MCPy: High-Performance Minecraft Server Engine

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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!

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🚧 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.

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🚀 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.

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📐 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

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📊 Performance Goals

Metric	Target Value
Scalability	20 TPS with 100+ concurrent players
Memory Usage	<2 GB for 10,000 chunks
Latency	<50 ms per player action
Reliability	100% test coverage for core modules
Throughput	10,000+ entity updates per tick

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⚙️ 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

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📦 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

git clone https://github.com/magi8101/mcpy.git
cd mcpy
# Windows
setup.bat
# Linux/macOS
chmod +x setup.sh
./setup.sh

Manual Installation

git clone https://github.com/magi8101/mcpy.git
cd mcpy
python -m venv .venv
# Windows:
.venv\Scripts\activate
# Linux/macOS:
source .venv/bin/activate
pip install -r _requirements.txt
pip install -e ".[dev]"
pip install -e ".[ai]"  # Optional: Enable AI features
python check_dependencies.py
python setup.py build_ext --inplace

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🚀 Running the Server

# Using setup scripts
# Windows:
setup.bat run
# Linux/macOS:
./setup.sh run

# Directly from the command line
python -m mcpy.server
python -m mcpy.server --config custom_config.toml --world my_world
python -m mcpy.server --performance-mode --max-players 100
python -m mcpy.server --debug --log-level debug

Command Line Options

Option	Description
--config PATH	Path to TOML config file
--world PATH	World directory
--port NUMBER	Network port (default: 25565)
--max-players NUMBER	Max players (default: 20)
--view-distance NUMBER	Chunk view distance (default: 10)
--performance-mode	Extra performance optimizations
--debug	Enable debug mode
--log-level LEVEL	Set log level (default: info)
--backup	Enable automatic backups

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🗄️ Database Configuration

SQLite (Default)

[database]
type = "sqlite"
path = "world/mcpy.db"
journal_mode = "WAL"
synchronous = "NORMAL"

PostgreSQL (Production)

[database]
type = "postgresql"
host = "localhost"
port = 5432
dbname = "mcpy"
user = "postgres"
password = "your_password"
pool_size = 10
max_overflow = 20
echo = false

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💾 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

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🧪 Development & Testing

pytest                                # Run full test suite
pytest tests/test_entity_system.py     # Entity system tests
python -m benchmarks.benchmark        # Benchmarks
python -m mcpy.profiling.profile_module world_engine  # Profile module
pytest --cov=mcpy --cov-report=html   # Test coverage report

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)
  

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🔧 Advanced Features

Plugin System

Add custom commands and behaviors easily:

from mcpy.plugins import Plugin, event

class TeleportPlugin(Plugin):
    @event("player.command")
    def on_command(self, player, command, args):
        if command == "tp" and len(args) >= 1:
            target = self.server.get_player_by_name(args[0])
            if target:
                player.teleport(target.x, target.y, target.z)
                return True
        return False

Real-time Monitoring

Integrated Prometheus/Grafana support:

[monitoring]
enabled = true
prometheus_port = 9090
metrics = ["tps", "memory_usage", "players_online", "chunks_loaded"]

AI Entity Behaviors

Flexible, behavior-tree-driven AI:

class ZombieAI(MobAI):
    def setup_behaviors(self):
        self.behaviors = BehaviorTree(
            Selector([
                Sequence([
                    CheckPlayerNearby(radius=16),
                    PathfindToPlayer(),
                    AttackPlayer()
                ]),
                Sequence([
                    Wait(random.randint(20, 100)),
                    MoveToRandomPosition(radius=10)
                ])
            ])
        )

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🗺️ 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

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🤝 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

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📄 License

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

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