MCPy: High-Performance Minecraft Server Engine

MCPy is a next-generation, ultra-optimized Minecraft server engine powered by Python, Cython, and advanced scientific computing libraries. Our mission is to deliver exceptional performance and flexibility, making Minecraft server development both accessible and future-proof.

Note:
MCPy is under active development and is not yet feature-complete. The codebase contains known errors and is unstable. We welcome your bug reports and contributions to help us reach our goals faster!

🚧 Project Status

• The project is incomplete and contains known issues.

• Major features are under active development; the codebase is unstable.

• We highly value contributions and bug reports from the community.

🚀 Features at a Glance

• Cython-Accelerated Core: Event-driven server engine approaching C-level performance.

• Scientific Computing Backbone: Integrates NumPy, SciPy, and Polars for high-efficiency operations.

• Zero-Overhead Networking: Asynchronous, non-blocking, protocol-optimized networking.

• Sophisticated Entity System: Efficient, extensible entity management with advanced AI support.

• Robust Persistence Layer: Powered by PostgreSQL and SQLAlchemy ORM for reliable data storage.

• Comprehensive Benchmarking: Built-in performance analytics and profiling tools.

• Extensible Plugin Framework: Easily add server modifications.

• Real-Time Monitoring: Prometheus & Grafana integration for live metrics.

📐 Architecture Overview

MCPy is modular, with five high-performance core components:

1. server_core.pyx

   • Event-driven request handling

   • Adaptive, high-precision tick system

   • Dynamic worker thread pool management

   • Real-time performance profiling

2. world_engine.pyx

   • Procedural terrain generation with multi-octave noise and advanced biomes

   • Multi-threaded chunk generation & memory-efficient terrain storage

3. network_core.pyx

   • Zero-copy packet serialization and protocol-level compression

   • Robust connection pooling & DDoS mitigation

4. entity_system.pyx

   • Spatial hash-based entity tracking and multi-threaded physics

   • Modular AI behavior trees

5. persistence

   • SQLAlchemy ORM for PostgreSQL/SQLite

   • Efficient chunk serialization and transactional world state

📊 Performance Goals

⚙️ Technical Highlights

Cython & Performance

• Static typing (cdef) and aggressive compiler directives

• Direct NumPy buffer access and pointer arithmetic

• Multi-threaded parallelism via thread pools

Entity System

• Hierarchical, component-based design

• O(1) spatial partitioning via custom memory pools

• Adaptive Level-of-Detail (LOD) entity management

World Generation

• Multi-octave Perlin/Simplex noise

• Voronoi-based biome transitions

• Erosion, cave, and structure algorithms

• 10x chunk compression for storage efficiency

📦 Installation

Prerequisites

• Python 3.9+ (3.11+ recommended)

• Modern C++ compiler (VS 2019+ / GCC 9+)

• PostgreSQL 13+ (for production)

• Minimum 8 GB RAM (16 GB recommended)

Quick Setup

Manual Installation

🚀 Running the Server

Command Line Options

🗄️ Database Configuration

SQLite (Default)

PostgreSQL (Production)

💾 Persistence Features

• Transactional World Saving

  with session.begin(): for chunk in dirty_chunks: session.add(ChunkModel.from_chunk(chunk))

• Efficient Chunk Serialization

  chunk_data = np.savez_compressed(io_buffer, blocks=chunk.blocks, heightmap=chunk.heightmap, biomes=chunk.biomes)

• Player Data Management

  player_model = PlayerModel( uuid=player.uuid, username=player.username, position=json.dumps([player.x, player.y, player.z]), inventory=pickle.dumps(player.inventory, protocol=5), stats=json.dumps(player.stats) )

• Intelligent Auto-saving: Only modified chunks/entities are saved

• Automated Backups: Configurable intervals & retention

🧪 Development & Testing

Performance Tuning Examples

• Entity System

  entity_spatial_hash = {(int(e.x/16), int(e.z/16)): [] for e in entities} for entity in entities: entity_spatial_hash[(int(entity.x/16), int(entity.z/16))].append(entity)

• World Engine

  with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor: futures = [executor.submit(generate_chunk, x, z) for x, z in chunk_coords] chunks = [f.result() for f in futures]

• Network Optimization

  cdef char* buffer = <char*>malloc(packet_size) memcpy(buffer, &packet_header, sizeof(packet_header)) memcpy(buffer + sizeof(packet_header), packet_data, packet_data_size)

🔧 Advanced Features

Plugin System

Add custom commands and behaviors easily:

Real-time Monitoring

Integrated Prometheus/Grafana support:

AI Entity Behaviors

Flexible, behavior-tree-driven AI:

🗺️ Roadmap

Short-Term

• Entity collision system

• Crafting & inventory management

• Basic combat mechanics

• World generation optimization

Medium-Term

• Multi-world support & portals

• Custom block behaviors

• Enhanced mob AI

• In-game scripting API

Long-Term

• Distributed server architecture

• Machine learning-driven mob AI

• Real-time ray-traced lighting

• Custom physics engine

🤝 Contributing

We welcome your contributions! Please see our Contributing Guide to get started:

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add some amazing feature')

4. Push to your branch (git push origin feature/amazing-feature)

5. Open a Pull Request

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.