Tags: measure

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A framework for web performance

Here at Clearleft, we’ve recently been doing some front-end consultancy. That prompted me to jot down thoughts on design principles and performance:

We continued with some more performance work this week. Having already covered some of the nitty-gritty performance tactics like font-loading, image optimisation, etc., we wanted to take a step back and formulate an ongoing strategy for performance.

When it comes to web performance, the eternal question is “What should we measure?” The answer to that question will determine where you then concentrate your efforts—whatever it is your measuring, that’s what you’ll be looking to improve.

I started by drawing a distinction between measurements of quantities and measurements of time. Quantities are quite easy to measure. You can measure these quantities using nothing more than browser dev tools:

  • overall file size (page weight + assets), and
  • number of requests.

I think it’s good to measure these quantities, and I think it’s good to have a performance budget for them. But I also think they’re table stakes. They don’t actually tell you much about the impact that performance is having on the user experience. For that, we need to enumerate moments in time:

  • time to first byte,
  • time to first render,
  • time to first meaningful paint, and
  • time to first meaningful interaction.

There’s one more moment in time, which is the time until DOM content is loaded. But I’m not sure that has a direct effect on how performance is perceived, so it feels like it belongs more in the category of quantities than time.

Next, we listed out all the factors that could affect each of the moments in time. For example, the time to first byte depends on the speed of the network that the user is on. It also depends on how speedily your server (or Content Delivery Network) can return a response. Meanwhile, time to first render is affected by the speed of the user’s network, but it’s also affected by how many blocking elements are on the critical path.

By listing all the factors out, we can draw a distinction between the factors that are outside of our control, and the factors that we can do something about. So while we might not be able to do anything about the speed of the user’s network, we might well be able to optimise the speed at which our server returns a response, or we might be able to defer some assets that are currently blocking the critical path.

Factors
1st byte
  • server speed
  • network speed
1st render
  • network speed
  • critical path assets
1st meaningful paint
  • network speed
  • font-loading strategy
  • image optimisation
1st meaningful interaction
  • network speed
  • device processing power
  • JavaScript size

So far, everything in our list of performance-affecting factors is related to the first visit. It’s worth drawing up a second list to document all the factors for subsequent visits. This will look the same as the list for first visits, but with the crucial difference that caching now becomes a factor.

First visit factors Repeat visit factors
1st byte
  • server speed
  • network speed
  • server speed
  • network speed
  • caching
1st render
  • network speed
  • critical path assets
  • network speed
  • critical path assets
  • caching
1st meaningful paint
  • network speed
  • font-loading strategy
  • image optimisation
  • network speed
  • font-loading strategy
  • image optimisation
  • caching
1st meaningful interaction
  • network speed
  • device processing power
  • JavaScript size
  • network speed
  • device processing power
  • JavaScript size
  • caching

Alright. Now it’s time to get some numbers for each of the four moments in time. I use Web Page Test for this. Choose a realistic setting, like 3G on an Android from the East coast of the USA. Under advanced settings, be sure to select “First View and Repeat View” so that you can put those numbers in two different columns.

Here are some numbers for adactio.com:

First visit time Repeat visit time
1st byte 1.476 seconds 1.215 seconds
1st render 2.633 seconds 1.930 seconds
1st meaningful paint 2.633 seconds 1.930 seconds
1st meaningful interaction 2.868 seconds 2.083 seconds

I’m getting the same numbers for first render as first meaningful paint. That tells me that there’s no point in trying to optimise my font-loading, for example …which makes total sense, because adactio.com isn’t using any web fonts. But on a different site, you might see a big gap between those numbers.

I am seeing a gap between time to first byte and time to first render. That tells me that I might be able to get some blocking requests off the critical path. Sure enough, I’m currently referencing an external stylesheet in the head of adactio.com—if I were to inline critical styles and defer the loading of that stylesheet, I should be able to narrow that gap.

A straightforward site like adactio.com isn’t going to have much to worry about when it comes to the time to first meaningful interaction, but on other sites, this can be a significant bottleneck. If you’re sending UI elements in the initial HTML, but then waiting for JavaScript to “hydrate” those elements into working, the user can end up in an uncanny valley of tapping on page elements that look fine, but aren’t ready yet.

