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feat(benchmark): Add dataset generator and sorting benchmarks for quickSort and pdqsort
This commit introduces a new dataset generator for creating various data patterns used in benchmarking sorting algorithms. It also implements a comprehensive benchmark harness that compares the performance of the baseline quickSort and the enhanced pdqsort across multiple data conditions, including random, sorted, reverse sorted, few unique, and pathological datasets. The results will help evaluate the effectiveness of the pdqsort enhancement.
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‎pkgs/collection/benchmark/dataset_generator.dart‎

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// Copyright (c) 2025, the Dart project authors. Please see the AUTHORS file
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// for details. All rights reserved. Use of this source code is governed by a
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// BSD-style license that can be found in the LICENSE file.
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/// Centralized generation of datasets for all benchmarks.
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///
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/// Ensures all algorithms are tested on the exact same data.
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library;
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import 'dart:math';
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const size = 50000;
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const count = 128; // Number of lists to cycle through.
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final List<List<int>> random = _generateRandom();
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final List<List<int>> sorted = _generateSorted();
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final List<List<int>> reverse = _generateReverse();
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final List<List<int>> fewUnique = _generateFewUnique();
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final List<List<int>> pathological = _generatePathological();
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List<List<int>> _generateRandom() {
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final r = Random(12345);
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return List.generate(
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count, (_) => List.generate(size, (_) => r.nextInt(2000)));
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}
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List<List<int>> _generateSorted() {
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final base = List.generate(size, (i) => i);
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return List.generate(count, (_) => List<int>.from(base));
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}
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List<List<int>> _generateReverse() {
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final base = List.generate(size, (i) => size - 1 - i);
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return List.generate(count, (_) => List<int>.from(base));
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}
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List<List<int>> _generateFewUnique() {
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final r = Random(67890);
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return List.generate(count, (_) => List.generate(size, (_) => r.nextInt(7)));
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}
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List<List<int>> _generatePathological() {
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final base = List.generate(size, (i) => i);
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// Creates a "V-shape" or "organ pipe" array that can be challenging
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// for quicksort implementations by promoting unbalanced partitions.
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final pathological = <int>[
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for (int i = 0; i < size; i++)
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if (i.isEven) base[i],
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for (int i = size - 1; i > 0; i--)
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if (i.isOdd) base[i],
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];
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return List.generate(count, (_) => List<int>.from(pathological));
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}

‎pkgs/collection/benchmark/sort_comparison_benchmark.dart‎ renamed to ‎pkgs/collection/benchmark/sort_benchmark.dart‎

