sorting-race
| Crates.io | sorting-race |
| lib.rs | sorting-race |
| version | 0.2.0 |
| created_at | 2025-09-14 08:49:26.568548+00 |
| updated_at | 2025-09-14 14:02:06.348969+00 |
| description | Terminal-based sorting algorithm race visualization tool with real-time metrics |
| homepage | https://github.com/avifenesh/algo-zoo |
| repository | https://github.com/avifenesh/algo-zoo |
| max_upload_size | |
| id | 1838497 |
| size | 1,037,089 |
Avi Fenesh (avifenesh)
documentation
README
🏁 Sorting Race
Watch sorting algorithms race against each other in your terminal!
A beautiful terminal visualization that runs multiple sorting algorithms simultaneously, showing their real-time progress, comparisons, and memory usage.
✨ Features
- 7 Sorting Algorithms racing in parallel
- Real-time Visualization with beautiful TUI
- 4 Fairness Modes to control algorithm execution
- Multiple Data Patterns to test different scenarios
- Live Metrics tracking comparisons, moves, and memory
- Interactive Controls to pause, reset, and explore
🚀 Quick Start
Installation
📦 Pre-built Binaries (Recommended)
Download for your platform from GitHub Releases:
Available Platforms:
- Linux (AMD64, ARM64) -
sorting-race-linux-*.tar.gz - macOS (Intel, Apple Silicon) -
sorting-race-macos-*.tar.gz - Windows (AMD64) -
sorting-race-windows-amd64.zip
# Linux/macOS - extract and run
tar -xzf sorting-race-*-*.tar.gz
./sorting-race
# Windows - extract and run
sorting-race.exe
🦀 Using Cargo
Install from crates.io - requires Rust toolchain:
cargo install sorting-race
sorting-race
🔧 From Source
For development or latest unreleased features:
# Clone and build
git clone https://github.com/avifenesh/algo-zoo.git
cd algo-zoo
cargo build --release
# Run
./target/release/sorting-race
💡 Tip: Pre-built binaries are fastest to get started. Use Cargo install if you want automatic updates with
cargo install-update sorting-race.
Basic Usage
# Run with defaults (50 elements)
sorting-race
# Larger array
sorting-race --size 100
# Different data pattern
sorting-race --distribution reversed
# Custom fairness mode
sorting-race --fair weighted --alpha 2 --beta 0.5
🎮 Controls
| Key | Action |
|---|---|
Space |
Pause/Resume the race |
R |
Reset with new random data |
Q |
Quit application |
🎯 Command Line Options
Array Configuration
-s, --size <N>- Number of elements to sort (default: 50)-S, --seed <SEED>- Random seed for reproducible runs-d, --distribution <TYPE>- Data distribution pattern
Distribution Types
shuffled- Random order (default)reversed- Worst case for some algorithmsnearly-sorted- Best case for adaptive algorithmsfew-unique- Tests stability with duplicates
Fairness Modes
Comparison Budget (--fair comp)
Each algorithm gets equal comparison operations per step.
sorting-race --fair comp --budget 16
Weighted Fairness (--fair weighted)
Balance between comparisons (α) and moves (β).
sorting-race --fair weighted --alpha 2.0 --beta 0.5
Wall-Time Fairness (--fair walltime)
Each algorithm gets equal CPU time slices.
sorting-race --fair walltime
Adaptive Fairness (--fair adaptive)
Dynamically adjusts based on algorithm progress.
sorting-race --fair adaptive --learning-rate 0.3
📊 The Algorithms
| Algorithm | Best Case | Average | Worst Case | Memory |
|---|---|---|---|---|
| Bubble Sort | O(n) | O(n²) | O(n²) | O(1) |
| Insertion Sort | O(n) | O(n²) | O(n²) | O(1) |
| Selection Sort | O(n²) | O(n²) | O(n²) | O(1) |
| Heap Sort | O(n log n) | O(n log n) | O(n log n) | O(1) |
| Merge Sort | O(n log n) | O(n log n) | O(n log n) | O(n) |
| Quick Sort | O(n log n) | O(n log n) | O(n²) | O(log n) |
| Shell Sort | O(n log n) | O(n^1.3) | O(n²) | O(1) |
🎨 Visual Elements
- Bar Chart - Current array state with color-coded values
- Progress Bars - Completion percentage for each algorithm
- Memory Graph - Real-time memory usage tracking
- Metrics Panel - Live comparisons and moves counter
- Status Text - Current operation for each algorithm
💡 Examples
Compare on Nearly Sorted Data
sorting-race --size 100 --distribution nearly-sorted
Watch how Insertion Sort excels on nearly sorted data!
Large Array with Adaptive Fairness
sorting-race --size 500 --fair adaptive --learning-rate 0.5
See how the system adapts to give slower algorithms more resources.
Worst Case Scenario
sorting-race --size 100 --distribution reversed --fair comp --budget 8
Observe how Quick Sort struggles with reversed data.
Stability Test
sorting-race --size 50 --distribution few-unique
Test which algorithms maintain the relative order of equal elements.
🖥️ System Requirements
- OS: Linux, macOS, Windows (x86_64, ARM64)
- Terminal: UTF-8 support with 256 colors
- Size: Minimum 80×24 terminal recommended
- Binary Size: ~2MB (no external dependencies)
- Rust: Only required if building from source
🤝 Contributing
Contributions are welcome! Check out DEVELOPER.md for development setup and guidelines.
📄 License
MIT License - see LICENSE for details.
🙏 Acknowledgments
Built with amazing Rust libraries:
Enjoy watching algorithms race! May the best sort win! 🏆