Comparing Static Site Generator Build Times

Choosing a static site generator (SSG) can be daunting, given the sheer number of options. While many articles compare popular choices, a truly informed decision requires understanding real-world performance. This benchmark study analyzes build times across six popular SSGs to reveal not just speed differences, but also the underlying reasons for those differences. It's not simply about finding the fastest; it's about understanding the trade-offs.

Every SSG shares a core process: taking input data, applying a templating engine, and generating HTML. This "build" process is the focus of our comparison.

This isn't just a speed race (though Hugo's self-proclaimed title of "world's fastest" warrants investigation!). The goal is a deeper dive into build time variations and their causes. Dismissing slow performers or blindly favoring speed champions would be an oversimplification.

The Benchmark Tests

Our initial tests employed a simplified approach: six popular SSGs and a straightforward data format. This provides a baseline for future expansion to more SSGs and complex data structures. The participants:

• Eleventy
• Gatsby
• Hugo
• Jekyll
• Next
• Nuxt

Testing Methodology:

• Data: Markdown files with randomly generated titles (frontmatter) and three-paragraph bodies. No images were included.
• Environment: Tests were conducted sequentially on a single machine; relative comparisons are more significant than absolute values.
• Output: Plain text HTML, using each SSG's default starter configuration.
• Build Type: "Cold runs," with caches cleared and Markdown files regenerated for each test.

These are benchmark tests using basic Markdown and unstyled HTML output. While technically deployable, they don't fully represent real-world scenarios. This provides a foundational comparison. Real-world build times will vary based on developer choices and project complexity (usually increasing build time). For instance, "cold builds" differ significantly from real-world scenarios where caching (especially in Gatsby) drastically reduces build times, often by half or more. Incremental builds, focusing only on changed files, are also not included in this initial phase.

Two Tiers of SSGs

We categorized the generators into two tiers:

• Basic: Essentially command-line interfaces (CLIs) processing data and outputting HTML. Extensions for asset processing are possible but not tested here. (Eleventy, Hugo, Jekyll)
• Advanced: Offer additional features beyond static site generation, such as server-side rendering, serverless functions, and framework integration. More dynamic out-of-the-box. (Gatsby, Next, Nuxt)

Three SSGs from each tier were included.

Hypothesis

Our hypothesis: Advanced SSGs will be slower than basic SSGs due to increased overhead.

We anticipated:

• Linear(ish) and Fast: Hugo and Eleventy, leveraging Go and Node.js respectively, should exhibit high speed, especially with smaller datasets. Scaling should slow them, but they should remain top performers.

• Slow, Then Fast(er), But Still Slow: Framework-based SSGs (Gatsby, Next, Nuxt) using webpack would initially appear slow due to overhead. However, with larger datasets, we expected the gap to narrow, though they would remain behind the basic SSGs. Gatsby was predicted to be the fastest among the advanced group due to its lack of server-side components. Nuxt was expected to outperform Next due to potentially lower Vue.js overhead compared to React.

• Jekyll: The Wildcard: Ruby's performance is a factor. While lacking framework overhead, its speed was uncertain, particularly at scale.

Results

The test code and results are available on GitHub [link to GitHub repo]. Ten runs were conducted across three datasets:

• Base: Single file
• Small Sites: 1 to 1024 files, doubling each time.
• Large Sites: 1000 to 64,000 files, doubling each time. (128,000 was initially planned but hit bottlenecks with some frameworks.)

[Include charts/graphs here]

Summary of Findings

• Hugo's Dominance: As predicted, Hugo was the fastest across all datasets, significantly outperforming others even in base builds.

• Tiered Performance: The performance difference between basic and advanced SSGs was clear in small site tests. Surprisingly, Next and Eleventy showed comparable performance at 64,000 files. Jekyll consistently outperformed Eleventy.

• Gatsby's Unexpected Slowness: Gatsby, contrary to our hypothesis, was the slowest, exhibiting the most dramatic performance curve.

• Scale of Differences: The magnitude of performance differences was greater than anticipated. Hugo was approximately 250 times faster than Gatsby at a single file, and 40 times faster at 64,000 files. While Hugo remained significantly faster, the gap narrowed with increasing site size.

Interpretation

Feedback from SSG maintainers indicated that slower build times often reflect increased functionality and developer experience. Basic tools prioritize HTML generation, while advanced tools provide more features.

Key Takeaway: Scaling Jamstack sites presents unique challenges. The optimal SSG depends on your project's specific needs and your tolerance for build time versus developer experience. Large, image-heavy sites using Gatsby may have slower builds, but offer extensive plugin support and a structured development environment. Jekyll might offer faster builds but require more manual organization.

Personally, Gatsby (or Next) is preferred for large, client projects due to its robust features and plugin ecosystem, though optimization strategies are crucial at scale. Eleventy is favored for smaller, personal projects where fine-grained control and performance are prioritized.

Ultimately, the choice isn't solely about speed, but about the best fit for your project and workflow.

Next Steps

This is just the beginning. Future improvements include more realistic scenarios, larger datasets, dedicated testing machines, and more SSG inclusion. Collaboration is encouraged to refine the testing methodology and expand the scope of the benchmark.

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