr.is_null(row)).collect();
+                element_types.push(field.data_type().clone());
+                views.push(Some(ListColumnView {
+                    values: Arc::clone(arr.values()),
+                    offsets,
+                    is_null,
+                }));
+            }
+            LargeList(field) => {
+                let arr = as_large_list_array(arg)?;
+                let raw_offsets = arr.value_offsets();
+                let offsets: Vec<usize> =
+                    raw_offsets.iter().map(|&o| o as usize).collect();
+                let is_null = (0..num_rows).map(|row| arr.is_null(row)).collect();
+                element_types.push(field.data_type().clone());
+                views.push(Some(ListColumnView {
+                    values: Arc::clone(arr.values()),
+                    offsets,
+                    is_null,
+                }));
+            }
+            FixedSizeList(field, size) => {
+                let arr = as_fixed_size_list_array(arg)?;
+                let size = *size as usize;
+                let offsets: Vec<usize> = (0..=num_rows).map(|row| row * size).collect();
+                let is_null = (0..num_rows).map(|row| arr.is_null(row)).collect();
+                element_types.push(field.data_type().clone());
+                views.push(Some(ListColumnView {
+                    values: Arc::clone(arr.values()),
+                    offsets,
+                    is_null,
+                }));
+            }
+            Null => {
+                element_types.push(Null);
+                views.push(None);
+            }
+            dt => {
+                return exec_err!("arrays_zip argument {i} expected list type, got {dt}");
+            }
+        }
+    }
+
+    // Collect per-column values data for MutableArrayData builders.
+    let values_data: Vec<_> = views
+        .iter()
+        .map(|v| v.as_ref().map(|view| view.values.to_data()))
+        .collect();
+
+    let struct_fields: Fields = element_types
+        .iter()
+        .enumerate()
+        .map(|(i, dt)| Field::new(format!("c{i}"), dt.clone(), true))
+        .collect::<Vec<_>>()
+        .into();
+
+    // Create a MutableArrayData builder per column. For None (Null-typed)
+    // args we only need extend_nulls, so we track them separately.
+    let mut builders: Vec<Option<MutableArrayData>> = values_data
+        .iter()
+        .map(|vd| {
+            vd.as_ref().map(|data| {
+                MutableArrayData::with_capacities(vec![data], true, Capacities::Array(0))
+            })
+        })
+        .collect();
+
+    let mut offsets: Vec<i32> = Vec::with_capacity(num_rows + 1);
+    offsets.push(0);
+    let mut null_mask: Vec<bool> = Vec::with_capacity(num_rows);
+    let mut total_values: usize = 0;
+
+    // Process each row: compute per-array lengths, then copy values
+    // and pad shorter arrays with NULLs.
+    for row_idx in 0..num_rows {
+        let mut max_len: usize = 0;
+        let mut all_null = true;
+
+        for view in views.iter().flatten() {
+            if !view.is_null[row_idx] {
+                all_null = false;
+                let len = view.offsets[row_idx + 1] - view.offsets[row_idx];
+                max_len = max_len.max(len);
+            }
+        }
+
+        if all_null {
+            null_mask.push(true);
+            offsets.push(*offsets.last().unwrap());
+            continue;
+        }
+        null_mask.push(false);
+
+        // Extend each column builder for this row.
+        for (col_idx, view) in views.iter().enumerate() {
+            match view {
+                Some(v) if !v.is_null[row_idx] => {
+                    let start = v.offsets[row_idx];
+                    let end = v.offsets[row_idx + 1];
+                    let len = end - start;
+                    let builder = builders[col_idx].as_mut().unwrap();
+                    builder.extend(0, start, end);
+                    if len < max_len {
+                        builder.extend_nulls(max_len - len);
+                    }
+                }
+                _ => {
+                    // Null list entry or None (Null-typed) arg — all nulls.
+                    if let Some(builder) = builders[col_idx].as_mut() {
+                        builder.extend_nulls(max_len);
+                    }
+                }
+            }
+        }
+
+        total_values += max_len;
+        let last = *offsets.last().unwrap();
+        offsets.push(last + max_len as i32);
+    }
+
+    // Assemble struct columns from builders.
+    let struct_columns: Vec<ArrayRef> = builders
+        .into_iter()
+        .zip(element_types.iter())
+        .map(|(builder, elem_type)| match builder {
+            Some(b) => arrow::array::make_array(b.freeze()),
+            None => new_null_array(
+                if elem_type.is_null() {
+                    &Null
+                } else {
+                    elem_type
+                },
+                total_values,
+            ),
+        })
+        .collect();
+
+    let struct_array = StructArray::try_new(struct_fields, struct_columns, None)?;
+
+    let null_buffer = if null_mask.iter().any(|&v| v) {
+        Some(NullBuffer::from(
+            null_mask.iter().map(|v| !v).collect::<Vec<bool>>(),
+        ))
+    } else {
+        None
+    };
+
+    let result = ListArray::try_new(
+        Arc::new(Field::new_list_field(
+            struct_array.data_type().clone(),
+            true,
+        )),
+        OffsetBuffer::new(offsets.into()),
+        Arc::new(struct_array),
+        null_buffer,
+    )?;
+
+    Ok(Arc::new(result))
+}

datafusion/functions-nested/src/lib.rs

@@ -38,6 +38,7 @@
 pub mod macros;
 
 pub mod array_has;
+pub mod arrays_zip;
 pub mod cardinality;
 pub mod concat;
 pub mod dimension;
@@ -79,6 +80,7 @@ pub mod expr_fn {
     pub use super::array_has::array_has;
     pub use super::array_has::array_has_all;
     pub use super::array_has::array_has_any;
+    pub use super::arrays_zip::arrays_zip;
     pub use super::cardinality::cardinality;
     pub use super::concat::array_append;
     pub use super::concat::array_concat;
@@ -159,6 +161,7 @@ pub fn all_default_nested_functions() -> Vec<Arc<ScalarUDF>> {
         set_ops::array_distinct_udf(),
         set_ops::array_intersect_udf(),
         set_ops::array_union_udf(),
+        arrays_zip::arrays_zip_udf(),
         position::array_position_udf(),
         position::array_positions_udf(),
         remove::array_remove_udf(),