Web Server Tuning: Performance and Reliability

A well tuned web server serves users quickly and with fewer problems. Tuning touches three layers: the operating system, the server software, and how content travels over the network. Start with a simple baseline and measure changes with consistent tests.

Understanding the workload

Know your workload before you tune. Collect data on requests per second, response times, error rates, and the mix of static versus dynamic content. Look for peak hours and longer tail latency. Set clear targets, for example: 95% of requests under 200 ms and an acceptable error rate below 1%. Use gentle load tests that resemble real traffic, not just synthetic bursts.

System and network tuning

Make small, safe changes first:

  • Increase the number of open files to match traffic, so new connections are not blocked.
  • Minimize swap use and keep most data in RAM to avoid pauses during requests.
  • Improve network readiness: ensure the kernel can queue connections and handle bursts with a larger backlog.

These adjustments reduce queuing and help connections start faster. Also consider keeping CPU cores at steady frequencies during busy periods and enabling basic logging to spot trends.

Web server configuration

Tune the software to fit your workload. Common knobs include:

  • Worker connections: 1024–4096, depending on traffic and memory.
  • Keepalive timeout: a short value (5–15 seconds) balances latency and resource reuse.
  • Compression and caching: enable gzip and set long cache headers for static assets.
  • Graceful restarts: allow a small window for old workers to finish.

Document changes and test one setting at a time to see its real impact. If you run multiple processes, ensure they don’t contend for the same memory resources.

Caching and static content

Serve static files directly when possible and use a content delivery network for distant users. Cache headers help browsers store content, reducing repeated work on the server. Offload heavy assets to a CDN and compress resources to save bandwidth. Consider schema for cache invalidation so updates reach users promptly.

Observability and safety

Monitor key metrics: latency, error rate, CPU and memory use, and queue lengths. Set alerts for unusual spikes and test failure scenarios. Keep logs rotating and plan for safe restarts during off-peak times. Store configuration changes in version control and tag experiments so you can roll back quickly.

Practical steps you can take

  • Establish a baseline with a simple load test.
  • Apply one change at a time and measure results.
  • Roll back quickly if a change hurts reliability.
  • Regularly review logs and adjust as traffic grows.

Key Takeaways

  • Start with workload data to guide tuning.
  • Balance performance with reliability, not just speed.
  • Use monitoring and staged changes to stay safe.