Wednesday May 5, 2021 By David Quintanilla
Reducing HTML Payload With Next.js (Case Study) — Smashing Magazine

About The Creator

Liran Cohen is a full-stack developer, continuously seeking to discover ways to make quick and accessible web sites for people and robots alike.
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This text showcases a case examine of Bookaway’s touchdown web page efficiency. We’ll see how taking good care of the props we ship to Subsequent.js pages could make loading instances and Net Vitals higher.

I do know what you might be pondering. Right here’s one other article about decreasing JavaScript dependencies and the bundle measurement despatched to the shopper. However this one is a bit completely different, I promise.

This text is about a few issues that Bookaway confronted and we (as an organization within the touring business) managed to optimize our pages, in order that the HTML we ship is smaller. Smaller HTML means much less time for Google to obtain and course of these lengthy strings of textual content.

Often, the HTML code measurement shouldn’t be a giant challenge, particularly for small pages, not data-intensive, or pages that aren’t Website positioning-oriented. Nevertheless, in our pages, the case was completely different as our database shops a number of knowledge, and we have to serve hundreds of touchdown pages at scale.

It’s possible you’ll be questioning why we want such a scale. Effectively, Bookaway works with 1,500 operators and supply over 20k providers in 63 international locations with 200% development 12 months over 12 months (pre Covid-19). In 2019, we bought 500k tickets a 12 months, so our operations are complicated and we have to showcase it with our touchdown pages in an interesting and quick method. Each for Google bots (Website positioning) and to precise purchasers.

On this article, I’ll clarify:

  • how we discovered the HTML measurement is just too huge;
  • the way it obtained decreased;
  • the advantages of this course of (i.e. creating improved structure, enhancing ode group, offering an easy job for Google to index tens of hundreds of touchdown pages, and serving a lot fewer bytes to the shopper — particularly appropriate for folks with gradual connections).

However first, let’s speak in regards to the significance of pace enchancment.

Why Is Velocity Enchancment Crucial To Our Website positioning Efforts?

Meet “Web Vitals”, however particularly, meet LCP (Largest Contentful Paint):

“Largest Contentful Paint (LCP) is a vital, user-centric metric for measuring perceived load speed as a result of it marks the purpose within the web page load timeline when the web page’s predominant content material has doubtless loaded — a quick LCP helps reassure the consumer that the web page is useful.”

The principle aim is to have a small LCP as attainable. A part of having a small LCP is to let the consumer obtain as small HTML as attainable. That method, the consumer can begin the method of portray the most important content material paint ASAP.

Whereas LCP is a user-centric metric, decreasing it ought to make a giant assist to Google bots as Googe states:

“The net is a virtually infinite house, exceeding Google’s capacity to discover and index each obtainable URL. Because of this, there are limits to how a lot time Googlebot can spend crawling any single web site. Google’s period of time and assets to crawling a web site is often known as the positioning’s crawl price range.”

— “Advanced SEO,” Google Search Central Documentation

Probably the greatest technical methods to enhance the crawl price range is to help Google do more in less time:

Q: “Does web site pace have an effect on my crawl price range? How about errors?”

A: “Making a web site quicker improves the customers’ expertise whereas additionally rising the crawl price. For Googlebot, a speedy web site is an indication of wholesome servers in order that it could actually get extra content material over the identical variety of connections.”

To sum it up, Google bots and Bookaway purchasers have the identical aim — they each need to get content material delivered quick. Since our database accommodates a considerable amount of knowledge for each web page, we have to combination it effectively and ship one thing small and skinny to the purchasers.

Investigations for methods we will enhance led to discovering that there’s a huge JSON embedded in our HTML, making the HTML chunky. For that case, we’ll want to know React Hydration.

React Hydration: Why There Is A JSON In HTML

That occurs due to how Server-side rendering works in react and Subsequent.js:

  1. When the request arrives on the server — it must make an HTML based mostly on an information assortment. That assortment of knowledge is the article returned by getServerSideProps.
  2. React obtained the information. Now it kicks into play within the server. It builds in HTML and sends it.
  3. When the shopper receives the HTML, it’s instantly pained in entrance of him. In the intervening time, React javascript is being downloaded and executed.
  4. When javascript execution is completed, React kicks into play once more, now on the shopper. It builds the HTML once more and attaches occasion listeners. This motion known as hydration.
  5. As React constructing the HTML once more for the hydration course of, it requires the identical knowledge assortment used on the server (look again at 1.).
  6. This knowledge assortment is being made obtainable by inserting the JSON inside a script tag with id __NEXT_DATA__.

