Tag: Times

Fun Times Styling Checkbox States

We might leave a text input unstyled. We might leave a link unstyled. Even a button. But checkboxes… we don’t leave them alone. That’s why styling checkboxes never gets old.

Although designing checkboxes is not that complicated, we also don’t have to settle for simple background color changes, or adding and removing borders, to indicate state changes. We also don’t have to pull out any fancy design skills — that we don’t possess — to make this work. I’ll show you how.

Basics

In the following demos, the checkboxes pretty much have the same three-stack layout — at the bottom is a checkbox, and on top of it are two stacked elements, or pseudo-elements. The checkbox is indicated as checked or unchecked depending on which of the two is visible.

If you look at the CSS code in the pens you’ll notice all the layouts — including the one for the checkboxes — are grids. You can use other layouts that feel right for your use case (and learn more in the CSS-Tricks Grid Guide). Additional notes on code and design alternatives are at the end of the source code inside the pens.

In addition, any elements stacked on top of the checkbox have pointer-events: none so they don’t prevent users from clicking or tapping the checkbox.

Let’s now get to the first method.

Idea 1: Blended backgrounds as a state

Blending in CSS is a versatile technique. Manipulating colors relative to two or more elements or backgrounds can be handy in contexts you might not have thought of.

One such instance is the checkbox.

<input id="un" type="checkbox"> <label for="un">un</label> <!-- more checkboxes --> 
input[type=checkbox]::before, input[type=checkbox]::after {   mix-blend-mode: hard-light;   pointer-events: none;   /* more style */ } input[type=checkbox]::before {   background: green;   content: '✓';   color: white;   /* more style */ } input[type=checkbox]::after {   background: blue;   content: '⨯';   /* more style */ } input[type=checkbox]:checked::after {   mix-blend-mode: unset;   color: transparent; }

In this demo, I’ve styled the checkbox’s pseudo-elements green and blue, stacked them up, and gave them each a mix-blend-mode value. This means the background of each element blends with its backdrop.

I used the hard-light value, which emulates the result of either multiply or screen depending on if the top color is darker or lighter. You can learn in depth about different blend modes over at MDN.

When the box is checked, the ::after pseudo-element’s mix blend mode value is unset, resulting in a different visual.

Idea 2: Make a 3D animation

Animating a block of color is fun. Make them seem 3D and it’s even better. CSS has the means to render elements along an emulated 3D space. So using that, we make a 3D box and rotate it to indicate the checkbox state change.

<div class="c-checkbox">   <input type="checkbox" id="un">   <!-- cube design -->   <div><i></i><i></i><i></i><i></i></div> </div> <label for="un">un</label> <!-- more checkboxes -->
.c-checkbox > div {   transition: transform .6s cubic-bezier(.8, .5, .2, 1.4);   transform-style: preserve-3d;   pointer-events: none;   /* more style */ } /* front face */ .c-checkbox > div > i:first-child {   background: #ddd;   transform:  translateZ( -10px ); } /* back face */ .c-checkbox > div > i:last-child {   background: blue;   transform:  translateZ( 10px ); } /* side faces */ .c-checkbox > div > i:nth-of-type(2), .c-checkbox > div > i:nth-of-type(3) {   transform: rotateX(90deg)rotateY(90deg);   position: relative;   height: 20px;   top: 10px; } .c-checkbox > div > i:nth-of-type(2) {   background: navy;   right: 20px; } .c-checkbox > div > i:nth-of-type(3) {   background: darkslategray;   left: 20px; }

The <div> after the checkbox becomes a container of a 3D space — its child elements can be placed along the x, y and z axes — after it’s given transform-style: preserve-3d;.

Using the transform property, we place two <i> elements (grey and blue colored) with some distance between them across the z-axis. Two more are wedged between them, covering their left and right sides. It’s like a cardboard box that’s covered except at the top and bottom.

When the checkbox is checked, this grey and blue box is rotated sideways to face the other side. Since I’ve already added a transition to the <div>, its rotation is animated.

input:checked + div {    transform: rotateY( 180deg );  }

Idea 3: Playing with border radius

Changing a checked box’s border radius? Not that fun. Changing also the border radius of other boxes near it? Now we have something.

