Peterbe.com

A blog and website by Peter Bengtsson

How to get all of MDN Web Docs running locally

09 June 2021 0 comments   Web development, MDN


tl;dr; git clone https://github.com/mdn/content.git && cd content && yarn install && yarn start && open http://localhost:5000/ will get you all of MDN Web Docs running on your laptop.

The MDN Web Docs is built from a git repository: github.com/mdn/content. It contains all you need to get all the content running locally. Including search. Embedded inside that repository is a package.json which helps you start a Yari server. Aka. the preview server. It's a static build of the github.com/mdn/yari project which handles client-side rendering, search, an just-in-time server-side rendering server.

Basics

All you need is the following:

▶ git clone https://github.com/mdn/content.git
▶ cd content
▶ yarn install
▶ yarn start

And now open http://localhost:5000 in your browser.

This will now run in "preview server" mode. It's meant for contributors (and core writers) to use when they're working on a git branch. Because of that, you'll see a "Writer's homepage" at the root URL. And when viewing each document, you get buttons about "flaws" and stuff. Looks like this:

Preview server

Alternative ways to download

If you don't want to use git clone you can download the ZIP file. For example:

▶ wget https://github.com/mdn/content/archive/refs/heads/main.zip
▶ unzip main.zip
▶ cd content-main
▶ yarn install
▶ yarn start

At the time of writing, the downloaded Zip file is 86MB and unzipped the directory is 278MB on disk.

When you use git clone, by default it will download all the git history. That can actually be useful. This way, when rendering each document, it can figure out from the git logs when each individual document was last modified. For example:

"Last modified"

If you don't care about the "Last modified" date, you can do a "shallow git clone" instead. Replace the above-mentioned first command with:

▶ git clone --depth 1 https://github.com/mdn/content.git

At the time of writing the shallow cloned content folder becomes 234MB instead of (the deep clone) 302MB.

Just the raw rendered data

Every MDN Web Docs page has an index.json equivalent. Take any MDN page and add /index.json to the URL. For example /en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/slice/index.json

Essentially, this is the intermediate state that's used for server-side rendering the page. A glorified way of sandwiching the content in a header, a footer, and a sidebar to the side. These URLs work on localhost:5000 too. Try http://localhost:5000/en-US/docs/Web/API/Fetch_API/Using_Fetch/index.json for example.

The content for that index.json is built just in time. It also contains a bunch of extra metadata about "flaws"; a system used to highlight things that should be fixed that is somewhat easy to automate. So, it doesn't contain things like spelling mistakes or code snippets that are actually invalid.

But suppose you want all that raw (rendered) data, without any of the flaw detections, you can run this command:

▶ BUILD_FLAW_LEVELS="*:ignore" yarn build

It'll take a while (because it produces an index.html file too). But now you have all the index.json files for everything in the newly created ./build/ directory. It should have created a lot of files:

▶ find build -name index.json | wc -l
   11649

If you just want a subtree of files you could have run it like this instead:

▶ BUILD_FOLDERSEARCH=web/javascript BUILD_FLAW_LEVELS="*:ignore" yarn build

Programmatic API access

The programmatic APIs are all about finding the source files. But you can use the sources to turn that into the built files you might need. Or just to get a list of URLs. To get started, create a file called find-files.js in the root:

const { Document } = require("@mdn/yari/content");

console.log(Document.findAll().count);

Now, run it like this:

▶ export CONTENT_ROOT=files

▶ node find-files.js
11649

Other things you can do with that findAll function:

const { Document } = require("@mdn/yari/content");

const found = Document.findAll({
  folderSearch: "web/javascript/reference/statements/f",
});
for (const document of found.iter()) {
  console.log(document.url);
}

Or, suppose you want to actually build each of these that you find:

const { Document } = require("@mdn/yari/content");
const { buildDocument } = require("@mdn/yari/build");

const found = Document.findAll({
  folderSearch: "web/javascript/reference/statements/f",
});

