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🚀 Performance Design

Understanding the performance architecture and optimization strategies of the MCP ADR Analysis Server.

🎯 Overview​

The MCP ADR Analysis Server is designed for high-performance architectural analysis with intelligent caching, parallel processing, and memory optimization. The server handles large codebases efficiently while providing real-time AI-powered insights.

Key Performance Principles​

  • Intelligent Caching - Multi-level caching for analysis results and AI responses
  • Parallel Processing - Concurrent analysis of multiple files and components
  • Memory Optimization - Efficient memory usage for large projects
  • Incremental Analysis - Only analyze changed components
  • Smart Batching - Group related operations for efficiency

🏗️ Architecture and Design​

Multi-Level Caching Strategy​

The server implements a sophisticated caching system with three levels:

Cache Key Strategy:

  • File Hash: Content-based caching using SHA-256
  • Configuration Hash: Environment and settings changes
  • Dependency Hash: External dependencies and versions

Parallel Processing Architecture​

Worker Pool Management:

  • Dynamic Scaling: Adjust workers based on CPU cores
  • Memory Limits: Each worker has memory constraints
  • Timeout Handling: Prevent hanging operations
  • Error Isolation: Worker failures don't affect others

🔄 How It Works​

Analysis Pipeline​

Phase 1: Project Discovery

async function discoverProjectStructure(projectPath: string): Promise<ProjectStructure> {
  // 1. Scan directory tree (parallel)
  const files = await Promise.all([
    scanTypeScriptFiles(projectPath),
    scanPythonFiles(projectPath),
    scanConfigFiles(projectPath),
    scanDocumentation(projectPath),
  ]);

  // 2. Build dependency graph
  const dependencies = await buildDependencyGraph(files);

  // 3. Identify architectural patterns
  const patterns = await identifyPatterns(files, dependencies);

  return { files, dependencies, patterns };
}

Phase 2: Intelligent Analysis

async function performIntelligentAnalysis(structure: ProjectStructure): Promise<AnalysisResult> {
  // 1. Check cache first
  const cacheKey = generateCacheKey(structure);
  const cached = await getFromCache(cacheKey);
  if (cached) return cached;

  // 2. Parallel analysis
  const [security, performance, architecture] = await Promise.all([
    analyzeSecurity(structure),
    analyzePerformance(structure),
    analyzeArchitecture(structure),
  ]);

  // 3. AI-enhanced insights
  const insights = await generateAIInsights(security, performance, architecture);

  // 4. Cache results
  await setCache(cacheKey, { security, performance, architecture, insights });

  return { security, performance, architecture, insights };
}

Phase 3: Result Optimization

async function optimizeResults(results: AnalysisResult): Promise<OptimizedResult> {
  // 1. Compress large results
  const compressed = await compressResults(results);

  // 2. Extract key insights
  const keyInsights = extractKeyInsights(compressed);

  // 3. Generate summaries
  const summaries = await generateSummaries(keyInsights);

  return { compressed, keyInsights, summaries };
}

Memory Management​

Smart Memory Allocation:

  • Heap Monitoring: Track memory usage in real-time
  • Garbage Collection: Aggressive cleanup of temporary objects
  • Streaming: Process large files in chunks
  • Memory Pools: Reuse objects to reduce allocation overhead

Memory Optimization Techniques:

class MemoryOptimizedAnalyzer {
  private memoryPool: Map<string, any[]> = new Map();
  private maxMemoryUsage = 512 * 1024 * 1024; // 512MB limit

  async analyzeFile(filePath: string): Promise<AnalysisResult> {
    // Check memory usage
    if (this.getCurrentMemoryUsage() > this.maxMemoryUsage) {
      await this.cleanupMemory();
    }

    // Use memory pool for temporary objects
    const tempObjects = this.memoryPool.get('temp') || [];
    const result = await this.performAnalysis(filePath, tempObjects);

    // Return objects to pool
    this.memoryPool.set('temp', tempObjects);

    return result;
  }
}

💡 Design Decisions​

Decision 1: Multi-Level Caching​

Problem: AI analysis is expensive and slow, but results are often reusable
Solution: Implement intelligent caching with multiple levels and smart invalidation
Trade-offs:

  • ✅ Pros: 90% faster response times, reduced API costs
  • ❌ Cons: Increased memory usage, cache invalidation complexity

Decision 2: Parallel Processing​

Problem: Large projects have thousands of files, sequential processing is too slow
Solution: Parallel worker pool with intelligent file distribution
Trade-offs:

  • ✅ Pros: 5-10x faster analysis, better CPU utilization
  • ❌ Cons: Higher memory usage, complex error handling

Decision 3: Streaming Architecture​

Problem: Large files can cause memory exhaustion
Solution: Stream processing with chunked analysis
Trade-offs:

  • ✅ Pros: Handles files of any size, predictable memory usage
  • ❌ Cons: More complex code, potential for incomplete analysis

Decision 4: Incremental Analysis​

Problem: Re-analyzing unchanged files wastes time and resources
Solution: File change detection with incremental updates
Trade-offs:

  • ✅ Pros: Faster subsequent analyses, reduced resource usage
  • ❌ Cons: Complexity in change detection, potential for missed updates

📊 Performance Metrics​

Current Performance Benchmarks​

MetricSmall Project
(<100 files)		Medium Project
(100-1000 files)		Large Project
(1000+ files)
Initial Analysis2-5 seconds15-30 seconds1-3 minutes
Cached Analysis0.1-0.5 seconds1-3 seconds5-15 seconds
Memory Usage50-100 MB200-400 MB500-800 MB
CPU Usage20-40%40-70%60-90%

Optimization Targets​

  • Response Time: <2 seconds for cached results
  • Memory Usage: <1GB for projects with 10,000+ files
  • Cache Hit Rate: >80% for repeated analyses
  • Throughput: Process 1000+ files per minute

🔗 Related Concepts​

📚 Further Reading​

Questions about performance design? → Open an Issue