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Lighthouse Variability

Lots of factors contribute to the performance of webpages. Lighthouse performance scores tend to change due to the inherent variability in web and network technologies, even if there hasn't been a change to the page.

Sources of Variability

Variability in performance measurement is introduced via a number of channels with different levels of impact. Below is a table containing several common sources of metric variability, the typical impact they have on results, and the extent to which they are likely to occur in different environments.

Source Impact Typical End User PageSpeed Insights Controlled Lab
Page nondeterminism High LIKELY LIKELY LIKELY
Local network variability High LIKELY UNLIKELY UNLIKELY
Tier-1 network variability Medium POSSIBLE POSSIBLE POSSIBLE
Web server variability Low LIKELY LIKELY LIKELY
Client hardware variability High LIKELY UNLIKELY UNLIKELY
Client resource contention High LIKELY POSSIBLE UNLIKELY
Browser nondeterminism Medium CERTAIN CERTAIN CERTAIN

Below are more detailed descriptions of the sources of variance and the impact they have on the most likely combinations of Lighthouse runtime + environment. While applied throttling and simulated throttling approaches could be used in any of these three environments, the typical end user uses simulated throttling.

Page Nondeterminism

Pages can contain logic that is nondeterministic that changes the way a user experiences a page, i.e. an A/B test that changes the layout and assets loaded or a different ad experience based on campaign progress. This is an intentional and irremovable source of variance. If the page changes in a way that hurts performance, Lighthouse should be able to identify this case. The only mitigation here is on the part of the site owner in ensuring that the exact same version of the page is being tested between different runs.

Local Network Variability

Local networks have inherent variability from packet loss, variable traffic prioritization, and last-mile network congestion. Users with cheap routers and many devices sharing limited bandwidth are usually the most susceptible to this. Applied throttling partially mitigates these effects by applying a minimum request latency and maximum throughput that masks underlying retries. Simulated throttling mitigates these effects by replaying network activity on its own.

Tier-1 Network Variability

Network interconnects are generally very stable and have minimal impact but cross-geo requests, i.e. measuring performance of a Chinese site from the US, can start to experience a high degree of latency introduced from tier-1 network hops. Applied throttling partially mask these effects with network throttling. Simulated throttling mitigates these effects by replaying network activity on its own.

Web Server Variability

Web servers have variable load and do not always respond with the same delay. Lower-traffic sites with shared hosting infrastructure are typically more susceptible to this. Applied throttling partially masks these effects by applying a minimum request latency in its network throttling. Simulated throttling is susceptible to this effect but the overall impact is usually low when compared to other network variability.

Client Hardware Variability

The hardware on which the web page is loading can greatly impact performance. Applied throttling cannot do much to mitigate this issue. Simulated throttling partially mitigates this issue by capping the theoretical execution time of CPU tasks during simulation.

Client Resource Contention

Other applications running on the same machine while Lighthouse is running can cause contention for CPU, memory, and network resources. Malware, browser extensions, and anti-virus software have particularly strong impacts on web performance. Multi-tenant server environments (such as Travis, AWS, etc) can also suffer from these issues. Running multiple instances of Lighthouse at once also typically distorts results due to this problem. Applied throttling is susceptible to this issue. Simulated throttling partially mitigates this issue by replaying network activity on its own and capping CPU execution.

Browser Nondeterminism

Browsers have inherent variability in their execution of tasks that impacts the way webpages are loaded. This is unavoidable for applied throttling as at the end of the day they are simply reporting whatever was observed by the browser. Simulated throttling is able to partially mitigate this effect by simulating execution on its own, only re-using task execution times from the browser in its estimate.

Effect of Throttling Strategies

Below is a table containing several common sources of metric variability, the typical impact they have on results, and the extent to which different Lighthouse throttling strategies are able to mitigate their effect. Learn more about different throttling strategies in our throttling documentation.

Source Impact Simulated Throttling Applied Throttling No Throttling
Page nondeterminism High NO MITIGATION NO MITIGATION NO MITIGATION
Local network variability High MITIGATED PARTIALLY MITIGATED NO MITIGATION
Tier-1 network variability Medium MITIGATED PARTIALLY MITIGATED NO MITIGATION
Web server variability Low NO MITIGATION PARTIALLY MITIGATED NO MITIGATION
Client hardware variability High PARTIALLY MITIGATED NO MITIGATION NO MITIGATION
Client resource contention High PARTIALLY MITIGATED NO MITIGATION NO MITIGATION
Browser nondeterminism Medium PARTIALLY MITIGATED NO MITIGATION NO MITIGATION

Strategies for Dealing With Variance

Isolate External Factors

  • Isolate your page from third-party influence as much as possible. It’s never fun to be blamed for someone else's variable failures.
  • Isolate your own code’s nondeterminism during testing. If you’ve got an animation that randomly shows up, your performance numbers might be random too!
  • Isolate your test server from as much network volatility as possible. Use localhost or a machine on the same exact network whenever stability is a concern.
  • Isolate your client environment from external influences like anti-virus software and browser extensions. Use a dedicated device for testing when possible.

If your machine has really limited resources or creating a clean environment has been difficult, use a hosted lab environment like PageSpeed Insights or WebPageTest to run your tests for you. In continuous integration situations, use dedicated servers when possible. Free CI environments and “burstable” instances are typically quite volatile.

Run Lighthouse Multiple Times

When creating your thresholds for failure, either mental or programmatic, use aggregate values like the median, 90th percentile, or even min instead of single tests.

The median Lighthouse score of 5 runs is twice as stable as 1 run, and tools like pwmetrics can run Lighthouse for you automatically. Using the minimum value is also a big improvement over not testing at all and is incredibly simple to implement, just run Lighthouse up to 5 times until it passes!

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