performance-design.md•8.41 kB
# 🚀 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:
```mermaid
graph TB
subgraph "Level 1: Memory Cache"
L1Cache[In-Memory Cache<br/>Fast access, limited size]
L1Expiry[TTL: 5 minutes]
end
subgraph "Level 2: File Cache"
L2Cache[File System Cache<br/>Persistent across sessions]
L2Expiry[TTL: 1 hour]
end
subgraph "Level 3: AI Cache"
L3Cache[AI Response Cache<br/>Expensive operations]
L3Expiry[TTL: 24 hours]
end
Request[Analysis Request] --> L1Cache
L1Cache -->|Cache Miss| L2Cache
L2Cache -->|Cache Miss| L3Cache
L3Cache -->|Cache Miss| Analysis[Full Analysis]
```
**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
```mermaid
graph LR
subgraph "Input Processing"
Input[Project Files] --> Split[File Splitting]
Split --> Queue[Analysis Queue]
end
subgraph "Parallel Workers"
Queue --> W1[Worker 1<br/>TypeScript Files]
Queue --> W2[Worker 2<br/>Python Files]
Queue --> W3[Worker 3<br/>Configuration Files]
Queue --> W4[Worker N<br/>Other Files]
end
subgraph "Result Aggregation"
W1 --> Merge[Result Merger]
W2 --> Merge
W3 --> Merge
W4 --> Merge
Merge --> Output[Analysis Results]
end
```
**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**
```typescript
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**
```typescript
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**
```typescript
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**:
```typescript
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
| Metric | Small Project<br/>(<100 files) | Medium Project<br/>(100-1000 files) | Large Project<br/>(1000+ files) |
|--------|-------------------------------|-------------------------------------|----------------------------------|
| **Initial Analysis** | 2-5 seconds | 15-30 seconds | 1-3 minutes |
| **Cached Analysis** | 0.1-0.5 seconds | 1-3 seconds | 5-15 seconds |
| **Memory Usage** | 50-100 MB | 200-400 MB | 500-800 MB |
| **CPU Usage** | 20-40% | 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
- **[Server Architecture](./server-architecture.md)** - Overall system design
- **[Security Philosophy](./security-philosophy.md)** - Security considerations
- **[Tool Design](./tool-design.md)** - Individual tool performance
---
## 📚 Further Reading
- **[Performance Testing Guide](../how-to-guides/performance-testing.md)** - How to test and optimize performance
- **[Configuration Reference](../reference/environment-config.md)** - Performance-related configuration options
- **[API Reference](../reference/api-reference.md)** - Performance monitoring endpoints
---
**Questions about performance design?** → **[Join the Discussion](https://github.com/tosin2013/mcp-adr-analysis-server/discussions)**