Cumulative Layout Shift (CLS)

Milica Mihajlija
Milica Mihajlija
Philip Walton
Philip Walton

Browser Support

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Have you ever been reading an article online when something suddenly changes on the page? Without warning, the text moves, and you've lost your place. Or even worse: you're about to tap a link or a button, but in the instant before your finger lands—BOOM—the link moves, and you end up clicking something else!

Most of the time these kinds of experiences are just annoying, but in some cases, they can cause real damage.

A screencast illustrating how layout instability can negatively affect users.

Unexpected movement of page content usually happens because resources are loaded asynchronously or DOM elements get dynamically added to the page above existing content. The culprit might be an image or video with unknown dimensions, a font that renders larger or smaller than its fallback, or a third-party ad or widget that dynamically resizes itself.

What makes this issue even more problematic is that how a site functions in development is often quite different from how users experience it. Personalized or third-party content often doesn't behave the same in development as it does in production, test images are often already in the developer's browser cache, and API calls that run locally are often so fast that the delay isn't noticeable.

The Cumulative Layout Shift (CLS) metric helps you address this problem by measuring how often it's occurring for real users.

What is CLS?

CLS is a measure of the largest burst of layout shift scores for every unexpected layout shift that occurs during the entire lifespan of a page.

A layout shift occurs any time a visible element changes its position from one rendered frame to the next. (See below for details on how individual layout shift scores are calculated.)

A burst of layout shifts, known as a session window, is when one or more individual layout shifts occur in rapid succession with less than 1-second in between each shift and a maximum of 5 seconds for the total window duration.

The largest burst is the session window with the maximum cumulative score of all layout shifts within that window.

Example of session windows. Blue bars represent the scores of each individual layout shift.

What is a good CLS score?

To provide a good user experience, sites should strive to have a CLS score of 0.1 or less. To ensure you're hitting this target for most of your users, a good threshold to measure is the 75th percentile of page loads, segmented across mobile and desktop devices.

Good CLS values are 0.1 or less, poor values are greater than 0.25, and anything in between needs improvement

Layout shifts in detail

Layout shifts are defined by the Layout Instability API, which reports layout-shift entries any time an element that is visible within the viewport changes its start position (for example, its top and left position in the default writing mode) between two frames. Such elements are considered unstable elements.

Note that layout shifts only occur when existing elements change their start position. If a new element is added to the DOM or an existing element changes size, it doesn't count as a layout shift—as long as the change doesn't cause other visible elements to change their start position.

Layout shift score

To calculate the layout shift score, the browser looks at the viewport size and the movement of unstable elements in the viewport between two rendered frames. The layout shift score is a product of two measures of that movement: the impact fraction and the distance fraction (both defined below).

layout shift score = impact fraction * distance fraction

Impact fraction

The impact fraction measures how unstable elements impact the viewport area between two frames.

The union of the visible areas of all unstable elements for the previous frame and the current frame—as a fraction of the total area of the viewport—is the impact fraction for the current frame.

Impact fraction example with one _unstable element_

In the image above there's an element that takes up half of the viewport in one frame. Then, in the next frame, the element shifts down by 25% of the viewport height. The red, dotted rectangle indicates the union of the element's visible area in both frames, which, in this case, is 75% of the total viewport, so its impact fraction is 0.75.

Distance fraction

The other part of the layout shift score equation measures the distance that unstable elements have moved, relative to the viewport. The distance fraction is the greatest distance any unstable element has moved in the frame (either horizontally or vertically) divided by the viewport's largest dimension (width or height, whichever is greater).

Distance fraction example with one _unstable element_

In the example above, the largest viewport dimension is the height, and the unstable element has moved by 25% of the viewport height, which makes the distance fraction 0.25.

So, in this example the impact fraction is 0.75 and the distance fraction is 0.25, so the layout shift score is 0.75 * 0.25 = 0.1875.

The next example illustrates how adding content to an existing element affects the layout shift score:

Layout shift example with multiple stable and _unstable elements_

The "Click Me!" button is appended to the bottom of the gray box with black text, which pushes the green box with white text down (and partially out of the viewport).

In this example, the gray box changes size, but its start position does not change so it's not an unstable element.

The "Click Me!" button was not previously in the DOM, so its start position doesn't change either.

The start position of the green box, however, does change, but since it's been moved partially out of the viewport, the invisible area is not considered when calculating the impact fraction. The union of the visible areas for the green box in both frames (illustrated by the red, dotted rectangle) is the same as the area of the green box in the first frame—50% of the viewport. The impact fraction is 0.5.

The distance fraction is illustrated with the purple arrow. The green box has moved down by about 14% of the viewport so the distance fraction is 0.14.

The layout shift score is 0.5 x 0.14 = 0.07.

This last example illustrates multiple unstable elements:

Layout shift example with stable and _unstable elements_ and viewport clipping

In the first frame above there are four results of an API request for animals, sorted in alphabetical order. In the second frame, more results are added to the sorted list.

