Performance#

What is Performance#

The topic of performance aims to discuss the issue of how to make Lynx pages faster. Performance optimization focuses on improving Lynx page speed through two core objectives: maximizing runtime efficiency and ensuring a fluid user experience. It revolves around two core dimensions: User Perceived Performance and Application Runtime Performance.

User Perceived Performance#

User Perceived Performance (UPP) quantifies users’ subjective judgment of a page’s responsiveness, including loading speed and interaction latency. As the primary driver of user retention, UPP directly reflects how users experience application performance.

When network latency or insufficient system resources delay page rendering, users may abandon the page if no feedback mechanism (e.g., loading indicators) confirms ongoing progress. To address this, displaying loading animations, progress bars, or other visual components can maintain user engagement. These elements reassure users that the application is still functioning smoothly during the wait, thereby reducing bounce rates.

Improving user-perceived performance is highly beneficial for increasing user retention. However, since user perceived performance is subjective, accurately measuring it can be challenging and complex. The Performance API provides metrics to help evaluate user-perceived performance. For more details, refer to the Evaluating User Perceived Performance.

Application Runtime Performance#

Application Runtime Performance (ARP) measures quantifiable metrics for page loading and rendering speeds, forming the technical basis for user experience. ARP tracks time spent in critical stages (e.g., initialization, resource loading) and directly determines how quickly content becomes visible.

Suboptimal ARP manifests as prolonged resource loading or rendering delays, resulting in extended wait time before content becomes interactive. Pages that load slowly or are unresponsive can lead users to abandon further browsing.

ARP Analysis identifies bottlenecks by profiling time spent in key stages (initialization, resource loading, rendering pipelines). Developers can then implement optimizations such as resource preloading, incremental data streaming, or task scheduling logic refinements to enhance runtime efficiency. For more details, refer to the Evaluating Application Runtime Performance.