Google Lighthouse is a tool used to audit and analyze web pages, providing insights into their performance, accessibility, SEO, and more. It is designed to be as reliable as possible; however, there are several reasons you might perceive its results as inconsistent.
Dynamic Content: Websites with dynamic content can exhibit variability in performance scores. Elements that load dynamically (e.g., advertisements, third-party widgets) can affect page load times and thus alter Lighthouse reports.
Network Issues: Network conditions can fluctuate, impacting the speed at which resources are loaded. Different network speeds during multiple tests can result in variances in performance scores.
Server Response: Changes in server response times also affect Lighthouse results. Server load, geographic location of the server, and server-side optimizations are factors that can lead to discrepancies in repeated tests.
Testing Environment: Running Lighthouse in different environments (e.g., local machine vs. server, browser extension vs. command line) can yield different results. The local system’s performance, background processes, and browser extensions may influence outcomes.
Browser State: Browsers with multiple open tabs or heavy RAM usage can affect Lighthouse results. It’s crucial to test Lighthouse in an optimal browser state, ideally with other tabs and applications closed.
Cache and Storage: Variations in browser cache and storage states between tests can influence performance metrics. Clearing cache or running tests in an incognito window may yield more consistent results.
For more accurate readings, run multiple tests and focus on trends over time rather than individual scores. Improvements could involve optimizing server response times, reducing dynamic content’s impact, and ensuring a clean testing environment.
One response to “Are Google Lighthouse results inconsistent, or is it just me?”
Thank you for shedding light on the nuances of Google Lighthouse results! It’s a great reminder that perceived inconsistencies can often stem from environmental variables rather than the tool itself. One additional factor I’d like to highlight is the importance of using the right audit settings, specifically choosing the correct device and network simulation options when running Lighthouse tests. Many users may overlook the impact of emulating a slower network or testing on mobile vs. desktop, which can significantly skew results.
Additionally, while it’s beneficial to focus on trends over time as you suggest, it might be useful to run tests during different times of day to factor in variations in server load and network conditions that could affect performance results. This approach could provide a fuller picture of performance capability and help identify specific areas for ongoing optimization.
Lastly, integrating Lighthouse into a continuous integration/continuous deployment (CI/CD) pipeline could also promote more consistent results over time as part of the development workflow. This way, developers can catch issues early and maintain strong performance metrics as part of their iterative process. Great post—lighthouse testing can be an enlightening journey with the right strategies!