What Pages Are We Speaking About Precisely?

As we have to promote our choices in engines like google, the necessity for touchdown pages has arisen. Individuals often don’t seek for a particular bus line’s identify, however extra like, “How you can get from Bangkok to Pattaya?” Thus far, now we have created 4 sorts of touchdown pages that ought to reply such queries:

  1. Metropolis A to Metropolis B
    All of the strains stretched from a station in Metropolis A to a station in Metropolis B. (e.g. Bangkok to Pattaya)
  2. Metropolis
    All strains that undergo a particular metropolis. (e.g. Cancun)
  3. Nation
    All strains that undergo a particular nation. (e.g. Italy)
  4. Station
    All strains that undergo a particular station. (e.g. Hanoi-airport)

Now, A Look At Structure

Let’s take a high-level and really simplified have a look at the infrastructure powering the touchdown pages we’re speaking about. Fascinating components lie on 4 and 5. That’s the place the losing components:

Simplified Architecture
Authentic structure of Bookaway touchdown pages. (Large preview)

Key Takeaways From The Course of

  1. The request is hitting the getInitialProps operate. This operate runs on the server. This operate’s accountability is to fetch knowledge required for the development of a web page.
  2. The uncooked knowledge returned from REST Servers handed as is to React.
  3. First, it runs on the server. Because the non-aggregated knowledge was transferred to React, React can be chargeable for aggregating the information into one thing that can be utilized by UI parts (extra about that within the following sections)
  4. The HTML is being despatched to the shopper, along with the uncooked knowledge. Then React is kicking once more into play additionally within the shopper and doing the identical job. As a result of hydration is required (extra about that within the following sections). So React is doing the information aggregation job twice.

The Downside

Analyzing our web page creation course of led us to the discovering of Large JSON embedded contained in the HTML. Precisely how huge is troublesome to say. Every web page is barely completely different as a result of every station or metropolis has to combination a special knowledge set. Nevertheless, it’s secure to say that the JSON measurement could possibly be as huge as 250kb on fashionable pages. It was Later decreased to sizes round 5kb-15kb. Appreciable discount. On some pages, it was hanging round 200-300 kb. That’s huge.

The large JSON is embedded inside a script tag with id of ___NEXT_DATA___:

<script id="__NEXT_DATA__" sort="utility/json">
// Enormous JSON right here.

If you wish to simply copy this JSON into your clipboard, do that snippet in your Subsequent.js web page:


A query arises.

Why Is It So Large? What’s In There?

An amazing software, JSON Size analyzer, is aware of the best way to course of a JSON and exhibits the place many of the bulk of measurement resides.

That was our preliminary findings whereas inspecting a station page:

Json analysis of our station page
Construction of URL of touchdown pages for international locations that bookaway operates in. (Large preview)

There are two points with the evaluation:

  1. Information shouldn’t be aggregated.
    Our HTML accommodates the whole listing of granular merchandise. We don’t want them for portray on-screen functions. We do want them for aggregation strategies. For instance, We’re fetching a listing of all of the strains passing by this station. Every line has a provider. However we have to cut back the listing of strains into an array of two suppliers. That’s it. We’ll see an instance later.
  2. Pointless fields.
    When drilling down every object, we noticed some fields we don’t want in any respect. Not for aggregation functions and never for portray strategies. That’s as a result of We fetch the information from REST API. We will’t management what knowledge we fetch.

These two points confirmed that the pages want structure change. However wait. Why do we want an information JSON embedded in our HTML within the first place? 🤔

Structure Change

The problem of the very huge JSON needed to be solved in a neat and layered resolution. How? Effectively, by including the layers marked in inexperienced within the following diagram:

Frontend architecture change
Evaluation of knowledge payload despatched to the shopper. (Large preview)

A couple of issues to notice:

  1. Double knowledge aggregation was eliminated and consolidated to only being made simply as soon as on the Subsequent.js server solely;
  2. Graphql Server layer added. That makes certain we get solely the fields we would like. The database can develop with many extra fields for every entity, however that gained’t have an effect on us anymore;
  3. PageLogic operate added in getServerSideProps. This operate will get non-aggregated knowledge from back-end providers. This operate aggregates and prepares the information for the UI parts. (It runs solely on the server.)

Information Circulate Instance

We need to render this part from a station page:

Station suppliers
Suppliers part in Bookaway station web page. (Large preview)

We have to know who’re the suppliers are working in a given station. We have to fetch all strains for the strains REST endpoint. That’s the response we obtained (instance objective, in actuality, it was a lot bigger):

    id: "58a8bd82b4869b00063b22d2",
    class: "Standard",
    supplier: "Hyatt-Mosciski",
    type: "bus",
    id: "58f5e40da02e97f000888e07a",
    class: "Luxury",
    supplier: "Hyatt-Mosciski",
    type: "bus",
    id: "58f5e4a0a02e97f000325e3a",
    class: 'Luxury',
    supplier: "Jones Ltd",
    type: "minivan",
  { supplier: "Hyatt-Mosciski", amountOfLines: 2, types: ["bus"] },
  { provider: "Jones Ltd", amountOfLines: 1, sorts: ["minivan"] },

As you may see, we obtained some irrelevant fields. footage and id will not be going to play any function within the part. So we’ll name the Graphql Server and request solely the fields we want. So now it appears like this:

    supplier: "Hyatt-Mosciski",
    type: "bus",
    supplier: "Hyatt-Mosciski",
    type: "bus",
    supplier: "Jones Ltd",
    type: "minivan",

Now that’s a better object to work with. It’s smaller, simpler to debug, and takes much less reminiscence on the server. However, it’s not aggregated but. This isn’t the information construction required for the precise rendering.

Let’s ship it to the PageLogic operate to crunch it and see what we get:

  { supplier: "Hyatt-Mosciski", amountOfLines: 2, types: ["bus"] },
  { provider: "Jones Ltd", amountOfLines: 1, sorts: ["minivan"] },

This small knowledge assortment is shipped to the Subsequent.js web page.

Now that’s ready-made for UI rendering. No extra crunching and preparations are wanted. Additionally, it’s now very compact in comparison with the preliminary knowledge assortment now we have extracted. That’s essential as a result of we’ll be sending little or no knowledge to the shopper that method.

How To Measure The Affect Of The Change

Decreasing HTML measurement means there are fewer bits to obtain. When a consumer requests a web page, it will get absolutely shaped HTML in much less time. This may be measured in content material obtain of the HTML useful resource within the network panel.


Delivering skinny assets is important, particularly relating to HTML. If HTML is popping out huge, now we have no room left for CSS assets or javascript in our performance budget.

It’s best observe to imagine many real-world customers gained’t be utilizing an iPhone 12, however reasonably a mid-level gadget on a mid-level community. It seems that the efficiency ranges are fairly tight because the highly-regarded article suggests:

“Because of progress in networks and browsers (however not gadgets), a extra beneficiant world price range cap has emerged for websites constructed the “fashionable” method. We will now afford ~100KiB of HTML/CSS/fonts and ~300-350KiB of JS (gzipped). This rule-of-thumb restrict ought to maintain for not less than a 12 months or two. As at all times, the satan’s within the footnotes, however the top-line is unchanged: once we assemble the digital world to the boundaries of the most effective gadgets, we construct a much less usable one for 80+% of the world’s customers.”

Efficiency Affect

We measure the efficiency affect by the point it takes to obtain the HTML on gradual 3g throttling. that metric known as “content material obtain” in Chrome Dev Tools.

Right here’s a metric instance for a station page:

HTML measurement (earlier than gzip) HTML Obtain time (gradual 3G)
Earlier than 370kb 820ms
After 166 540ms
Whole change 204kb lower 34% Lower

Layered Answer

The structure adjustments included further layers:

  • GraphQl server: helpers with fetching precisely what we would like.
  • Devoted operate for aggregation: runs solely on the server.

These modified, other than pure efficiency enhancements, additionally supplied a lot better code group and debugging expertise:

  1. All of the logic concerning decreasing and aggregating knowledge now centralized in a single operate;
  2. The UI capabilities are actually way more easy. No aggregation, no knowledge crunching. They’re simply getting knowledge and portray it;
  3. Debugging server code is extra nice since we extract solely the information we want—no extra pointless fields coming from a REST endpoint.
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