A blog and website by Peter Bengtsson

What English stop words overlap with JavaScript reserved keywords?

07 May 2021 0 comments   JavaScript, MDN

The list of stop words in Elasticsearch is:

a, an, and, are, as, at, be, but, by, for, if, in, into, 
is, it, no, not, of, on, or, such, that, the, their, 
then, there, these, they, this, to, was, will, with

The list of JavaScript reserved keywords is:

abstract, arguments, await, boolean, break, byte, case, 
catch, char, class, const, continue, debugger, default, 
delete, do, double, else, enum, eval, export, extends, 
false, final, finally, float, for, function, goto, if, 
implements, import, in, instanceof, int, interface, let, 
long, native, new, null, package, private, protected, 
public, return, short, static, super, switch, synchronized, 
this, throw, throws, transient, true, try, typeof, var, 
void, volatile, while, with, yield

That means that the overlap is:

for, if, in, this, with

And the remainder of the English stop words is:

a, an, and, are, as, at, be, but, by, into, is, it, no, 
not, of, on, or, such, that, the, their, then, there, 
these, they, to, was, will

Why does this matter? It matters when you're writing a search engine on English text that is about JavaScript. Such as, MDN Web Docs. At the time of writing, you can search for this because there's a special case explicitly for that word. But you can't search for for which is unfortunate.

But there's more! I think we should consider certain prototype words to be considered "reserved" because they are important JavaScript words that should not be treated as stop words. For example...

My contribution to 2021 Earth Day: optimizing some bad favicons on MDN Web Docs

23 April 2021 0 comments   Web development, MDN

tl;dr; The old /favicon.ico was 15KB and due to bad caching was downloaded 24M times in the last month totaling ~350GB of server-to-client traffic which can almost all be avoided.

How to save the planet? Well, do something you can do, they say. Ok, what I can do is to reduce the amount of electricity consumed to browse the web. Mozilla MDN Web Docs, which I work on, has a lot of traffic from all over the world. In the last 30 days, we have roughly 70M pageviews across roughly 15M unique users.
A lot of these people come back to MDN more than once per month so good assets and good asset-caching matter.

I found out that somehow we had failed to optimize the /favicon.ico asset! It was 15,086 bytes when, with Optimage, I was quickly able to turn it down to 1,153 bytes. That's a 13x improvement! Here's what that looks like when zoomed in 4x:

Old and new favicon.ico

The next challenge was the Cache-Control. Our CDN is AWS Cloudfront and it respects whatever Cache-Control headers we set on the assets. Because favicon.ico doesn't have a unique hash in its name, the Cache-Control falls back to the default of 24 hours (max-age=86400) which isn't much. Especially for an asset that almost never changes and besides, if we do decide to change the image (but not the name) we'd have to wait a minimum of 24 hours until it's fully rolled out.

Another thing I did as part of this was to stop assuming the default URL of /favicon.ico and instead control it with the <link rel="shortcut icon" href="/favicon.323ad90c.ico" type="image/x-icon"> HTML meta tag. Now I can control the URL of the image that will be downloaded.

Our client-side code is based on create-react-app and it can't optimize the files in the client/public/ directory.
So I wrote a script that post-processes the files in client/build/. In particular, it looks through the index.html template and replaces...

<link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">


<link rel="shortcut icon" href="/favicon.323ad90c.ico" type="image/x-icon">

Plus it makes a copy of the file with this hash in it so that the old URL still resolves. But now can cache it much more aggressively. 1 year in fact.

In summary

Combined, we used to have ~350GB worth of data sent from our CDN(s) to people's browsers every month.
Just changing the image itself would turn that number to ~25GB instead.
The new Cache-Control hopefully means that all those returning users can skip the download on a daily basis which will reduce the amount of network usage even more, but it's hard to predict in advance.

How to simulate slow lazy chunk-loading in React

25 March 2021 0 comments   React, JavaScript

Suppose you have one of those React apps that lazy-load some chunk. It just basically means it injects a .js static asset URL into the DOM and once it's downloaded by the browser, it carries on the React rendering with the new code loaded. Well, what if the network is really slow? In local development, it can be hard to simulate this. You can mess with the browser's Devtools to try to slow down the network, but even that can be too fast sometimes.

What I often do is, I take this:

const SettingsApp = React.lazy(() => import("./app"));

...and change it to this:

const SettingsApp = React.lazy(() =>
  import("./app").then((module) => {
    return new Promise((resolve) => {
      setTimeout(() => {
        resolve(module as any);
      }, 10000);

Now, it won't load that JS chunk until 10 seconds later. Only temporarily, in local development.

I know it's admittedly just a hack but it's nifty. Just don't forget to undo it when you're done simulating your snail-speed web app.

PS. That resolve(module as any); is for TypeScript. You can just change that to resolve(module); if it's regular JavaScript.

Umlauts (non-ascii characters) with git on macOS

22 March 2021 0 comments   Python, MacOSX

I edit a file called files/en-us/glossary/bézier_curve/index.html and then type git status and I get this:

▶ git status
Changes not staged for commit:
    modified:   "files/en-us/glossary/b\303\251zier_curve/index.html"


What's that?! First of all, I actually had this wrapped in a Python script that uses GitPython to analyze the output of for change in repo.index.diff(None):. So I got...

FileNotFoundError: [Errno 2] No such file or directory: '"files/en-us/glossary/b\\303\\251zier_curve/index.html"'

What's that?!

At first, I thought it was something wrong with how I use GitPython and thought I could force some sort of conversion to UTF-8 with Python. That, and to strip the quotation parts with something like path = path[1:-1] if path.startwith('"') else path

After much googling and experimentation, what totally solved all my problems was to run:

▶ git config --global core.quotePath false

Now you get...:

▶ git status
Changes not staged for commit:
    modified:   files/en-us/glossary/bézier_curve/index.html


And that also means it works perfectly fine with any GitPython code that does something with the repo.index.diff(None) or repo.index.diff(repo.head.commit).

Also, we I use the git-diff-action GitHub Action which would fail to spot files that contained umlauts but now I run this:

       - uses: actions/checkout@v2
+      - name: Config git core.quotePath
+        run: git config --global core.quotePath false
       - uses: technote-space/get-diff-action@v4.0.6
         id: git_diff_content

In JavaScript (Node) which is fastest, generator function or a big array function?

05 March 2021 0 comments   Node, JavaScript

Sorry about the weird title of this blog post. Not sure what else to call it.

I have a function that recursively traverses the file system. You can iterate over this function to do something with each found file on disk. Silly example:

for (const filePath of walker("/lots/of/files/here")) {
  count += filePath.length;

The implementation looks like this:

function* walker(root) {
  const files = fs.readdirSync(root);
  for (const name of files) {
    const filepath = path.join(root, name);
    const isDirectory = fs.statSync(filepath).isDirectory();
    if (isDirectory) {
      yield* walker(filepath);
    } else {
      yield filepath;

But I wondered; is it faster to not use a generator function since there might an overhead in swapping from the generator to whatever callback does something with each yielded thing. A pure big-array function looks like this:

function walker(root) {
  const files = fs.readdirSync(root);
  const all = [];
  for (const name of files) {
    const filepath = path.join(root, name);
    const isDirectory = fs.statSync(filepath).isDirectory();
    if (isDirectory) {
    } else {
  return all;

It gets the same result/outcome.

It's hard to measure this but I pointed it to some large directory with many files and did something silly with each one just to make sure it does something:

const label = "generator";
let count = 0;
for (const filePath of walker(SEARCH_ROOT)) {
  count += filePath.length;
const heapBytes = process.memoryUsage().heapUsed;
console.log(`HEAP: ${(heapBytes / 1024.0).toFixed(1)}KB`);

I ran it a bunch of times. After a while, the numbers settle and you get:

In other words, no speed difference.

Obviously building up a massive array in memory will increase the heap memory usage. Taking a snapshot at the end of the run and printing it each time, you can see that...


The potential swap overhead for a Node generator function is absolutely minuscule. At least in contexts similar to mine.

It's not unexpected that the generator function bounds less heap memory because it doesn't build up a big array at all.

How MDN's site-search works

26 February 2021 3 comments   Web development, Django, Python, MDN

tl;dr; Periodically, the whole of MDN is built, by our Node code, in a GitHub Action. A Python script bulk-publishes this to Elasticsearch. Our Django server queries the same Elasticsearch via /api/v1/search. The site-search page is a static single-page app that sends XHR requests to the /api/v1/search endpoint. Search results' sort-order is determined by match and "popularity".


The challenge with "Jamstack" websites is with data that is too vast and dynamic that it doesn't make sense to build statically. Search is one of those. For the record, as of Feb 2021, MDN consists of 11,619 documents (aka. articles) in English. Roughly another 40,000 translated documents. In English alone, there are 5.3 million words. So to build a good search experience we need to, as a static site build side-effect, index all of this in a full-text search database. And Elasticsearch is one such database and it's good. In particular, Elasticsearch is something MDN is already quite familiar with because it's what was used from within the Django app when MDN was a wiki.

Note: MDN gets about 20k site-searches per day from within the site.



When we build the whole site, it's a script that basically loops over all the raw content, applies macros and fixes, dumps one index.html (via React server-side rendering) and one index.json. The index.json contains all the fully rendered text (as HTML!) in blocks of "prose". It looks something like this:

  "doc": {
    "title": "DOCUMENT TITLE",
    "summary": "DOCUMENT SUMMARY",
    "body": [
        "type": "prose", 
        "value": {
          "id": "introduction", 
          "title": "INTRODUCTION",
          "content": "<p>FIRST BLOCK OF TEXTS</p>"
   "popularity": 0.12345,

You can see one here: /en-US/docs/Web/index.json


Next, after all the index.json files have been produced, a Python script takes over and it traverses all the index.json files and based on that structure it figures out the, title, summary, and the whole body (as HTML).

Next up, before sending this into the bulk-publisher in Elasticsearch it strips the HTML. It's a bit more than just turning <p>Some <em>cool</em> text.</p> to Some cool text. because it also cleans up things like <div class="hidden"> and certain <div class="notecard warning"> blocks.

One thing worth noting is that this whole thing runs roughly every 24 hours and then it builds everything. But what if, between two runs, a certain page has been removed (or moved), how do you remove what was previously added to Elasticsearch? The solution is simple: it deletes and re-creates the index from scratch every day. The whole bulk-publish takes a while so right after the index has been deleted, the searches won't be that great. Someone could be unlucky in that they're searching MDN a couple of seconds after the index was deleted and now waiting for it to build up again.
It's an unfortunate reality but it's a risk worth taking for the sake of simplicity. Also, most people are searching for things in English and specifically the Web/ tree so the bulk-publishing is done in a way the most popular content is bulk-published first and the rest was done after. Here's what the build output logs:

Found 50,461 (potential) documents to index
Deleting any possible existing index and creating a new one called mdn_docs
Took 3m 35s to index 50,362 documents. Approximately 234.1 docs/second
Counts per priority prefixes:
    en-us/docs/web                 9,056
    *rest*                         41,306

So, yes, for 3m 35s there's stuff missing from the index and some unlucky few will get fewer search results than they should. But we can optimize this in the future.


The way you connect to Elasticsearch is simply by a URL it looks something like this:

It's an Elasticsearch cluster managed by Elastic running inside AWS. Our job is to make sure that we put the exact same URL in our GitHub Action ("the writer") as we put it into our Django server ("the reader").
In fact, we have 3 Elastic clusters: Prod, Stage, Dev.
And we have 2 Django servers: Prod, Stage.
So we just need to carefully make sure the secrets are set correctly to match the right environment.

Now, in the Django server, we just need to convert a request like GET /api/v1/search?q=foo&locale=fr (for example) to a query to send to Elasticsearch. We have a simple Django view function that validates the query string parameters, does some rate-limiting, creates a query (using elasticsearch-dsl) and packages the Elasticsearch results back to JSON.

How we make that query is important. In here lies the most important feature of the search; how it sorts results.

In one simple explanation, the sort order is a combination of popularity and "matchness". The assumption is that most people want the popular content. I.e. they search for foreach and mean to go to /en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/forEach not /en-US/docs/Web/API/NodeList/forEach both of which contains forEach in the title. The "popularity" is based on Google Analytics pageviews which we download periodically, normalize into a floating-point number between 1 and 0. At the of writing the scoring function does something like this:

rank = doc.popularity * 10 + search.score

This seems to produce pretty reasonable results.

But there's more to the "matchness" too. Elasticsearch has its own API for defining boosting and the way we apply is:

This is then applied on top of whatever else Elasticsearch does such as "Term Frequency" and "Inverse Document Frequency" (tf and if). This article is a helpful introduction.

We're most likely not done with this. There's probably a lot more we can do to tune this myriad of knobs and sliders to get the best possible ranking of documents that match.

Web UI

The last piece of the puzzle is how we display all of this to the user. The way it works is that$locale/search returns a static page that is blank. As soon as the page has loaded, it lazy-loads JavaScript that can actually issue the XHR request to get and display search results. The code looks something like this:

function SearchResults() {
  const [searchParams] = useSearchParams();
  const sp = createSearchParams(searchParams);
  // add defaults and stuff here
  const fetchURL = `/api/v1/search?${sp.toString()}`;

  const { data, error } = useSWR(
    async (url) => {
      const response = await fetch(URL);
      // various checks on the response.statusCode here
      return await response.json();

  // render 'data' or 'error' accordingly here

A lot of interesting details are omitted from this code snippet. You have to check it out for yourself to get a more up-to-date insight into how it actually works. But basically, the window.location (and pushState) query string drives the fetch() call and then all the component has to do is display the search results with some highlighting.

The /api/v1/search endpoint also runs a suggestion query as part of the main search query. This extracts out interest alternative search queries. These are filtered and scored and we issue "sub-queries" just to get a count for each. Now we can do one of those "Did you mean...". For example: search for intersections.

In conclusion

There are a lot of interesting, important, and careful details that are glossed over here in this blog post. It's a constantly evolving system and we're constantly trying to improve and perfect the system in a way that it fits what users expect.

A lot of people reach MDN via a Google search (e.g. mdn array foreach) but despite that, nearly 5% of all traffic on MDN is the site-search functionality. The /$locale/search?... endpoint is the most frequently viewed page of all of MDN. And having a good search engine that's reliable is nevertheless important. By owning and controlling the whole pipeline allows us to do specific things that are unique to MDN that other websites don't need. For example, we index a lot of raw HTML (e.g. <video>) and we have code snippets that needs to be searchable.

Hopefully, the MDN site-search will elevate from being known to be very limited to something now that can genuinely help people get to the exact page better than Google can. Yes, it's worth aiming high!