documcp

# Static Site Generator Performance Analysis **Research Date**: 2025-01-14 **Domain**: SSG Recommendation Engine **Status**: Completed ## Research Overview Comprehensive analysis of static site generator performance characteristics, build times, and deployment considerations for DocuMCP recommendation engine. ## Key Research Findings ### Build Performance Comparison Based on CSS-Tricks comprehensive benchmarking study: | SSG | Language | Small Sites (1-1024 files) | Large Sites (1K-64K files) | Key Characteristics | |-----|----------|----------------------------|----------------------------|-------------------| | **Hugo** | Go | ~250x faster than Gatsby | ~40x faster than Gatsby | Fastest across all scales | | **Jekyll** | Ruby | Competitive with Eleventy | Slower scaling, Ruby bottleneck | Good for small-medium sites | | **Eleventy** | Node.js | Fast, lightweight | Good scaling | Excellent developer experience | | **Gatsby** | React | Slower startup (webpack overhead) | Improves relatively at scale | Rich features, plugin ecosystem | | **Next.js** | React | Framework overhead | Good with optimization | Hybrid capabilities | | **Docusaurus** | React | Moderate performance | Documentation optimized | Purpose-built for docs | ### Performance Characteristics Analysis #### **Tier 1: Speed Champions (Hugo)** - **Build Time**: Sub-second for small sites, seconds for large sites - **Scaling**: Linear performance, excellent for content-heavy sites - **Trade-offs**: Limited plugin ecosystem, steeper learning curve #### **Tier 2: Balanced Performance (Jekyll, Eleventy)** - **Build Time**: Fast for small sites, moderate scaling - **Scaling**: Jekyll hits Ruby performance ceiling, Eleventy scales better - **Trade-offs**: Good balance of features and performance #### **Tier 3: Feature-Rich (Gatsby, Next.js, Docusaurus)** - **Build Time**: Significant webpack/framework overhead - **Scaling**: Performance gap narrows at scale due to optimizations - **Trade-offs**: Rich ecosystems, modern features, slower builds ### Real-World Performance Implications #### **For DocuMCP Recommendation Logic:** 1. **Small Projects** (< 100 pages): - All SSGs perform adequately - Developer experience becomes primary factor - Hugo still 250x faster than Gatsby for simple sites 2. **Medium Projects** (100-1000 pages): - Performance differences become noticeable - Hugo maintains significant advantage - Jekyll starts showing Ruby limitations 3. **Large Projects** (1000+ pages): - Hugo remains fastest but gap narrows - Framework-based SSGs benefit from optimizations - Build time becomes CI/CD bottleneck consideration ### Deployment and CI/CD Considerations #### **GitHub Actions Build Time Impact** - **Free Plan Limitations**: 2000 minutes/month - **Cost Implications**: Slow builds consume more CI time - **Real Example**: Gatsby site taking 15 minutes vs Hugo taking 30 seconds #### **Content Editor Experience** - **Preview Generation**: Fast builds enable quick content previews - **Development Workflow**: Build speed affects local development experience - **Incremental Builds**: Framework support varies significantly ### Recommendation Engine Criteria Based on research findings, DocuMCP should weight these factors: 1. **Project Scale Weight**: - Small projects: 40% performance, 60% features/DX - Medium projects: 60% performance, 40% features/DX - Large projects: 80% performance, 20% features/DX 2. **Team Context Multipliers**: - Technical team: Favor performance (Hugo/Eleventy) - Non-technical content creators: Favor ease-of-use (Jekyll/Docusaurus) - Mixed teams: Balanced approach (Next.js/Gatsby) 3. **Use Case Optimization**: - **Documentation**: Docusaurus > MkDocs > Hugo - **Marketing Sites**: Next.js > Gatsby > Hugo - **Blogs**: Jekyll > Eleventy > Hugo - **Large Content Sites**: Hugo > Eleventy > Others ## Implementation Recommendations for DocuMCP ### Algorithm Design ```typescript // Performance scoring algorithm const calculatePerformanceScore = (projectMetrics: ProjectMetrics) => { const { pageCount, teamSize, techLevel, updateFrequency } = projectMetrics; // Scale-based performance weighting const performanceWeight = pageCount > 1000 ? 0.8 : pageCount > 100 ? 0.6 : 0.4; // SSG-specific performance scores (0-100) const performanceScores = { hugo: 100, eleventy: 85, jekyll: pageCount > 500 ? 60 : 80, nextjs: 70, gatsby: pageCount > 1000 ? 65 : 45, docusaurus: 75 }; return performanceScores; }; ``` ### Research Validation - ✅ Performance benchmarks analyzed from multiple sources - ✅ Real-world implications documented - ✅ Recommendation criteria established - ⚠️ Needs validation: Edge case performance scenarios - ⚠️ Needs testing: Algorithm implementation with real project data ## Sources & References 1. CSS-Tricks Comprehensive SSG Build Time Analysis 2. Jamstack.org Performance Surveys 3. GitHub Actions CI/CD Cost Analysis 4. Community Performance Reports (Hugo, Gatsby, Next.js)