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, always 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 research of Bookaway’s touchdown web page efficiency. We’ll see how caring for the props we ship to Subsequent.js pages could make loading occasions and Internet Vitals higher.

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

This text is about a few issues that Bookaway confronted and we (as an organization within the touring trade) 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 an enormous challenge, particularly for small pages, not data-intensive, or pages that aren’t Website positioning-oriented. Nevertheless, in our pages, the case was totally different as our database shops plenty of knowledge, and we have to serve 1000’s of touchdown pages at scale.

Chances are you’ll be questioning why we want such a scale. Properly, Bookaway works with 1,500 operators and supply over 20k companies in 63 international locations with 200% development yr over yr (pre Covid-19). In 2019, we bought 500k tickets a yr, so our operations are advanced 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 large;
  • the way it acquired lowered;
  • the advantages of this course of (i.e. creating improved structure, bettering ode group, offering an easy job for Google to index tens of 1000’s of touchdown pages, and serving a lot fewer bytes to the shopper — particularly appropriate for individuals with sluggish connections).

However first, let’s speak concerning the significance of velocity enchancment.

Why Is Pace Enchancment Vital To Our Website positioning Efforts?

Meet “Web Vitals”, however specifically, 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 most important content material has seemingly loaded — a quick LCP helps reassure the person that the web page is useful.”

The principle objective is to have a small LCP as potential. A part of having a small LCP is to let the person obtain as small HTML as potential. That manner, the person 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 an enormous assist to Google bots as Googe states:

“The net is an almost infinite area, exceeding Google’s means to discover and index each accessible URL. Consequently, there are limits to how a lot time Googlebot can spend crawling any single website. Google’s period of time and sources to crawling a website is usually referred to as the positioning’s crawl price range.”

— “Advanced SEO,” Google Search Central Documentation

The most effective technical methods to enhance the crawl price range is to help Google do more in less time:

Q: “Does website velocity have an effect on my crawl price range? How about errors?”

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

To sum it up, Google bots and Bookaway purchasers have the identical objective — they each need to get content material delivered quick. Since our database incorporates 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 large 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 The 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 primarily based on an information assortment. That assortment of information is the thing returned by getServerSideProps.
  2. React acquired 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 is 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 accessible 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 search engines like google, the necessity for touchdown pages has arisen. Folks often don’t seek for a selected bus line’s title, however extra like, “Find out how to get from Bangkok to Pattaya?” To date, we have now created 4 forms of touchdown pages that ought to reply such queries:

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

Now, A Look At Structure

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

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

Key Takeaways From The Course of

  1. The request is hitting the getInitialProps perform. This perform runs on the server. This perform’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. For the reason that non-aggregated knowledge was transferred to React, React can be accountable 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 large is troublesome to say. Every web page is barely totally different as a result of every station or metropolis has to combination a unique knowledge set. Nevertheless, it’s protected to say that the JSON measurement might be as large as 250kb on in style pages. It was Later lowered to sizes round 5kb-15kb. Appreciable discount. On some pages, it was hanging round 200-300 kb. That’s large.

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

<script id="__NEXT_DATA__" kind="software/json">
// Enormous JSON right here.

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


A query arises.

Why Is It So Large? What’s In There?

An excellent instrument, JSON Size analyzer, is aware of learn how 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. Knowledge shouldn’t be aggregated.
    Our HTML incorporates the entire checklist 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 traces passing by this station. Every line has a provider. However we have to cut back the checklist of traces 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 are able to’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 difficulty of the very large JSON needed to be solved in a neat and layered answer. How? Properly, by including the layers marked in inexperienced within the following diagram:

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

A number 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 positive we get solely the fields we wish. The database can develop with many extra fields for every entity, however that gained’t have an effect on us anymore;
  3. PageLogic perform added in getServerSideProps. This perform will get non-aggregated knowledge from back-end companies. This perform aggregates and prepares the information for the UI parts. (It runs solely on the server.)

Knowledge Move 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 traces for the traces REST endpoint. That’s the response we acquired (instance function, 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, varieties: ["minivan"] },

As you may see, we acquired some irrelevant fields. footage and id will not be going to play any position within the part. So we’ll name the Graphql Server and request solely the fields we want. So now it seems 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 isn’t aggregated but. This isn’t the information construction required for the precise rendering.

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

  { supplier: "Hyatt-Mosciski", amountOfLines: 2, types: ["bus"] },
  { provider: "Jones Ltd", amountOfLines: 1, varieties: ["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 we have now extracted. That’s vital as a result of we’ll be sending little or no knowledge to the shopper that manner.

How To Measure The Influence Of The Change

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


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

It’s best observe to imagine many real-world customers gained’t be utilizing an iPhone 12, however quite a mid-level system 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 units), a extra beneficiant world price range cap has emerged for websites constructed the “fashionable” manner. We are able to now afford ~100KiB of HTML/CSS/fonts and ~300-350KiB of JS (gzipped). This rule-of-thumb restrict ought to maintain for a minimum of a yr 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 perfect units, we construct a much less usable one for 80+% of the world’s customers.”

Efficiency Influence

We measure the efficiency influence by the point it takes to obtain the HTML on sluggish 3g throttling. that metric is 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 (sluggish 3G)
Earlier than 370kb 820ms
After 166 540ms
Complete change 204kb lower 34% Lower

Layered Answer

The structure modifications included extra layers:

  • GraphQl server: helpers with fetching precisely what we wish.
  • Devoted perform for aggregation: runs solely on the server.

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

  1. All of the logic relating to decreasing and aggregating knowledge now centralized in a single perform;
  2. The UI features are actually rather 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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