<input type="checkbox" id="un"> <label for="un">un</label> <!-- more rows of checkboxes -->
input {   background: #ddd;   border-radius: 20px;   /* more style */ } input:not(:first-of-type)::before {   content: '';       transform: translateY(-60px); /* move up a row */   pointer-events: none; } input:checked + * + input::before, input:last-of-type:checked {   border-radius: 20px;   background: blue; } input:checked + * + input:checked + * + input::before {   border-top-left-radius: unset !important;   border-top-right-radius: unset !important; } input:checked::before {   border-bottom-left-radius: unset !important;   border-bottom-right-radius: unset !important; } /* between the second-last and last boxes */  input:nth-of-type(4):checked + * + input:checked {   border-top-left-radius: unset;   border-top-right-radius: unset; }

If you’d just interacted with the demo before, you’ll notice that when you click or tap a checkbox, it not only can change its own borders but also the borders of the boxes after and before it.

Now, we don’t have selectors that can select elements prior, only the ones after. So what we did to control the appearance of a preceding box is use the pseudo-element of a checkbox to style the box before it. With exception of the first box, every other box gets a pseudo-element that’s moved to the top of the box before it.

Let’s say boxes A, B and C are one after another. If I click B, I can change the appearance of A by styling B’s pseudo-element, B by styling C’s pseudo-element, and C by styling D’s pseudo-element.

From B, the pseudo-elements of B, C and D are accessible — as long as the next element selector can be used between them in the layout.

The four corners of each checkbox are initially rounded when checked and unchecked. But if a box is checked, the following box’s top corners and preceding box’s bottom corners are straightened (by overriding and removing their border radii).

Idea 4: Using a CSS mask

Toggles, switches… they are also checkboxes as far as the code goes. So we can style the boxes as toggles for this one, and it’s done with a CSS mask, which Chris has written about before. But in a nutshell, it’s a technique where we use an image to filter out portions of its backdrop.

<input type="checkbox"> <div class="skin one"></div> <div class="skin two"></div>
.one.skin {   background: no-repeat center -40px url('photo-1584107662774-8d575e8f3550?w=350&q=100'); } .two.skin {   background: no-repeat center -110px url('photo-1531430550463-9658d67c492d?w=350&q=100');   --mask: radial-gradient(circle at 45px 45px , rgba(0,0,0,0) 40px, rgba(0,0,0,1) 40px);   mask-image: var(--mask); -webkit-mask-image: var(--mask); }

Two skins (displaying landscape photos) are on top of a checkbox. The topmost one gets a mask-image that’s in the shape of a typical toggle switch — a transparent circle at the left, and the rest is a fully opaque color. Through the transparent circle we see the photo below while the rest of the mask image shows the photo at the top.

When a checkbox is clicked, the transparent circle is moved to the right, so we see the image at the top through the circle while the rest shows the photo at the bottom.

input:checked ~ .two.skin {   --mask: radial-gradient(circle at 305px 45px, rgba(0,0,0,1) 40px, rgba(0,0,0,0) 40px);   mask-image: var(--mask); -webkit-mask-image: var(--mask); }

Idea 5: Using box shadow

Let’s end with the simplest — but what I consider to be the most effective — method of them all: an animated inset box-shadow.

<input id="un" type="checkbox"> <label for="un">un</label>
input {   transition: box-shadow .3s;   background: lightgrey;   /* more style */ } input:checked {    box-shadow: inset 0 0 0 20px blue; }

There are some CSS properties that can be animated by default and one of them is box-shadow. This type of subtle animation goes well with a minimalist theme.


That’s it! I hope this sparks some inspiration the next time you find yourself working with checkboxes. CSS gives us so many possibilities to indicate state changes, so have a little fun and please share if you have any interesting ideas.


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Media Queries in Times of @container

Max Böck took me up on my challenge to look through a codebase and see how many of the @media queries could ultimately become @container queries.

I took the bait and had a look at some of my projects – and yes, most of what I use @media for today can probably be accomplished by @container at some point. Nevertheless, I came up with a few scenarios where I think media queries will still be necessary.

Max didn’t say exactly how many would be replaced, but I got the impression it was 50/50ish.

A combination of both techniques will probably be the best way forward. @media can handle the big picture stuff, user preferences and global styles; @container will take care of all the micro-adjustments in the components themselves.

A perfect team!

I also think there will be a big difference between what we do when refactoring an existing CSS codebase to what we do when we are building from scratch. And it will be different what we do when we first get container queries to what we do years from now when new patterns have settled in. I’ve long been bullish on components being the right abstraction for front-end development. It feels like everything lately pushes us in that direction, from JavaScript frameworks and to native components, to container queries and style scoping.

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Comparing Static Site Generator Build Times

There are so many static site generators (SSGs). It’s overwhelming trying to decide where to start. While an abundance of helpful articles may help wade through the (popular) options, they don’t magically make the decision easy.

I’ve been on a quest to help make that decision easier. A colleague of mine built a static site generator evaluation cheatsheet. It provides a really nice snapshot across numerous popular SSG choices. What’s missing is how they actually perform in action.

