General

New Core Web Vitals and How They Work

Published Feb 14, 2024

Updated Feb 16, 2024

6 min read

Core Web Vitals, introduced by Google in 2020, have been around for a while now. And after four years, a significant change is coming in March 2024. In this article, I will try to tell you everything you need to know to be prepared. But first, let's briefly talk about what Core Web Vitals are.

What are Core Web Vitals?

Web Vitals is a Google initiative that aims to provide developers with guidance on authoring the best user experience on the web as well as tools to assess and measure it. Core Web Vitals are a subset of Web Vitals and currently consist of three main metrics:

Largest Contentful Paint (LCP): This metric measures the time it takes for the largest content element to be rendered on the screen. It should reflect the loading aspect of the user experience.
First Input Delay (FID): This metric measures the time it takes for the browser to respond to the first user interaction. It should reflect the interactivity aspect of the user experience.
Cumulative Layout Shift (CLS): This metric measures the amount of unexpected layout shifts of visual elements on the page. It should reflect the visual stability aspect of the user experience.

Part of the Core Web Vitals are also the thresholds that define what is considered a good experience. These thresholds are currently set to:

Metric	Good	Needs Improvement	Bad
LCP	≤ 2.5 s	2.5 - 4 s	> 4 s
FID	≤ 100 ms	100 - 300 ms	> 300 ms
CLS	≤ 0.1	0.1 - 0.25	> 0.25

While the reward of providing a good user experience should be enough for you to strive for great results in these metrics, that is not the only reason you should care about them. Google is reflecting these metrics in its search ranking algorithm as it wants it to "reward content that offers good user experience".

What's Changing in March 2024?

The change affects how interactivity is measured. The First Input Delay (FID) has been deemed no longer sufficient to measure the interactivity aspect of the user experience. It will be replaced by the Interaction to Next Paint (INP) metric on March 12, 2024.

INP, unlike FID, measures the response of all interactions up to a URL change. It then picks the slowest response from all interactions. (If there are more than 50 interactions, it picks a percentile instead, most often the 98th) making it much stricter than FID.

Why the Change?

There are three main reasons why FID is being replaced by INP:

FID is not strict enough: Upwards of 90% of websites meet the good threshold for FID nowadays, making it a poor differentiator between good and bad user experiences.
FID is not representative of the full interaction: FID only measures the first part of the reaction delay, up to the point before the response bubbles to JavaScript, not accounting for the (often quite long) time it takes for the JavaScript to process the response and for the browser to paint the next frame.
FID only measures the first interaction: According to Google, 90% of interactions happen after the first load, making FID not representative of the full user experience.

INP aims to address these issues by measuring the full interaction and by being stricter in its thresholds (according to Google, ~70% of websites should pass the good INP threshold now) and thus capturing a more representative picture of the user experience.

How Does INP Work?

As I previously mentioned, INP measures all interactions on the page and picks the slowest one or a percentile, which solves one of the issues with FID not representing the whole experience of interacting with a website. This way of measuring is not dissimilar to how CLS measures the unexpected layout shifts on the page as it also captures the full scope of the stay on the page.

The interactions considered by INP are:

Mouse clicks
Touchscreen taps
Keyboard presses (including virtual onscreen keyboards)

If an interaction is composed of multiple events, such as a keystroke, consisting of the keydown, keypress, and keyup events; INP groups them and measures the latency as the maximum duration of all the included events.

Unlike FID, it captures not only the time taken by tasks blocked by the main thread (the input delay) but also the processing time (events and JavaScript processing) and the presentation delay (the time it takes for the browser to paint the next frame), making it a much better reflection of the full user experience, but also much harder to pass.

So while FID reflects the first impression, INP should capture the overall responsiveness of a page.

For more information about interactions with comprehensive diagrams of what actually happens in the browser, check out the web.dev article on INP.

Google defined the thresholds for INP as follows:

Metric	Good	Needs Improvement	Bad
INP	≤ 200 ms	200 - 500 ms	> 500 ms

How to Prepare for INP?

Let's start with some general steps you should take to prepare for the new Web Vital:

1. Measure

Start with measuring your INP score. The easiest way to measure it is using PageSpeed Insights, but if you need to measure it locally, you can use Lighthose user flows which already support INP. If your INP scores are good, you're fine and you can probably stop reading further.

