CodeAnalysis MCP Server

# Phase 2 Architecture: Redis Integration and Scalability This document outlines the architecture for Phase 2 of the MCP Code Analysis project, focusing on the Redis integration for improved scalability, state persistence, and concurrency handling. ## 1. Architecture Overview Phase 2 introduces distributed session management using Redis, enabling the MCP server to scale horizontally while maintaining state consistency across instances. The architecture follows a layered approach:  ### Key Components 1. **Client Layer**: AI agents and developer tools that interact with the MCP server 2. **API Gateway**: Entry point for requests, handling authentication and routing 3. **Service Layer**: Core business logic implementing the MCP protocol 4. **Persistence Layer**: Redis-backed storage for sessions and state 5. **Execution Layer**: Tools for code analysis, including Rust-based components ## 2. Redis Integration ### 2.1 Session Management Redis serves as the central repository for session data, replacing the in-memory session store from Phase 1. This enables: - **Distributed Sessions**: Multiple MCP server instances can share session data - **Persistence**: Sessions survive server restarts - **TTL Management**: Automatic expiration of inactive sessions - **Horizontal Scaling**: Add more server instances without session management concerns ### 2.2 Implementation Details The `RedisSessionStore` class implements the `SessionStore` interface, providing: ```typescript // Core session operations async getSession<T>(sessionId: string): Promise<T | null>; async setSession<T>(sessionId: string, state: T, ttl?: number): Promise<void>; async clearSession(sessionId: string): Promise<void>; async getSessions(): Promise<string[]>; // Extended capabilities async acquireLock(sessionId: string, timeout?: number): Promise<string | null>; async releaseLock(sessionId: string, lockToken: string): Promise<boolean>; async extendSessionTtl(sessionId: string, ttl: number): Promise<boolean>; async getSessionTtl(sessionId: string): Promise<number | null>; async createSessionIfNotExists<T>(sessionId: string, initialState: T): Promise<T>; ``` ### 2.3 Concurrency Control To handle concurrent access to session data, we implement a distributed locking mechanism: 1. **Lock Acquisition**: Before modifying a session, a server instance acquires a lock 2. **Token-Based Locking**: Each lock has a unique token to prevent accidental release 3. **Timeout Mechanism**: Locks automatically expire to prevent deadlocks 4. **Atomic Operations**: Using Redis Lua scripts for atomic lock operations ### 2.4 Data Serialization Session data is serialized and deserialized when storing in Redis: - **JSON Serialization**: For structured data - **Binary Serialization**: For efficient storage of large data structures (using MessagePack) - **Compression**: For large analysis results ## 3. Enhanced Tool Execution Service The `RedisToolExecutionService` extends the base implementation with: ### 3.1 State Persistence - **XState Machine Serialization**: Store and restore state machines - **Context Persistence**: Maintain execution context across server instances - **Incremental State Updates**: Optimize state persistence with partial updates ### 3.2 Concurrency Management - **Distributed Locking**: Prevent concurrent modifications of the same session - **Optimistic Concurrency**: Version-based conflict resolution for performance - **Deadlock Prevention**: Lock timeouts and circuit breakers ### 3.3 Failover and Recovery - **State Recovery**: Rebuild state machines from persisted data - **Execution Resumption**: Resume interrupted operations - **Error Handling**: Structured error responses with retry capabilities ## 4. Performance Optimizations ### 4.1 Caching Layer We implement a multi-level caching strategy: 1. **Local Memory Cache**: For frequently accessed data 2. **Redis Cache**: For shared cache across instances 3. **Cache Invalidation**: Event-based invalidation for consistency ### 4.2 Batched Operations - **Request Batching**: Group related operations - **Pipeline Execution**: Redis command pipelining for efficiency - **Bulk Processing**: Batch tool execution for analysis operations ### 4.3 Connection Pooling - **Connection Reuse**: Maintain a pool of Redis connections - **Health Monitoring**: Automatic detection of connection issues - **Graceful Degradation**: Fallback to local operations when Redis is unavailable ## 5. Redis Configuration ### 5.1 Deployment Options 1. **Single Redis Server**: For development and testing 2. **Redis Sentinel**: For high availability 3. **Redis Cluster**: For horizontal scaling and sharding ### 5.2 Key Structure We use a standardized key structure for Redis: - **Session Data**: `{prefix}:session:{sessionId}` - **Session Locks**: `{prefix}:lock:{sessionId}` - **Tool Results**: `{prefix}:result:{resultId}` - **Cache Entries**: `{prefix}:cache:{key}` ### 5.3 Memory Management - **TTL Policies**: Automatic expiration of unused data - **Memory Limits**: Configurable memory thresholds - **Eviction Policies**: LRU-based eviction for memory pressure ## 6. Rust-based Analysis Tools ### 6.1 Tool Integration We integrate Rust-based analysis tools using: 1. **Child Process Execution**: For standalone tools 2. **WebAssembly**: For browser-compatible tools 3. **FFI**: For direct integration with Node.js ### 6.2 Tool Communication - **Standardized I/O**: JSON-based input/output - **Streaming Results**: For large analysis outputs - **Progress Reporting**: Real-time progress updates ### 6.3 Tool Registry - **Tool Discovery**: Automatic registration of available tools - **Version Management**: Tool version tracking - **Dependency Resolution**: Handle tool dependencies ## 7. Deployment Architecture ### 7.1 Docker Containers Each component is containerized for easy deployment: 1. **MCP Server**: Node.js application container 2. **Redis**: Redis server container or managed service 3. **Analysis Tools**: Containerized Rust tools ### 7.2 Kubernetes Deployment For production environments, we provide Kubernetes manifests: - **Stateless Services**: For MCP server instances - **StatefulSets**: For Redis instances - **ConfigMaps**: For configuration - **Secrets**: For sensitive information ### 7.3 Cloud Deployment Support for major cloud providers: - **AWS**: Using ElastiCache for Redis - **Azure**: Using Azure Cache for Redis - **GCP**: Using Memorystore ## 8. Monitoring and Observability ### 8.1 Redis Monitoring - **Connection Health**: Track connection success rates - **Command Latency**: Measure Redis operation timing - **Memory Usage**: Monitor memory consumption ### 8.2 Service Metrics - **Request Rates**: Measure requests per second - **Response Times**: Track p50, p95, p99 latencies - **Error Rates**: Monitor failure percentages ### 8.3 Logging - **Structured Logging**: JSON-formatted logs - **Correlation IDs**: Track requests across components - **Log Levels**: Configurable verbosity ## 9. Security Considerations ### 9.1 Redis Security - **Authentication**: Redis password authentication - **Network Security**: Secure Redis connections - **Access Control**: Redis ACL for user management ### 9.2 Data Protection - **Sensitive Data Handling**: Encryption for sensitive information - **Data Isolation**: Session isolation between users - **Data Retention**: Policies for session data retention ## 10. Migration Plan ### 10.1 Gradual Rollout 1. **Development Integration**: Integrate Redis in development 2. **Testing Phase**: Comprehensive testing with Redis 3. **Shadow Mode**: Run Redis alongside existing storage 4. **Gradual Cutover**: Incrementally shift traffic to Redis ### 10.2 Backward Compatibility - **Interface Compatibility**: Maintain existing APIs - **Feature Flags**: Toggle Redis integration - **Fallback Mechanism**: Use local store if Redis is unavailable ## Conclusion Phase 2 architecture introduces Redis as a central component for session management and state persistence, enabling horizontal scaling while maintaining consistency. The proposed architecture balances performance, scalability, and reliability, providing a solid foundation for the advanced analysis tools planned for Phase 3.