One feature every static site generator has in common is that it takes input data, runs it through a templating engine, and outputs HTML files. We typically refer to this process as The Build.

There’s too much nuance, context, and variability needed to compare how various SSGs perform during the build process to display on a spreadsheet — and thus begins our test to benchmark build times against popular static site generators.

This isn’t just to determine which SSG is fastest. Hugo already has that reputation. I mean, they say it on their website — The world’s fastest framework for building websites — so it must be true!

This is an in-depth comparison of build times across multiple popular SSGs and, more importantly, to analyze why those build times look the way they do. Blindly choosing the fastest or discrediting the slowest would be a mistake. Let’s find out why.

The tests

The testing process is designed to start simple — with just a few popular SSGs and a simple data format. A foundation on which to expand to more SSGs and more nuanced data. For today, the test includes six popular SSG choices:

Each test used the following approach and conditions:

  • The data source for each build are Markdown files with a randomly-generated title (as frontmatter) and body (containing three paragraphs of content).
  • The content contains no images.
  • Tests are run in series on a single machine, making the actual values less relevant than the relative comparison among the lot.
  • The output is plain text on an HTML page, run through the default starter, following each SSG’s respective guide on getting started.
  • Each test is a cold run. Caches are cleared and Markdown files are regenerated for every test.

These tests are considered benchmark tests. They are using basic Markdown files and outputting unstyled HTML into the built output.

In other words, the output is technically a website that could be deployed to production, though it’s not really a real-world scenario. Instead, this provides a baseline comparison among these frameworks. The choices you make as a developer using one of these frameworks will adjust the build times in various ways (usually by slowing it down).

For example, one way in which this doesn’t represent the real-world is that we’re testing cold builds. In the real-world, if you have 10,000 Markdown files as your data source and are using Gatsby, you’re going to make use of Gatsby’s cache, which will greatly reduce the build times (by as much as half).

The same can be said for incremental builds, which are related to warm versus cold runs in that they only build the file that changed. We’re not testing the incremental approach in these tests (at this time).

The two tiers of static site generators

Before we do that, let’s first consider that there are really two tiers of static site generators. Let’s call them basic and advanced.

  • Basic generators (which are not basic under the hood) are essentially a command-line interface (CLI) that takes in data and outputs HTML, and can often be extended to process assets (which we’re not doing here).
  • Advanced generators offer something in addition to outputting a static site, such as server-side rendering, serverless functions, and framework integration. They tend to be configured to be more dynamic right out of the box.

I intentionally chose three of each type of generator in this test. Falling into the basic bucket would be Eleventy, Hugo, and Jekyll. The other three are based on a front-end framework and ship with various amounts of tooling. Gatsby and Next are built on React, while Nuxt is built atop Vue.

Basic generators Advanced generators
Eleventy Gatsby
Hugo Next
Jekyll Nuxt

My hypothesis

Let’s apply the scientific method to this approach because science is fun (and useful)!

My hypothesis is that if an SSG is advanced, then it will perform slower than a basic SSG. I believe the results will reflect that because advanced SSGs have more overhead than basic SSGs. Thus, it’s likely that we’re going to see both groups of generators — basic and advanced — bundled together, in the results with basic generators moving significantly quicker.

Let me expand on that hypothesis a bit.

Linear(ish) and fast

Hugo and Eleventy will fly with smaller datasets. They are (relatively) simple processes in Go and Node.js, respectively, and their build output will reflect that. While both SSG will slow down as the number of files grows, I expect them to remain at the top of the class, though Eleventy may be a little less linear at scale, simply because Go tends to be more performant than Node.

Slow, then fast, but still slow

The advanced, or framework-bound SSGs, will start out and appear slow. I suspect a single-file test to contain a significant difference — milliseconds for the basic ones, compared to several seconds for Gatsby, Next, and Nuxt.

The framework-based SSGs are each built using webpack, bringing a significant amount of overhead along with it, regardless of the amount of content they are processing. That’s the baggage we sign up for in using those tools (more on this later).

But, as we add thousands of files, I suspect we’ll see the gap between the buckets close, though the advanced SSG group will stay farther behind by some significant amount.

In the advanced SSG group, I expect Gatsby to be the fastest, only because it doesn’t have a server-side component to worry about — but that’s just a gut feeling. Next and Nuxt may have optimized this to the point where, if we’re not using that feature, it won’t affect build times. And I suspect Nuxt will beat out Next, only because there is a little less overhead with Vue, compared to React.