Promise.all([...found.iter()].map((document) => buildDocument(document))).then(
  (built) => {
    for (const { doc } of built) {
      console.log(doc.title.padEnd(20), doc.popularity);
    }
  }
);

That'll output something like this:

▶ node find-files.js
for                  0.0143
for await...of       0.0129
for...in             0.0748
for...of             0.0531
function declaration 0.0088
function*            0.0122

All the HTML content in production-grade mode

In the most basic form, it will start the "preview server" which is tailored towards building just in time and has all those buttons at the top for writers/contributors. If you want the more "production-grade" version, you can't use the copy of @mdn/yari that is "included" in the mdn/content repo. To do this, you need to git clone mdn/yari and install that. Hang on, this is about to get a bit more advanced:

▶ git clone https://github.com/mdn/yari.git
▶ cd yari
▶ yarn install
▶ yarn build:client
▶ yarn build:ssr
▶ CONTENT_ROOT=../files REACT_APP_DISABLE_AUTH=true BUILD_FLAW_LEVELS="*:ignore" yarn build
▶ CONTENT_ROOT=../files node server/static.js

Now, if you go to something like http://localhost:5000/en-US/docs/Web/Guide/ you'll get the same thing as you get on https://developer.mozilla.org but all on your laptop. Should be pretty snappy.

Is it really entirely offline?

No, it leaks a little. For example, there are interactive examples that uses an iframe that's hardcoded to https://interactive-examples.mdn.mozilla.net/.

There are also external images for example. You might get a live sample that refers to sample images on https://mdn.mozillademos.org/files/.... So that'll fail if you're without WiFi in a space ship.

Conclusion

Making all of MDN Web Docs available offline is, honestly, not a priority. The focus is on A) a secure production build, and B) a good environment for previewing content changes. But all the pieces are there. Search is a little bit tricky, as an example. When you're running it as a preview server you can't do a full-text search on all the content, but you get a useful autocomplete search widget for navigating between different titles. And the full-text search engine is a remote centralized server that you can't take with you offline.

But all the pieces are there. Somehow. It all depends on your use case and what you're willing to "compromise" on.

The correct way to index data into Elasticsearch with (Python) elasticsearch-dsl

14 May 2021 0 comments   Python, MDN, Elasticsearch


This is how MDN Web Docs uses Elasticsearch. Daily, we build all the content and then upload it all using elasticsearch-dsl using aliases. Because there are no good complete guides to do this, I thought I'd write it down for the next person who needs to do something similar. Let's jump straight into the code. The reader will need a healthy dose of imagination to fill in their details.

Indexing

# models.py

from datetime.datetime import utcnow

from elasticsearch_dsl import Document

PREFIX = "myprefix"


class MyDocument(Document):
    title = Text()
    body = Text()
    # ...

    class Index:
        name = (
            f'{PREFIX}_{utcnow().strftime("%Y%m%d%H%M%S")}'
        )

What's important to note here is that the MyDocument.Index.name is dynamically allocated every single time the module is imported. It's not very important exactly what it is called but it's important that it becomes unique each time.
This means that when you start using MyDocument it will automatically figure out which index to use. Now, it's time to create the index and bulk publish it.

# index.py
# Note! This example code skips over things like progress bars
# and verbose logging and misc sanity checks and stuff.

from elasticsearch.helpers import parallel_bulk
from elasticsearch_dsl import Index
from elasticsearch_dsl.connections import connections

from .models import MyDocument, PREFIX


def index(buildroot: Path, url: str, update=False):
    """
    * 'buildroot' is where the files are we're going to read and index
    * 'url' is the host URL for the Elasticsearch server
    * 'update' is if just want to "cake on" a couple of documents 
      instead of starting over and doing a complete indexing.
    """

