Peterbe.com

tl;dr; <img src="/avatar.random.png" alt="Random avataaar"> generates this image:

(try reloading to get a random new one. funny aren't they?)

When you use Gravatar you can convert people's email addresses to their mugshot.
It works like this:

<img src="https://www.gravatar.com/avatar/$(md5(user.email))">

But most people don't have their mugshot on Gravatar.com unfortunately. But you still want to display an avatar that is distinct per user. Your best option is to generate one and just use the user's name or email as a seed (so it's always random but always deterministic for the same user). And you can also supply a fallback image to Gravatar that they use if the email doesn't match any email they have. That's where this blog post comes in.

I needed that so I shopped around and found avataaars generator which is available as a React component. But I need it to be server-side and in Python. And thankfully there's a great port called: py-avataaars.

It depends on CairoSVG to convert an SVG to a PNG but it's easy to install. Anyway, here's my hack to generate random "avataaars" from Django:

import io
import random

import py_avataaars
from django import http
from django.utils.cache import add_never_cache_headers, patch_cache_control


def avatar_image(request, seed=None):
    if not seed:
        seed = request.GET.get("seed") or "random"

    if seed != "random":
        random.seed(seed)

    bytes = io.BytesIO()

    def r(enum_):
        return random.choice(list(enum_))

    avatar = py_avataaars.PyAvataaar(
        style=py_avataaars.AvatarStyle.CIRCLE,
        # style=py_avataaars.AvatarStyle.TRANSPARENT,
        skin_color=r(py_avataaars.SkinColor),
        hair_color=r(py_avataaars.HairColor),
        facial_hair_type=r(py_avataaars.FacialHairType),
        facial_hair_color=r(py_avataaars.FacialHairColor),
        top_type=r(py_avataaars.TopType),
        hat_color=r(py_avataaars.ClotheColor),
        mouth_type=r(py_avataaars.MouthType),
        eye_type=r(py_avataaars.EyesType),
        eyebrow_type=r(py_avataaars.EyebrowType),
        nose_type=r(py_avataaars.NoseType),
        accessories_type=r(py_avataaars.AccessoriesType),
        clothe_type=r(py_avataaars.ClotheType),
        clothe_color=r(py_avataaars.ClotheColor),
        clothe_graphic_type=r(py_avataaars.ClotheGraphicType),
    )
    avatar.render_png_file(bytes)

    response = http.HttpResponse(bytes.getvalue())
    response["content-type"] = "image/png"
    if seed == "random":
        add_never_cache_headers(response)
    else:
        patch_cache_control(response, max_age=60, public=True)

    return response

It's not perfect but it works. The URL to this endpoint is /avatar.<seed>.png and if you make the seed parameter random the response is always different.

To make the image not random, you replace the <seed> with any string. For example (use your imagination):

{% for comment in comments %}
  <img src="/avatar.{{ comment.user.id }}.png" alt="{{ comment.user.name }}">
  <blockquote>{{ comment.text }}</blockquote>
  <i>{{ comment.date }}</i>
{% endfor %}

tl;dr That's Groce! is: A mobile web app to help families do grocery shopping and meal planning. Developed out of necessity by a family (Peter and Ashley) and used daily in their home.

Hopefully, the About page explains what it does.

Screenshot of a sample list

The backstory

We used to use Wunderlist, but that stopped working. Next, we tried Cozi and that worked for a while but it was buggy and annoying in so many ways. Finally, we gave up and decided to build our own. Exactly how we need it to be, as efficient as possible.

We also tried a couple of regular to-do list apps where you can have shared accounts but we wanted something perfectly tailored towards the specific needs of family grocery shopping (and meal planning). That's how That's Groce! was born.

The killer features

The about page does a good job of listing the killer features but let's emphasize it one more time.

• Free and simple.
• Is really smart about suggestions and auto-complete for speedy entry.
• Works offline (our usual grocery store has no reception unless you brave onto their WiFi).
• Is real-time, so every other device updates immediately as you update or add something on your device.
• Shared list; you can have your own list but you can invite co-owners.
• You can sort your grocery list in the same order you usually walk through your grocery store.

It's not an app store app

You won't find it on the Apple App store. It's a web app that's been tailored to work well in mobile web browsers (iOS Safari) and you can use the "Add to Home screen" so it looks and acts like a regular app.
It would be nice to try to make it a regular native mobile app but that takes significant time which is hard to find but certainly something to aspire to if it can be done in a nice way.

"Really smart about suggestions"

What does that killer feature mean? (At the time of writing (Oct 2020), it isn't launched yet but the pieces are coming together.) Are there certain stables you buy recurringly? Like milk or bananas or Cheerios. If the app can start to see a pattern of commonly added items, it can suggest it immediately so when you're making your list on Monday morning, you just need to tap to add those.

Another important thing is that as you type, it can suggest many things based on the first or couple of characters you type in, but you can't suggest every single possible word so which one should you suggest first?
The way That's Groce! works is that it learns based on the number of times and how recently you add something to your list. As of today, look what happens when I type a on my list:

When I type a it suggests things that start with "A" but based on frequency.