2. Do not panic

While, according to Tim Kadlec, only 17% of React websites and 11.8% of Next.js websites in the "top 100k" pass the good INP threshold, so it's very likely that you will have to do some work to pass; framework authors have been working on improving their performance and things may be as hot as Tim paints them. According to Google’s stats, ~80-90% of desktop sites pass the good INP threshold depending on the framework as of June 2023:

Technology	Passing on Mobile [%]	Passing on Desktop [%]
Angular (v2.0.0+)	28.6	83.6
Next.js	28.5	87.3
Nuxt.js	32.0	91.2
Preact	48.6	92.8
Vue (v2.0.0+)	50.3	94.1
Lit	50.0	88.3

3. Optimize

In case your INP scores are not good, you should start optimizing your website. Identify the pages that perform the worst and start with them. Focus on:

Code splitting and tree shaking to make sure only the necessary JavaScript is included.
Deferring non-critical JavaScript events until the main thread is idle. Consider also offloading heavy tasks to a Web Worker.
Debouncing or throttling frequently fired events.
Breaking up long JavaScript tasks into smaller ones.
Using passive event listeners.
Reducing the presentational delay e.g. by using the will-change CSS property, and preferring transform and opacity over the display and position properties in animations.
Eliminating third-party scripts. Very often, third-party scripts, be it for analytics, cookie consent, or other purposes, have a significant impact on performance. Considering which of them are not absolutely necessary, and either removing them or replacing them with a more performant solution is highly recommended.
Upgrading your dependencies to make sure you are taking advantage of the latest performance-improving features.

If you want to read more about the general INP optimization tips, you can check out this Vercel article.

While a lot of these optimizations, such as code splitting and tree shaking, are already taken care of by modern frameworks, I believe that it’s worth going through this list to see what you can do to make your website faster and thus a better experience for your users. We will explore concrete performance tips for popular frameworks in the next article.

Conclusion

The change in Core Web Vitals is coming and it's going to be a significant one. The new metric, Interaction to Next Paint, is going to be much stricter than the current First Input Delay and it's going to be much harder to pass. However, the good news is that framework authors have already been working on making the frameworks more INP-friendly, and while it may take some effort, you should be able to improve your INP score.

INP, however, should not be just another Lighthouse threshold we have to satisfy. This change should encourage us to look beyond surface-level metrics and dive deeper into the nuances of user interaction. As we adapt to these changes, our focus should remain on the end goal: creating a good and inclusive user experience for our visitors.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Jan Kaiser
Software engineer passionate about everything web-related. Particularly loves Angular, SCSS, TypeScript, soccer and dachshunds
@honzikec @honzikec

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Danny Thompson, Director of Technology at This Dot Labs, talks with Tanner Linsley, Creator of TanStack, about his latest project, TanStack Start. They discuss the challenges of existing frameworks like Next.js and Remix, the development of TanStack Router, and the future of React Server Components. Tanner also explains how caching strategies and fine-grained invalidation can transform the user experience. Chapters - - Introduction & Tanner’s Background (00:00) - Going Full-Time on TanStack (01:00) - The Birth of TanStack Router (02:21) - Why Build Another Framework? (04:00) - React Server Components: Potential & Limitations (07:05) - Fine-Grained Cache Invalidation & UX (09:02) - Parallel Data Fetching in Routing (13:39) - TanStack Start: Alpha & Future Plans (16:41) - Where to Learn More About TanStack (18:48) Find Tanner Linsley on Social Media Twitter: https://x.com/tannerlinsley Linkedin: https://www.linkedin.com/in/tannerlinsley/ Github: https://github.com/tannerlinsley TanStack: https://tanstack.com/ Sponsored by Wix Studio...