My point is, you’re going to see very different distributions of numbers depending on the kind of site you’re testing. There’s no one-size-fits-all metric to focus on.

Now that you’ve got numbers for how your site is currently performing, you can create two new columns: one of those is a list of first-visit targets, the other is a list of repeat-visit targets for each moment in time. Try to keep them realistic.

For example, if I could reduce the time to first render on adactio.com by 0.5 seconds, my goals would look like this:

First visit goal Repeat visit goal
1st byte 1.476 seconds 1.215 seconds
1st render 2.133 seconds 1.430 seconds
1st meaningful paint 2.133 seconds 1.430 seconds
1st meaningful interaction 2.368 seconds 1.583 seconds

See how the 0.5 seconds saving cascades down into the other numbers?

Alright! Now I’ve got something to aim for. It might also be worth having an extra column to record which of the moments in time are high priority, which are medium priority, and which are low priority.

Priority
1st byte Medium
1st render High
1st meaningful paint Low
1st meaningful interaction Low

Your goals and priorities may be quite different.

I think this is a fairly useful framework for figuring out where to focus when it comes to web performance. If you’d like to give it a go, I’ve made a web performance chart for you to print out and fill in. Here’s a PDF version if that’s easier for printing. Or you can download the HTML version if you want to edit it.

I have to say, I’m really enjoying the front-end consultancy work we’ve been doing at Clearleft around performance and related technologies, like offline functionality. I’d like to do more of it. If you’d like some help in prioritising performance at your company, please get in touch. Let’s make the web faster together.

The typography of a web book

I’m a sucker for classic old-style serif typefaces: Caslon, Baskerville, Bembo, Garamond …I love ‘em. That’s probably why I’ve always found the typesetting in Edward Tufte’s books so appealing—he always uses a combination of Bembo for body copy and Gill Sans for headings.

Earlier this year I stumbled on a screen version of Bembo used for Tufte’s digital releases called ET Book. Best of all, it’s open source:

ET Book is a Bembo-like font for the computer designed by Dmitry Krasny, Bonnie Scranton, and Edward Tufte. It is free and open-source.

When I was styling Resilient Web Design, I knew that the choice of typeface would be one of the most important decisions I would make. Remembering that open source ET Book font, I plugged it in to see how it looked. I liked what I saw. I found it particularly appealing when it’s full black on full white at a nice big size (with lower contrast or sizes, it starts to get a bit fuzzy).

I love, love, love the old-style numerals of ET Book. But I was disappointed to see that ligatures didn’t seem to be coming through (even when I had enabled them in CSS). I mentioned this to Rich and of course he couldn’t resist doing a bit of typographic sleuthing. It turns out that the ligature glyphs are there in the source files but the files needed a little tweaking to enable them. Because the files are open source, Rich was able to tweak away to his heart’s content. I then took the tweaked open type files and ran them through Font Squirrel to generate WOFF and WOFF2 files. I’ve put them on Github.

For this book, I decided that the measure would be the priority. I settled on a measure of around 55 to 60 characters—about 10 or 11 words per line. I used a max-width of 27em combined with Mike’s brilliant fluid type technique to maintain a consistent measure.

It looks great on small-screen devices and tablets. On large screens, the font size starts to get really, really big. Personally, I like that. Lots of other people like it too. But some people really don’t like it. I should probably add a font-resizing widget for those who find the font size too shocking on luxuriously large screens. In the meantime, their only recourse is to fork the CSS to make their own version of the book with more familiar font sizes.

The visceral reaction a few people have expressed to the font size reminds me of the flak Jeffrey received when he redesigned his personal site a few years back:

Many people who’ve visited this site since the redesign have commented on the big type. It’s hard to miss. After all, words are practically the only feature I haven’t removed. Some of the people say they love it. Others are undecided. Many are still processing. A few say they hate it and suggest I’ve lost my mind.

I wonder how the people who complained then are feeling now, a few years on, in a world with Medium in it? Jeffrey’s redesign doesn’t look so extreme any more.

Resilient Web Design will be on the web for a very, very, very long time. I’m curious to see if its type size will still look shockingly large in years to come.