# Native Graph Database - Rust Implementation
## Project Overview
This project implements a **native graph database** in Rust with built-in JSON support and memory-efficient design principles. The goal is to create a lightweight, embedded graph database that can handle 1 million documents while maintaining SQLite-like simplicity and performance characteristics.
## Vision Statement
Build a production-ready, memory-efficient native graph database that leverages Rust's safety guarantees and performance characteristics to provide:
- **Native storage** with index-free adjacency for O(1) traversal performance
- **First-class JSON support** for flexible node and relationship properties
- **Memory efficiency** comparable to SQLite for embedded applications
- **Thread-safe, ACID-compliant** operations with minimal overhead
## Technology Stack & Constraints
### Core Technologies
- **Language**: Rust (stable channel, latest version)
- **Storage Backend**: Custom native graph storage with memory-mapped files
- **JSON Library**: `serde_json` for serialization/deserialization
- **Memory Management**: Custom allocators with off-heap storage for graph topology
- **Concurrency**: `tokio` for async operations, `parking_lot` for synchronization
- **Testing**: `criterion` for benchmarking, `proptest` for property-based testing
### Performance Constraints
- **Memory Target**: ≤ 1GB for 1M documents with 4M relationships
- **Traversal Performance**: O(1) per hop using index-free adjacency
- **Query Latency**: < 10ms for typical graph traversals
- **Throughput**: 100K+ operations per second for read queries
- **Concurrent Users**: Support for 1000+ concurrent read operations
### Design Constraints
- **Embedded-first**: Single-file database like SQLite
- **Zero-copy operations** where possible for memory efficiency
- **ACID compliance** with optimistic concurrency control
- **Schema-flexible**: Support arbitrary JSON properties on nodes/edges
- **Platform support**: Linux, macOS, Windows (x86_64, ARM64)
## Architecture Principles
Use principles like the following to ensure code quality
- KISS
- YAGNI
- DRY
- SOLID
- Design by Contract
- 12-Factor App Methodology
- Law of Demeter
### Native Storage Design
1. **Fixed-size record storage** for nodes and relationships
2. **Separate storage pools** for different data types (nodes, edges, properties, indexes)
3. **Memory-mapped file access** with intelligent caching
4. **Pointer-based adjacency** for true index-free navigation
5. **String interning** for memory deduplication of common values
### Memory Efficiency Strategies
1. **Off-heap graph topology** storage to avoid GC pressure
2. **Compressed property storage** using binary encoding
3. **Lazy loading** of node/edge properties
4. **Connection pooling** for relationship data
5. **Smart caching** with LRU eviction policies
### JSON Integration
1. **Native JSON property types** on nodes and edges
2. **Efficient binary serialization** of JSON data
3. **JSONPath query support** for property filtering
4. **Schema validation** options for data quality
5. **Flexible indexing** on JSON property paths
## Development Guidelines
- When using Claude code for development, use MCPs like Serena, Context7 and others for improved development outcomes.
- Find MCPs when nnecessary that might improve the development workflow and ask the user to add them with relevant commands provided to user
### Code Standards
- Follow Rust 2021 edition idioms and best practices
- Use `#![deny(unsafe_code)]` except in clearly marked performance-critical sections
- Comprehensive error handling with custom error types
- Extensive documentation with code examples
- Zero-tolerance for memory leaks or data races
### Testing Strategy
- Unit tests for all core components (target: >90% coverage)
- Integration tests for end-to-end workflows
- Property-based testing for data integrity
- Benchmark tests for performance regression detection
- Stress testing with large datasets (1M+ nodes)
### Performance Monitoring
- Built-in metrics collection for query performance
- Memory usage tracking and reporting
- Deadlock detection and prevention
- Query plan analysis and optimization hints
- Real-time performance dashboard for development
## Key Implementation Priorities
1. **Core Storage Engine**: Native graph storage with memory mapping
2. **Graph Operations**: Node/edge CRUD with JSON property support
3. **Query Engine**: Graph traversal with filtering and aggregation
4. **Indexing System**: Primary and secondary indexes for fast lookups
5. **Transaction Management**: ACID-compliant concurrent operations
6. **Memory Management**: Efficient allocation and garbage collection
7. **Persistence Layer**: Durable storage with crash recovery
8. **API Design**: Ergonomic Rust API with async support
## Success Metrics
### Performance Benchmarks
- Handle 1M documents in ≤ 1GB memory
- Achieve <1ms average traversal time per hop
- Support 10K+ concurrent read operations
- Maintain 99.9% uptime under normal load
- Complete bulk imports at 50K+ nodes/second
### Quality Metrics
- Zero known memory leaks or data corruption bugs
- 100% ACID compliance in all transaction scenarios
- Full compatibility with major Rust versions (stable, beta)
- Comprehensive API documentation with examples
- Production deployment success in at least 3 different use cases
## Integration Notes
- Design for easy integration with existing Rust applications
- Provide both synchronous and asynchronous APIs
- Support for popular serialization frameworks (serde ecosystem)
- Plugin architecture for custom query functions
- Export capabilities for data migration and backup
## Implementation Status
### ✅ Completed Features
- **Core Storage Engine**: Native graph storage with memory mapping implementation
- **Graph Operations**: Complete CRUD operations for nodes and relationships with JSON properties
- **Indexing System**: Multi-layered indexing (PropertyIndex, RangeIndex, CompositeIndex, RelationshipTypeIndex)
- **Query Engine**: Graph traversal with QueryBuilder, filtering, aggregation, and path finding
- **Transaction Management**: ACID-compliant transactions with isolation levels and concurrency control
- **GQL Integration**: Graph Query Language parser, lexer, and executor with comprehensive AST
- **Error Handling**: Comprehensive error types with detailed error messages and recovery
- **Documentation**: Complete rustdoc documentation with examples for all public APIs
### 🚧 In Progress
- **Performance Benchmarking**: Framework for performance regression detection and optimization
### 📋 Planned Features
- **Memory Management**: Advanced allocation strategies and garbage collection
- **Persistence Layer**: Enhanced crash recovery and data integrity verification
- **API Design**: Async support and ergonomic API improvements
- **Performance Optimization**: Query plan optimization and advanced caching strategies
## Development Best Practices
- Ensure to use github branching strategy and commit post each successful test / demo run
- All code changes include comprehensive rustdoc documentation
- Performance benchmarks should be run before major releases