    # Connect and stuff
    connections.create_connection(hosts=[url], retry_on_timeout=True)
    connection = connections.get_connection()
    health = connection.cluster.health()
    status = health["status"]
    if status not in ("green", "yellow"):
        raise Exception(f"status {status} not green or yellow")

    if update:
        for name in connection.indices.get_alias():
            if name.startswith(f"{PREFIX}_"):
                document_index = Index(name)
                break
        else:
            raise IndexAliasError(
                f"Unable to find an index called {PREFIX}_*"
            )

    else:
        # Confusingly, `._index` is actually not a private API.
        # It's the documented way you're supposed to reach it.
        document_index = MyDocument._index
        document_index.create()

    def generator():
        for doc in Path(buildroot):
            # The reason for specifying the exact index name is that we might
            # be doing an update and if you don't specify it, elasticsearch_dsl
            # will fall back to using whatever Document._meta.Index automatically
            # becomes in this moment.
            yield to_search(doc, _index=document_index._name).to_dict(True)

    for success, info in parallel_bulk(connection, generator()):
        # 'success' is a boolean
        # 'info' has stuff like:
        #  - info["index"]["error"]
        #  - info["index"]["_shards"]["successful"]
        #  - info["index"]["_shards"]["failed"]
        pass

    if update:
        # When you do an update, Elasticsearch will internally delete the
        # previous docs (based on the _id primary key we set).
        # Normally, Elasticsearch will do this when you restart the cluster
        # but that's not something we usually do.
        # See https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-forcemerge.html
        document_index.forcemerge()
    else:
        # Now we're going to bundle the change to set the alias to point
        # to the new index and delete all old indexes.
        # The reason for doing this together in one update is to make it atomic.
        alias_updates = [
            {"add": {"index": document_index._name, "alias": PREFIX}}
        ]
        for index_name in connection.indices.get_alias():
            if index_name.startswith(f"{PREFIX}_"):
                if index_name != document_index._name:
                    alias_updates.append({"remove_index": {"index": index_name}})
        connection.indices.update_aliases({"actions": alias_updates})

    print("All done!")



def to_search(file: Path, _index=None):
    with open(file) as f:
        data = json.load(f)
    return MyDocument(
        _index=_index,
        _id=data["identifier"],
        title=data["title"],
        body=data["body"]
    )

A lot is left to the reader as an exercise to fill in but these are the most important operations. It demonstrates how you can

  1. Correctly create indexes
  2. Atomically create an alias and clean up old indexes (and aliases)
  3. How you can add to an existing index

After you've run this you'll see something like this:

$ curl http://localhost:9200/_cat/indices?v
...
health status index                   uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   myprefix_20210514141421 vulVt5EKRW2MNV47j403Mw   1   1      11629            0     28.7mb         28.7mb

$ curl http://localhost:9200/_cat/aliases?v
...
alias    index                   filter routing.index routing.search is_write_index
myprefix myprefix_20210514141421 -      -             -              -

Searching

When it comes to using the index, well, it depends on where your code for that is. For example, on MDN Web Docs, the code that searches the index is in an entirely different code-base. It's incidentally Python (and elasticsearch-dsl) in both places but other than that they have nothing in common. So for the searching, you need to manually make sure you write down the name of the index (or name of the alias if you prefer) into the code that searches. For example:

from elasticsearch_dsl import Search

def search(params):
    search_query = Search(index=settings.SEARCH_INDEX_NAME)

    # Do stuff to 'search_query' based on 'params'

    response = search_query.execute()   
    for hit in response:
        # ...

If you're within the same code that has that models.MyDocument in the first example code above, you can simply do things like this:

from elasticsearch_dsl import Index
from elasticsearch_dsl.connections import connections

from .models import PREFIX


def analyze(
    url: str,
    text: str,
    analyzer: str,
):
    connections.create_connection(hosts=[url])
    index = Index(PREFIX)
    analysis = index.analyze(body={"text": text, "analyzer": analyzer})
    # ...

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

https://github.com/mdn/yari/issues/3611


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">

...with...

<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:

    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: