Long Frames and INP – Understanding the Post Load Performance

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Most developers spend a lot of time optimizing for load time performance metrics, but most of the user's experience is after load. Lets understand what INP mean to your React application, how to utilize the new tolling around interactions such as the new Long Frame API alongside the upcoming INP metric to optimize our runtime performance. 

This talk has been presented at React Advanced 2024, check out the latest edition of this React Conference.

FAQ

Vinicius is a tech lead for Gillian and has worked for companies like Volvo, Spotify, and Kleiner. He has six years of experience in performance optimization.

Long frames and INP are metrics that help assess the post-load performance of web applications, focusing on user interactions and responsiveness beyond the initial load time.

Introduced in 2015, the RAIL model stands for Response, Animation, Idle, and Load. It provides a framework for understanding user interactions and improving responsiveness.

INP assesses overall page responsiveness by focusing on the longest interaction observed, replacing the first input delay as a core metric to consider post-load performance.

The Long Animation Frame API helps identify performance issues by providing detailed data on the execution time and delays in animation frames, improving attribution models for performance issues.

Developers traditionally focus on load time because slow loading can lead to high bounce rates and user frustration, often sidelining ongoing interaction performance.

It provides a more comprehensive breakdown of work done during animation frames, offering better insights into delays caused by various tasks, including styling and layout.

The Long Task API lacks detailed attribution, failing to identify specific scripts or functions causing performance issues, and does not account for styling and layout tasks.

Post-load performance is crucial because users spend most of their time interacting with the application after it loads. Improving this can enhance user experience and reduce frustration.

Vinicius Dallacqua
Vinicius Dallacqua
23 min
28 Oct, 2024

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  • Va Da
    Va Da
    P4
    It is amazing how much can be performantly rendered using a <canvas> element.
Video Summary and Transcription
Welcome to my talk on long frames and INP, understanding the post-load performance. The rail model introduced in 2015 provides recommendations for responsiveness and organizing JavaScript work around user input. Metrics like FCP, FMP, and LCP measure visual completeness, while metrics like first interactive and CPU idle assess CPU idleness. Total blocking time measures the impact of long tasks on page responsiveness, and TTI assesses when the browser can respond to interactions reliably. INP is a user-centric responsiveness metric that encompasses all potential causes of slow interactions. The long animation frame API improves attribution and helps pinpoint performance problems. Tools like Vercel toolbar and trace viewer on PerfLab can visualize interaction to next paint. INP and long animation frames provide valuable insights for improving web experiences.
Video transcription and chapters available for users with access.

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