perf: optimize array_distinct with batched row conversion (#20364)

## Which issue does this PR close?

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- Closes #.

## Rationale for this change

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This PR optimizes the `array_distinct` function by batching value
conversions and utilizing a `HashSet` for deduplication.
It is a follow-up to #20243.

## What changes are included in this PR?

This PR optimizes `array_distinct` by:
1. Converting all values to rows in a single batch rather than
individually.
2. Using a HashSet to deduplicate values for each list.

### Benchmark
```
group                                main                                   optimized
-----                                ----                                   ---------
array_distinct/high_duplicate/10     2.66   855.1±28.18µs        ? ?/sec    1.00    321.9±8.70µs        ? ?/sec
array_distinct/high_duplicate/100    2.21      6.4±0.13ms        ? ?/sec    1.00      2.9±0.09ms        ? ?/sec
array_distinct/high_duplicate/50     2.14      3.2±0.05ms        ? ?/sec    1.00  1478.3±41.90µs        ? ?/sec
array_distinct/low_duplicate/10      2.73  1017.3±44.67µs        ? ?/sec    1.00   372.5±17.33µs        ? ?/sec
array_distinct/low_duplicate/100     1.32      4.4±0.13ms        ? ?/sec    1.00      3.3±0.15ms        ? ?/sec
array_distinct/low_duplicate/50      1.55      2.6±0.06ms        ? ?/sec    1.00  1689.0±94.15µs        ? ?/sec
```

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## Are these changes tested?

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Yes, unit tests exist and pass.

## Are there any user-facing changes?

Yes, there is a slight change in the output order. This new behavior is
consistent with `array_union` and `array_intersect`, where the output
order is more intuitive as it preserves the original order of elements
in the array.

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---------

Co-authored-by: lyne7-sc <lilinfeng0310@gmail.com>

Author: lyne7-sc

datafusion/functions-nested/benches/array_set_ops.rs

@@ -23,7 +23,7 @@ use criterion::{
 };
 use datafusion_common::config::ConfigOptions;
 use datafusion_expr::{ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl};
-use datafusion_functions_nested::set_ops::{ArrayIntersect, ArrayUnion};
+use datafusion_functions_nested::set_ops::{ArrayDistinct, ArrayIntersect, ArrayUnion};
 use rand::SeedableRng;
 use rand::prelude::SliceRandom;
 use rand::rngs::StdRng;
@@ -38,6 +38,7 @@ const SEED: u64 = 42;
 fn criterion_benchmark(c: &mut Criterion) {
     bench_array_union(c);
     bench_array_intersect(c);
+    bench_array_distinct(c);
 }
 
 fn invoke_udf(udf: &impl ScalarUDFImpl, array1: &ArrayRef, array2: &ArrayRef) {
@@ -97,6 +98,48 @@ fn bench_array_intersect(c: &mut Criterion) {
     group.finish();
 }
 
+fn bench_array_distinct(c: &mut Criterion) {
+    let mut group = c.benchmark_group("array_distinct");
+    let udf = ArrayDistinct::new();
+
+    for (duplicate_label, duplicate_ratio) in
+        &[("high_duplicate", 0.8), ("low_duplicate", 0.2)]
+    {
+        for &array_size in ARRAY_SIZES {
+            let array =
+                create_array_with_duplicates(NUM_ROWS, array_size, *duplicate_ratio);
+            group.bench_with_input(
+                BenchmarkId::new(*duplicate_label, array_size),
+                &array_size,
+                |b, _| {
+                    b.iter(|| {
+                        black_box(
+                            udf.invoke_with_args(ScalarFunctionArgs {
+                                args: vec![ColumnarValue::Array(array.clone())],
+                                arg_fields: vec![
+                                    Field::new("arr", array.data_type().clone(), false)
+                                        .into(),
+                                ],
+                                number_rows: NUM_ROWS,
+                                return_field: Field::new(
+                                    "result",
+                                    array.data_type().clone(),
+                                    false,
+                                )
+                                .into(),
+                                config_options: Arc::new(ConfigOptions::default()),
+                            })
+                            .unwrap(),
+                        )
+                    })
+                },
+            );
+        }
+    }
+
+    group.finish();
+}
+
 fn create_arrays_with_overlap(
     num_rows: usize,
     array_size: usize,
@@ -164,5 +207,53 @@ fn create_arrays_with_overlap(
     (array1, array2)
 }
 
+fn create_array_with_duplicates(
+    num_rows: usize,
+    array_size: usize,
+    duplicate_ratio: f64,
+) -> ArrayRef {
+    assert!((0.0..=1.0).contains(&duplicate_ratio));
+    let unique_count = ((array_size as f64) * (1.0 - duplicate_ratio)).round() as usize;
+    let duplicate_count = array_size - unique_count;
+
+    let mut rng = StdRng::seed_from_u64(SEED);
+    let mut values = Vec::with_capacity(num_rows * array_size);
+
+    for row in 0..num_rows {
+        let base = (row as i64) * (array_size as i64) * 2;
+
+        // Add unique values first
+        for i in 0..unique_count {
+            values.push(base + i as i64);
+        }
+
+        // Fill the rest with duplicates randomly picked from the unique values
+        let mut unique_indices: Vec<i64> =
+            (0..unique_count).map(|i| base + i as i64).collect();
+        unique_indices.shuffle(&mut rng);
+
+        for i in 0..duplicate_count {
+            values.push(unique_indices[i % unique_count]);
+        }
+    }
+
+    let values = Int64Array::from(values);
+    let field = Arc::new(Field::new("item", DataType::Int64, true));
+
+    let offsets = (0..=num_rows)
+        .map(|i| (i * array_size) as i32)
+        .collect::<Vec<i32>>();
+
+    Arc::new(
+        ListArray::try_new(
+            field,
+            OffsetBuffer::new(offsets.into()),
+            Arc::new(values),
+            None,
+        )
+        .unwrap(),
+    )
+}
+
 criterion_group!(benches, criterion_benchmark);
 criterion_main!(benches);

