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

// 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)