test: add benchmark for struct column processing

Add a Criterion benchmark to measure the performance of struct column
processing in native shuffle. Tests various struct sizes (5, 10, 20 fields)
and row counts (1K, 10K rows).

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: andygrove

native/core/Cargo.toml

@@ -131,3 +131,7 @@ harness = false
 [[bench]]
 name = "parquet_decode"
 harness = false
+
+[[bench]]
+name = "struct_conversion"
+harness = false

native/core/benches/struct_conversion.rs

@@ -0,0 +1,174 @@
+// 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.
+
+//! Benchmark for struct column processing in native shuffle.
+//!
+//! This benchmark measures the performance of converting Spark UnsafeRow
+//! with struct columns to Arrow arrays.
+
+use arrow::datatypes::{DataType, Field, Fields};
+use comet::execution::shuffle::row::{
+    process_sorted_row_partition, SparkUnsafeObject, SparkUnsafeRow,
+};
+use comet::execution::shuffle::CompressionCodec;
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
+use std::sync::Arc;
+use tempfile::Builder;
+
+const BATCH_SIZE: usize = 5000;
+
+/// Create a struct schema with the given number of int64 fields.
+fn make_struct_schema(num_fields: usize) -> DataType {
+    let fields: Vec<Field> = (0..num_fields)
+        .map(|i| Field::new(format!("f{}", i), DataType::Int64, true))
+        .collect();
+    DataType::Struct(Fields::from(fields))
+}
+
+/// Calculate the row size for a struct with the given number of fields.
+/// UnsafeRow layout: [null bits] [fixed-length values]
+/// For struct: the struct value is stored as offset+size (8 bytes) pointing to nested row
+fn get_row_size(num_struct_fields: usize) -> usize {
+    // Top-level row has 1 column (the struct)
+    let top_level_bitset_width = SparkUnsafeRow::get_row_bitset_width(1);
+    // Struct pointer (offset + size) is 8 bytes
+    let struct_pointer_size = 8;
+    // Nested struct row
+    let nested_bitset_width = SparkUnsafeRow::get_row_bitset_width(num_struct_fields);
+    let nested_data_size = num_struct_fields * 8; // int64 values
+
+    top_level_bitset_width + struct_pointer_size + nested_bitset_width + nested_data_size
+}
+
+struct RowData {
+    data: Vec<u8>,
+}
+
+impl RowData {
+    fn new(num_struct_fields: usize) -> Self {
+        let row_size = get_row_size(num_struct_fields);
+        let mut data = vec![0u8; row_size];
+
+        // Top-level row layout:
+        // [null bits for 1 field] [struct pointer (offset, size)]
+        let top_level_bitset_width = SparkUnsafeRow::get_row_bitset_width(1);
+
+        // Nested struct starts after top-level row header + pointer
+        let nested_offset = top_level_bitset_width + 8;
+        let nested_bitset_width = SparkUnsafeRow::get_row_bitset_width(num_struct_fields);
+        let nested_size = nested_bitset_width + num_struct_fields * 8;
+
+        // Write struct pointer (offset in upper 32 bits, size in lower 32 bits)
+        let offset_and_size = ((nested_offset as i64) << 32) | (nested_size as i64);
+        data[top_level_bitset_width..top_level_bitset_width + 8]
+            .copy_from_slice(&offset_and_size.to_le_bytes());
+
+        // Fill nested struct with some data
+        for i in 0..num_struct_fields {
+            let value_offset = nested_offset + nested_bitset_width + i * 8;
+            let value = (i as i64) * 100;
+            data[value_offset..value_offset + 8].copy_from_slice(&value.to_le_bytes());
+        }
+
+        RowData { data }
+    }
+
+    fn to_spark_row(&self, spark_row: &mut SparkUnsafeRow) {
+        spark_row.point_to_slice(&self.data);
+    }
+}
+
+fn benchmark_struct_conversion(c: &mut Criterion) {
+    let mut group = c.benchmark_group("struct_conversion");
+
+    // Test with different struct sizes and row counts
+    for num_fields in [5, 10, 20] {
+        for num_rows in [1000, 10000] {
+            let schema = vec![make_struct_schema(num_fields)];
+
+            // Create row data
+            let rows: Vec<RowData> = (0..num_rows).map(|_| RowData::new(num_fields)).collect();
+
+            let mut spark_rows: Vec<SparkUnsafeRow> = rows
+                .iter()
+                .map(|row_data| {
+                    let mut spark_row = SparkUnsafeRow::new_with_num_fields(1);
+                    row_data.to_spark_row(&mut spark_row);
+                    // Mark the struct column as not null
+                    spark_row.set_not_null_at(0);
+                    spark_row
+                })
+                .collect();
+
+            let mut row_addresses: Vec<i64> = spark_rows
+                .iter()
+                .map(|row| row.get_row_addr())
+                .collect();
+            let mut row_sizes: Vec<i32> = spark_rows
+                .iter()
+                .map(|row| row.get_row_size())
+                .collect();
+
+            let row_address_ptr = row_addresses.as_mut_ptr();
+            let row_size_ptr = row_sizes.as_mut_ptr();
+
+            group.bench_with_input(
+                BenchmarkId::new(
+                    format!("fields_{}", num_fields),
+                    format!("rows_{}", num_rows),
+                ),
+                &(num_rows, &schema),
+                |b, (num_rows, schema)| {
+                    b.iter(|| {
+                        let tempfile = Builder::new().tempfile().unwrap();
+
+                        process_sorted_row_partition(
+                            *num_rows,
+                            BATCH_SIZE,
+                            row_address_ptr,
+                            row_size_ptr,
+                            schema,
+                            tempfile.path().to_str().unwrap().to_string(),
+                            1.0,
+                            false,
+                            0,
+                            None,
+                            &CompressionCodec::Zstd(1),
+                        )
+                        .unwrap();
+                    });
+                },
+            );
+
+            // Keep spark_rows alive for the benchmark
+            std::mem::drop(spark_rows);
+        }
+    }
+
+    group.finish();
+}
+
+fn config() -> Criterion {
+    Criterion::default()
+}
+
+criterion_group! {
+    name = benches;
+    config = config();
+    targets = benchmark_struct_conversion
+}
+criterion_main!(benches);