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@@ -8,58 +8,11 @@ import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:collection/src/algorithms.dart' show quickSort;
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import 'package:collection/src/utils.dart';
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import 'dataset_generator.dart' as dataset_generator;
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// Sink variable to prevent the compiler from optimizing away benchmark code.
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int sink = 0;
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/// Centralized generation of datasets for all benchmarks.
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///
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/// Ensures all algorithms are tested on the exact same data.
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class DatasetGenerator {
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static const size = 50000;
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static const count = 128; // Number of lists to cycle through.
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static final List<List<int>> random = _generateRandom();
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static final List<List<int>> sorted = _generateSorted();
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static final List<List<int>> reverse = _generateReverse();
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static final List<List<int>> fewUnique = _generateFewUnique();
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static final List<List<int>> pathological = _generatePathological();
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static List<List<int>> _generateRandom() {
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final r = Random(12345);
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return List.generate(
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count, (_) => List.generate(size, (_) => r.nextInt(2000)));
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}
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static List<List<int>> _generateSorted() {
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final base = List.generate(size, (i) => i);
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return List.generate(count, (_) => List<int>.from(base));
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}
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static List<List<int>> _generateReverse() {
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final base = List.generate(size, (i) => size - 1 - i);
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return List.generate(count, (_) => List<int>.from(base));
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}
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static List<List<int>> _generateFewUnique() {
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final r = Random(67890);
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return List.generate(
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count, (_) => List.generate(size, (_) => r.nextInt(7)));
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}
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static List<List<int>> _generatePathological() {
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final base = List.generate(size, (i) => i);
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// Creates a "V-shape" or "organ pipe" array that can be challenging
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// for quicksort implementations by promoting unbalanced partitions.
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final pathological = <int>[
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for (int i = 0; i < size; i++)
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if (i.isEven) base[i],
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for (int i = size - 1; i > 0; i--)
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if (i.isOdd) base[i],
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];
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return List.generate(count, (_) => List<int>.from(pathological));
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}
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}
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/// The final aggregated result of a benchmark.
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class BenchmarkResult {
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final double mean;
@@ -99,7 +52,7 @@ abstract class SortBenchmarkBase extends BenchmarkBase {
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// Baseline (Old SDK quickSort)
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class QuickSortBaselineRandomBenchmark extends SortBenchmarkBase {
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QuickSortBaselineRandomBenchmark()
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: super('Baseline.Random', DatasetGenerator.random);
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: super('Baseline.Random', dataset_generator.random);
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@override
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void performSort() {
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final list = getNextList();
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class QuickSortBaselineSortedBenchmark extends SortBenchmarkBase {
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QuickSortBaselineSortedBenchmark()
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: super('Baseline.Sorted', DatasetGenerator.sorted);
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: super('Baseline.Sorted', dataset_generator.sorted);
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@override
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void performSort() {
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final list = getNextList();
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class QuickSortBaselineReverseBenchmark extends SortBenchmarkBase {
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QuickSortBaselineReverseBenchmark()
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: super('Baseline.Reverse', DatasetGenerator.reverse);
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: super('Baseline.Reverse', dataset_generator.reverse);
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@override
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void performSort() {
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final list = getNextList();
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class QuickSortBaselineFewUniqueBenchmark extends SortBenchmarkBase {
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QuickSortBaselineFewUniqueBenchmark()
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: super('Baseline.FewUnique', DatasetGenerator.fewUnique);
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: super('Baseline.FewUnique', dataset_generator.fewUnique);
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@override
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void performSort() {
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final list = getNextList();
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class QuickSortBaselinePathologicalBenchmark extends SortBenchmarkBase {
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QuickSortBaselinePathologicalBenchmark()
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: super('Baseline.Pathological', DatasetGenerator.pathological);
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: super('Baseline.Pathological', dataset_generator.pathological);
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@override
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void performSort() {
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final list = getNextList();
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// Enhancement (New pdqsort)
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class PdqSortEnhancementRandomBenchmark extends SortBenchmarkBase {
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PdqSortEnhancementRandomBenchmark()
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: super('Enhancement.Random', DatasetGenerator.random);
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: super('Enhancement.Random', dataset_generator.random);
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@override
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void performSort() {
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final list = getNextList();
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class PdqSortEnhancementSortedBenchmark extends SortBenchmarkBase {
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PdqSortEnhancementSortedBenchmark()
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: super('Enhancement.Sorted', DatasetGenerator.sorted);
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: super('Enhancement.Sorted', dataset_generator.sorted);
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@override
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void performSort() {
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final list = getNextList();
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class PdqSortEnhancementReverseBenchmark extends SortBenchmarkBase {
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PdqSortEnhancementReverseBenchmark()
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: super('Enhancement.Reverse', DatasetGenerator.reverse);
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: super('Enhancement.Reverse', dataset_generator.reverse);
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@override
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void performSort() {
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final list = getNextList();
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class PdqSortEnhancementFewUniqueBenchmark extends SortBenchmarkBase {
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PdqSortEnhancementFewUniqueBenchmark()
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: super('Enhancement.FewUnique', DatasetGenerator.fewUnique);
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: super('Enhancement.FewUnique', dataset_generator.fewUnique);
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@override
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void performSort() {
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final list = getNextList();
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class PdqSortEnhancementPathologicalBenchmark extends SortBenchmarkBase {
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PdqSortEnhancementPathologicalBenchmark()
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: super('Enhancement.Pathological', DatasetGenerator.pathological);
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: super('Enhancement.Pathological', dataset_generator.pathological);
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@override
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void performSort() {
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final list = getNextList();
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target.setRange(min + 1, targetOffset + i + 1, target, min);
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target[min] = element;
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}
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}
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}

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