Jekyll: The odd child

Ruby is infamously slow. It’s gotten more performant over time, but I don’t expect it to scale with Node, and certainly not with Go. And yet, at the same time, it doesn’t have the baggage of a framework.

At first, I think we’ll see Jekyll as pretty speedy, perhaps even indistinguishable from Eleventy. But as we get to the thousands of files, the performance will take a hit. My gut feeling is that there may exist a point at which Jekyll becomes the slowest of all six. We’ll push up to the 100,000 mark to see for sure.

A hand-drawn line chart showing build time on the y-axis and number of files on the x-asix, where Next is a green line, then nuxt is a yellow line, gatsby is a pink line jekyll is a blue line, eleventy is a teal line and hugo is an orange line. All lines show the build time increasing as the number of files increase, where jekyll has the sharpest slope.

The results are in!

The code that powers these tests are on GitHub. There’s also a site that shows the relative results.

After many iterations of building out a foundation on which these tests could be run, I ended up with a series of 10 runs in three different datasets:

  • Base: A single file, to compare the base build times
  • Small sites: From 1 to 1024 files, doubling each to time (to make it easier to determine if the SSGs scaled linearly)
  • Large sites: From 1,000 to 64,000 files, double on each run. I originally wanted to go up to 128,000 files, but hit some bottlenecks with a few of the frameworks. 64,000 ended up being enough to produce an idea of how the players would scale with even larger sites.

Click or tap the images to view them larger.

Summarizing the results

A few results were surprising to me, while others were expected. Here are the high-level points:

  • As expected, Hugo was the fastest, regardless of size. What I didn’t expect is that it wasn’t even close to any other generator, even at base builds (nor was it linear, but more on that below.)
  • The basic and advanced groups of SSGs are quite obvious when looking at the results for small sites. That was expected, but it was surprising to see Next is faster than Jekyll at 32,000 files, and faster than both Eleventy and Jekyll at 64,000 files. Also surprising is that Jekyll performed faster than Eleventy at 64,000 files.
  • None of the SSGs scale linearly. Next was the closest. Hugo has the appearance of being linear, but only because it’s so much faster than the rest.
  • I figured Gatsby to be the fastest among the advanced frameworks, and suspected it would be the one to get closer to the basics. But Gatsby turned out to be the slowest, producing the most dramatic curve.
  • While it wasn’t specifically mentioned in the hypothesis, the scale of differences was larger than I would have imagined. At one file, Hugo was approximately 170 times faster than Gatsby. But at 64,000 files, it was closer — about 25 times faster. That means that, while Hugo remains the fastest, it actually has the most dramatic exponential growth shape among the lot. It just looks linear because of the scale of the chart.

What does it all mean?

When I shared my results with the creators and maintainers of these SSGs, I generally received the same message. To paraphrase:

The generators that take more time to build do so because they are doing more. They are bringing more to the table for developers to work with, whereas the faster sites (i.e. the “basic” tools) focus their efforts largely in converting templates into HTML files.

I agree.

To sum it up: Scaling Jamstack sites is hard.

The challenges that will present themselves to you, Developer, as you scale a site will vary depending on the site you’re trying to build. That data isn’t captured here because it can’t be — every project is unique in some way.

What it really comes down to is your level of tolerance for waiting in exchange for developer experience.

For example, if you’re going to build a large, image-heavy site with Gatsby, you’re going to pay for it with build times, but you’re also given an immense network of plugins and a foundation on which to build a solid, organized, component-based website. Do the same with Jekyll, and it’s going to take a lot more effort to stay organized and efficient throughout the process, though your builds may run faster.

At work, I typically build sites with Gatsby (or Next, depending on the level of dynamic interactivity required). We’ve worked with the Gatsby framework to build a core on which we can rapidly build highly-customized, image-rich websites, packed with an abundance of components. Our builds become slower as the sites scale, but that’s when we get creative by implementing micro front-ends, offloading image processing, implementing content previews, along with many other optimizations.

On the side, I tend to prefer working with Eleventy. It’s usually just me writing code, and my needs are much simpler. (I like to think of myself as a good client for myself.) I feel I have more control over the output files, which makes it easier for me to get 💯s on client-side performance, and that’s important to me.

In the end, this isn’t only about what is fast or slow. It’s about what works best for you and how long you’re willing to wait.

Wrapping up

This is just the beginning! The goal of this effort was to create a foundation on which we can, together, benchmark relative build times across popular static site generators.

What ideas do you have? What holes can you poke in the process? What can we do to tighten up these tests? How can we make them more like real-world scenarios? Should we offload the processing to a dedicated machine?

These are the questions I’d love for you to help me answer. Let’s talk about it.


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