The first item in the list ("Cat") does not change its start position between frames, so it's stable. Similarly, the new items added to the list were not previously in the DOM, so their start positions don't change either. But the items labelled "Dog", "Horse", and "Zebra" all shift their start positions, making them unstable elements.

Again, the red, dotted rectangles represent the union of these three unstable elements' before and after areas, which in this case is around 60% of the viewport's area (impact fraction of 0.60).

The arrows represent the distances that unstable elements have moved from their starting positions. The "Zebra" element, represented by the blue arrow, has moved the most, by about 30% of the viewport height. That makes the distance fraction in this example 0.3.

The layout shift score is 0.60 x 0.3 = 0.18.

Expected vs. unexpected layout shifts

Not all layout shifts are bad. In fact, many dynamic web applications frequently change the start position of elements on the page.

User-initiated layout shifts

A layout shift is only bad if the user isn't expecting it. On the other hand, layout shifts that occur in response to user interactions (clicking a link, pressing a button, typing in a search box and similar) are generally fine, as long as the shift occurs close enough to the interaction that the relationship is clear to the user.

For example, if a user interaction triggers a network request that may take a while to complete, it's best to create some space right away and show a loading indicator to avoid an unpleasant layout shift when the request completes. If the user doesn't realize something is loading, or doesn't have a sense of when the resource will be ready, they may try to click something else while waiting—something that could move out from under them.

Layout shifts that occur within 500 milliseconds of user input will have the hadRecentInput flag set, so they can be excluded from calculations.

Animations and transitions

Animations and transitions, when done well, are a great way to update content on the page without surprising the user. Content that shifts abruptly and unexpectedly on the page almost always creates a bad user experience. But content that moves gradually and naturally from one position to the next can often help the user better understand what's going on, and guide them between state changes.

Be sure to respect prefers-reduced-motion browser settings, as some site visitors can experience ill effects or attention issues from animation.

CSS transform property allows you to animate elements without triggering layout shifts:

  • Instead of changing the height and width properties, use transform: scale().
  • To move elements around, avoid changing the top, right, bottom, or left properties and use transform: translate() instead.

How to measure CLS

CLS can be measured in the lab or in the field, and it's available in the following tools:

Field tools

Lab tools

Measure layout shifts in JavaScript

To measure layout shifts in JavaScript, you use the Layout Instability API.

The following example shows how to create a PerformanceObserver to log layout-shift entries to the console:

new PerformanceObserver((entryList) => {
  for (const entry of entryList.getEntries()) {
    console.log('Layout shift:', entry);
}).observe({type: 'layout-shift', buffered: true});

Measure CLS in JavaScript

To measure CLS in JavaScript, you need to group these unexpected layout-shift entries into sessions, and calculate the maximum session value. You can refer to the web vitals JavaScript library source code which contains a reference implementation on how CLS is calculated.

In most cases, the current CLS value at the time the page is being unloaded is the final CLS value for that page, but there are a few important exceptions as noted in the next section. The web vitals JavaScript library accounts for these as much as possible, within the limitations of the Web APIs.

Differences between the metric and the API

  • If a page is loaded in the background, or if it's backgrounded prior to the browser painting any content, then it should not report any CLS value.
  • If a page is restored from the back/forward cache, its CLS value should be reset to zero since users experience this as a distinct page visit.
  • The API does not report layout-shift entries for shifts that occur within iframes but the metric does as they are part of the user experience of the page. This can show as a difference between CrUX and RUM. To properly measure CLS you should consider them. Sub-frames can use the API to report their layout-shift entries to the parent frame for aggregation.

In addition to these exceptions, CLS has some added complexity due to the fact that it measures the entire lifespan of a page:

  • Users might keep a tab open for a very long time—days, weeks, months. In fact, a user might never close a tab.
  • On mobile operating systems, browsers typically do not run page unload callbacks for background tabs, making it difficult to report the "final" value.

To handle such cases, CLS should be reported any time a page is background—in addition to any time it's unloaded (the visibilitychange event covers both of these scenarios). And analytics systems receiving this data will then need to calculate the final CLS value on the backend.

Rather than memorizing and grappling with all of these cases yourself, developers can use the web-vitals JavaScript library to measure CLS, which accounts for everything mentioned above except the iframe case:

import {onCLS} from 'web-vitals';

// Measure and log CLS in all situations
// where it needs to be reported.

How to improve CLS

A full guide on optimizing CLS is available to guide you through the process of identifying layout shifts in the field and using lab data to drill down and optimize them.

Additional resources


Occasionally, bugs are discovered in the APIs used to measure metrics, and sometimes in the definitions of the metrics themselves. As a result, changes must sometimes be made, and these changes can show up as improvements or regressions in your internal reports and dashboards.

To help you manage this, all changes to either the implementation or definition of these metrics will be surfaced in this CHANGELOG.

If you have feedback for these metrics, you can provide it in the web-vitals-feedback Google group.