datafusion/functions-nested/src/set_ops.rs

@@ -22,7 +22,6 @@ use arrow::array::{
     Array, ArrayRef, GenericListArray, OffsetSizeTrait, new_empty_array, new_null_array,
 };
 use arrow::buffer::{NullBuffer, OffsetBuffer};
-use arrow::compute;
 use arrow::datatypes::DataType::{LargeList, List, Null};
 use arrow::datatypes::{DataType, Field, FieldRef};
 use arrow::row::{RowConverter, SortField};
@@ -35,7 +34,6 @@ use datafusion_expr::{
     ColumnarValue, Documentation, ScalarUDFImpl, Signature, Volatility,
 };
 use datafusion_macros::user_doc;
-use itertools::Itertools;
 use std::any::Any;
 use std::collections::HashSet;
 use std::fmt::{Display, Formatter};
@@ -264,7 +262,7 @@ impl ScalarUDFImpl for ArrayIntersect {
     )
 )]
 #[derive(Debug, PartialEq, Eq, Hash)]
-pub(super) struct ArrayDistinct {
+pub struct ArrayDistinct {
     signature: Signature,
     aliases: Vec<String>,
 }
@@ -278,6 +276,12 @@ impl ArrayDistinct {
     }
 }
 
+impl Default for ArrayDistinct {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
 impl ScalarUDFImpl for ArrayDistinct {
     fn as_any(&self) -> &dyn Any {
         self
@@ -527,42 +531,52 @@ fn general_array_distinct<OffsetSize: OffsetSizeTrait>(
     if array.is_empty() {
         return Ok(Arc::new(array.clone()) as ArrayRef);
     }
+    let value_offsets = array.value_offsets();
     let dt = array.value_type();
-    let mut offsets = Vec::with_capacity(array.len());
+    let mut offsets = Vec::with_capacity(array.len() + 1);
     offsets.push(OffsetSize::usize_as(0));
-    let mut new_arrays = Vec::with_capacity(array.len());
-    let converter = RowConverter::new(vec![SortField::new(dt)])?;
-    // distinct for each list in ListArray
-    for arr in array.iter() {
-        let last_offset: OffsetSize = offsets.last().copied().unwrap();
-        let Some(arr) = arr else {
-            // Add same offset for null
+
+    // Convert all values to row format in a single batch for performance
+    let converter = RowConverter::new(vec![SortField::new(dt.clone())])?;
+    let rows = converter.convert_columns(&[Arc::clone(array.values())])?;
+    let mut final_rows = Vec::with_capacity(rows.num_rows());
+    let mut seen = HashSet::new();
+    for i in 0..array.len() {
+        let last_offset = *offsets.last().unwrap();
+
+        // Null list entries produce no output; just carry forward the offset.
+        if array.is_null(i) {
             offsets.push(last_offset);
             continue;
-        };
-        let values = converter.convert_columns(&[arr])?;
-        // sort elements in list and remove duplicates
-        let rows = values.iter().sorted().dedup().collect::<Vec<_>>();
-        offsets.push(last_offset + OffsetSize::usize_as(rows.len()));
-        let arrays = converter.convert_rows(rows)?;
-        let array = match arrays.first() {
-            Some(array) => Arc::clone(array),
-            None => {
-                return internal_err!("array_distinct: failed to get array from rows");
+        }
+
+        let start = value_offsets[i].as_usize();
+        let end = value_offsets[i + 1].as_usize();
+        seen.clear();
+        seen.reserve(end - start);
+
+        // Walk the sub-array and keep only the first occurrence of each value.
+        for idx in start..end {
+            let row = rows.row(idx);
+            if seen.insert(row) {
+                final_rows.push(row);
             }
-        };
-        new_arrays.push(array);
-    }
-    if new_arrays.is_empty() {
-        return Ok(Arc::new(array.clone()) as ArrayRef);
+        }
+        offsets.push(last_offset + OffsetSize::usize_as(seen.len()));
     }
-    let offsets = OffsetBuffer::new(offsets.into());
-    let new_arrays_ref = new_arrays.iter().map(|v| v.as_ref()).collect::<Vec<_>>();
-    let values = compute::concat(&new_arrays_ref)?;
+
+    // Convert all collected distinct rows back
+    let final_values = if final_rows.is_empty() {
+        new_empty_array(&dt)
+    } else {
+        let arrays = converter.convert_rows(final_rows)?;
+        Arc::clone(&arrays[0])
+    };
+
     Ok(Arc::new(GenericListArray::<OffsetSize>::try_new(
         Arc::clone(field),
-        offsets,
-        values,
+        OffsetBuffer::new(offsets.into()),
+        final_values,
         // Keep the list nulls
         array.nulls().cloned(),
     )?))

datafusion/sqllogictest/test_files/array.slt

@@ -6832,7 +6832,7 @@ from array_distinct_table_2D;
 ----
 [[1, 2], [3, 4], [5, 6]]
 [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
-[NULL, [5, 6]]
+[[5, 6], NULL]
 
 query ?
 select array_distinct(column1)
@@ -6864,7 +6864,7 @@ from array_distinct_table_2D_fixed;
 ----
 [[1, 2], [3, 4], [5, 6]]
 [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
-[NULL, [5, 6]]
+[[5, 6], NULL]
 
 query ???
 select array_intersect(column1, column2),