Oct 9, 2024

1 min

TanStack QueryJavaScriptReact

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

How to Run End-to-End Tests on Vercel Preview Deployments

How to Run End-to-End Tests on Vercel Preview Deployments End-to-end (E2E) tests are an essential part of modern web development. They simulate real user interactions and ensure your application works as expected in a live environment. When you're working with platforms like Vercel, which provide seamless deployment experiences, running E2E tests on preview deployments makes sense to catch any issues before merging your changes into the main branch. However, running E2E tests on preview deployments can be challenging as if the deployment isn’t ready when the tests start running, you’ll get errors when they should have succeeded. The Challenge: Timing Your E2E Tests Imagine you're working on a feature and pushing your changes to a branch. Vercel starts deploying the preview, and once that's done, you want to run your E2E tests automatically. But if the deployment isn't ready when the tests start, they might fail, and you'll be left wondering if the issue is with your code or the deployment process. Github Actions allow us to leverage a combination of actions that work together to ensure that your E2E tests only start once the Vercel deployment is ready. The Solution: Combining GitHub Actions for Seamless Testing To solve this problem, we can use two GitHub Actions in tandem: 1. vercel-preview-url: This action retrieves the preview URL of the Vercel deployment. It runs on push and pull request events, ensuring you get the exact URL of your deployment, which can then be used for further tests or other integrations. 2. github-action-await-vercel: This action waits until the Vercel deployment is marked as "READY". It continuously checks the deployment status, ensuring your tests only run once the deployment is fully prepared. ` Breaking Down the Workflow 1. Fetch the Preview URL: The vercel-preview-url action first retrieves the URL of the Vercel deployment. This URL is unique to the deployment, and we need to know it to run our E2E tests against the correct environment. This step ensures that we have the right URL before proceeding to the next steps. 2. Await Deployment Readiness: The github-action-await-vercel action waits for the deployment to be fully ready. It checks the deployment status at intervals (e.g., every 15 seconds) and only proceeds once the deployment is marked as "READY." This step ensures that your E2E tests don't run prematurely, preventing false failures. 3. Run E2E Tests: Once the deployment is ready, your E2E tests are executed against the live preview. By this point, you can be confident that the environment is fully set up, and any failures will likely be due to actual issues in the code rather than deployment timing. Why This Workflow Matters This workflow is essential for teams that rely on automated testing as part of their CI/CD pipelines. By waiting for the Vercel deployment to be fully ready, you minimize the risk of flaky tests that fail due to timing issues rather than code problems. This approach saves time and effort, as developers can focus on fixing real bugs rather than troubleshooting deployment issues. Moreover, this setup is flexible and can be adapted to various project needs. Whether you're running tests on a simple static site or a complex application, ensuring that your deployment is ready before running tests can dramatically improve the reliability of your CI/CD pipeline. Conclusion In modern web development, ensuring the reliability of your CI/CD pipeline is crucial. By leveraging GitHub Actions like vercel-preview-url and github-action-await-vercel, you can confidently run E2E tests on Vercel preview deployments. However, remember that the Vercel deployment can take some time to deploy, especially for larger projects, so use this approach wisely and ensure you only run it when necessary....

Sep 20, 2024

3 mins

InfrastructureVercelPlaywrightCypress

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

In this episode of Leadership Exchange, host Rob Ocel chats with Jerry Reghunadh, Senior Director of Enterprise Architecture at Data Nimbus, about leadership, AI adoption, and data-driven decision-making. Jerry shares his career journey, insights on leveraging tools like ChatGPT and Copilot, and strategies for building effective data pipelines. They explore how companies can avoid "analysis paralysis," adopt AI strategically, and evaluate new technologies to solve real-world problems. Tune in for practical advice on aligning innovation with business goals and staying competitive in a rapidly evolving tech landscape. Chapters 00:00 – Introduction 00:31 – Jerry’s Leadership Journey 02:01 – Discussing AI in Leadership 03:59 – Experimenting with AI Tools 06:24 – Overcoming Analysis Paralysis 09:18 – Importance of Early AI Adoption 13:12 – AI’s Impact on Efficiency 14:30 – Sponsor Message 16:26 – Setting Realistic Goals for AI 19:41 – Data Management and AI 23:09 – Is AI Just a Fad? 27:22 – Testing New Technologies 29:56 – Final Thoughts on AI 30:25 – Connect with Jerry 30:44 – Closing Remarks 31:20 – Outro Follow Jerry on Social Media Linkedin: https://www.linkedin.com/in/jerrymannel/ Sponsored by This Dot: thisdot.co...

Nov 18, 2024

1 min

AIEngineering Leadership

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

New Core Web Vitals and How They Work

What are Core Web Vitals?

What's Changing in March 2024?

Why the Change?

How Does INP Work?

How to Prepare for INP?

1. Measure

2. Do not panic

3. Optimize

Conclusion

Jan Kaiser

You might also like

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Introduction to Zod for Data Validation

How to Run End-to-End Tests on Vercel Preview Deployments

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

Let's innovate together!

You might also like

The Journey To Making A New Framework: TanStack Start with Tanner Linsley

Introduction to Zod for Data Validation

How to Run End-to-End Tests on Vercel Preview Deployments

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh