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.
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:
steps:
- 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
with:
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) {
all.push(...walker(filepath));
} else {
all.push(filepath);
}
}
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";
console.time(label);
let count = 0;
for (const filePath of walker(SEARCH_ROOT)) {
count += filePath.length;
}
console.timeEnd(label);
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:
- Generator function: (median time) 1.74s
- Big array function: (median time) 1.73s
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...
- Generator function: (median heap memory) 4.9MB
- Big array function: (median heap memory) 13.9MB
Conclusion
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.
26 February 2021
3 comments
Web development,
Django,
Python,
MDN,
Elasticsearch
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".
Jamstack'ing
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.
Build
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
Indexing
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.
Searching
The way you connect to Elasticsearch is simply by a URL it looks something like this:
https://USER:PASSWD@HASH.us-west-2.aws.found.io:9243
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:
- match phrase in the
title: Boost = 10.0
- match phrase in the
body: Boost = 5.0
- match in
title: Boost = 2.0
- match in
body: Boost = 1.0
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 developer.mozilla.org/$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?${sp.toString()}`;
const { data, error } = useSWR(
fetchURL,
async (url) => {
const response = await fetch(URL);
// various checks on the response.statusCode here
return await response.json();
}
);
// render 'data' or 'error' accordingly here
A lot of interesting details are omitted from this code snippet. You have to check it out for yourself to get a more up-to-date insight into how it actually works. But basically, the window.location (and pushState) query string drives the fetch() call and then all the component has to do is display the search results with some highlighting.
The /api/v1/search endpoint also runs a suggestion query as part of the main search query. This extracts out interest alternative search queries. These are filtered and scored and we issue "sub-queries" just to get a count for each. Now we can do one of those "Did you mean...". For example: search for intersections.
In conclusion
There are a lot of interesting, important, and careful details that are glossed over here in this blog post. It's a constantly evolving system and we're constantly trying to improve and perfect the system in a way that it fits what users expect.
A lot of people reach MDN via a Google search (e.g. mdn array foreach) but despite that, nearly 5% of all traffic on MDN is the site-search functionality. The /$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!
25 February 2021
0 comments
Node,
JavaScript
tl;dr; polka is the lightest Node HTTP server package.
Highly unscientific but nevertheless worth writing down. Lightest here refers to the eventual weight added to the node_modules directory which is a reflection of network and disk use.
When you write a serious web server in Node you probably don't care about which one is lightest. It's probably more important which ones are actively maintained, reliable, well documented, and generally "more familiar". However, I was interested in setting up a little Node HTTP server for the benefit of wrapping some HTTP endpoints for an integration test suite.
The test
In a fresh new directory, right after having run: yarn init -y run the yarn add ... and see how big the node_modules directory becomes afterward (du -sh node_modules).
The results
polka: 116K koa: 1.7M express: 2.4M fastify: 8.0M
Conclusion
polka is the lightest. But I'm not so sure it matters.
But it could if this has to be installed a lot. For example, in CI where you run that yarn install a lot. Then it might save quite a bit of electricity for the planet.
13 February 2021
0 comments
Node,
JavaScript
There are a lot of 'rss' related NPM packages but I think I've found a combination that is great for parsing RSS feeds. Something that takes up the minimal node_modules and works great. I think the killer combination is
The code impressively simple:
const got = require("got");
const parser = require("fast-xml-parser");
(async function main() {
const buffer = await got("https://hacks.mozilla.org/feed/", {
responseType: "buffer",
resolveBodyOnly: true,
timeout: 5000,
retry: 5,
});
var feed = parser.parse(buffer.toString());
for (const item of feed.rss.channel.item) {
console.log({ title: item.title, url: item.link });
break;
}
})();
// Outputs...
// {
// title: 'MDN localization update, February 2021',
// url: 'https://hacks.mozilla.org/2021/02/mdn-localization-update-february-2021/'
// }
I like about fast-xml-parser is that it has no dependencies. And it's tiny:
▶ du -sh node_modules/fast-xml-parser
104K node_modules/fast-xml-parser
The got package is quite a bit larger and has more dependencies. But I still love it. It's proven itself to be very reliable and very pleasant API. Both packages support TypeScript too.
A particular detail I like about fast-xml-parser is that it doesn't try to do the downloading part too. This way, I can use my own preferred library and I could potentially write my own caching code if I want to protect against flaky network.