The more you use it, the better the suggestions get.

Also, to get you started, over 100 items are preloaded as good suggestions but that's just to get you up and running. Once your family starts to use it, your own suggestions get better and better over time.

"Same order you usually walk through your grocery store"

This was important to us because we found we walk through the aisles in pretty much the same way. Every time. When you walk in you have your produce (veggies first, then fruit, then salad stuff) on the right. Then baked goods and deli. Then meats and alcohol. Etc. So if you can group your items based on these descriptions you can be really efficient with your list and it becomes a lot easier to cross off sections of the store and not have to scroll up and down or having to walk back to pick up that pizza dough all the way back at the deli section.

For this to work, you need to type in groups for your items. But you can call them whatever you like. If you want to type "Aisle 1", "Aisle 2", "Dairy stuff" you can. It's all up to you. Keep in mind that it might feel like a bit of up-front work at first, and it is, but your list is learning so you essentially only have to do it once.

Don't be a slave to your list!

If you do decide to try it, keep one thing in mind: You're in control. You don't need to type in perfect descriptions, amounts, groups, and quantities. If you don't know how to spell "bee-ar-naise sauce", don't worry about it. It's your list. You can type whatever you want or need. A lot of to-do lists invite you with complex options to organize the hell out of your list items. Don't do that. Think of That's Groce! as a fridge post-it note that you and your partner keep in their pocket that automatically synchronizes.

You can help

We built this for ourselves but it's built in a way that any family can use it and hopefully also be better organized. But once you sign in you can submit feedback for suggestions. And if you're into coding, the whole app is Open Source so it's fairly easy to modify the code or even host it yourself if you wanted to: https://github.com/peterbe/groce/

Also, if you do try it and like it, please consider going to the Share the ❤️ page and, you know, share it with friends. Much appreciated!

You can write your CSS so that it depends on images. Like this:

li.one {
  background-image: url("skull.png");
}

That means that the browser will do its best to style the li.one with what little it has from the CSS. Then, it'll ask the browser to go ahead and network download that skull.png URL.

But, another option is to embed the image as a data URL like this:

li.one{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIAAAACACAYAAADDPmHL...rkJggg==)

As a block of CSS, it's much larger but it's one less network call. What if you know that skull.png will be needed? Is it faster to inline it or to leave it as a URL? Let's see!

First of all, I wanted to get a feeling for how much larger an image is in bytes if you transform them to data URLs. Check out this script's output:

▶ ./bin/b64datauri.js src/*.png src/*.svg
src/lizard.png       43,551     58,090     1.3x
src/skull.png        7,870      10,518     1.3x
src/clippy.svg       483        670        1.4x
src/curve.svg        387        542        1.4x
src/dino.svg         909        1,238      1.4x
src/sprite.svg       10,330     13,802     1.3x
src/survey.svg       2,069      2,786      1.3x

Basically, as a blob of data URL, the images become about 1.3x larger. Hopefully, with HTTP2, the headers are cheap for each URL downloaded over the network, but it's not 0. (No idea what the CPU-work multiplier is)

Experiment assumptions and notes

• When you first calculate the critical CSS, you know that there's no url(data:mime/type;base64,....) that goes to waste. I.e. you didn't put that in the CSS file or HTML file, bloating it, for nothing.
• The images aren't too large. Mostly icons and fluff.
• If it's SVG images should probably inline them in the HTML natively so you can control their style.
• The HTML is compressed for best results.
• The server is HTTP2

It's a fairly commonly known fact that data URLs have a CPU cost. That base64 needs to be decoded before the image can be decoded by the renderer. So let's stick to fairly small images.

The experiment

I made a page that looks like this:

li {
  background-repeat: no-repeat;
  width: 150px;
  height: 150px;
  margin: 20px;
  background-size: contain;
}
li.one {
  background-image: url("skull.png");
}
li.two {
  background-image: url("dino.svg");
}
li.three {
  background-image: url("clippy.svg");
}
li.four {
  background-image: url("sprite.svg");
}
li.five {
  background-image: url("survey.svg");
}
li.six {
  background-image: url("curve.svg");
}

and

<ol>
  <li class="one">One</li>
  <li class="two">Two</li>
  <li class="three">Three</li>
  <li class="four">Four</li>
  <li class="five">Five</li>
  <li class="six">Six</li>
</ol>

See the whole page here

The page also uses Bootstrap to make it somewhat realistic. Then, using minimalcss combine the external CSS with the CSS inline and produce a page that is just HTML + 1 <style> tag.

Now, based on that page, the variant is that each url($URL) in the CSS gets converted to url(data:mime/type;base64,blablabla...). The HTML is gzipped (and brotli compressed) and put behind a CDN. The URLs are:

• https://www.peterbe.com/render-block-images-in-css/inlined.html - All CSS inlined but using image URLs.
• https://www.peterbe.com/render-block-images-in-css/inlined-datauri.html - All CSS inlined and all image URLs turn into data URLs.

Also, there's this page which is without the critical CSS inlined.

To appreciate what this means in terms of size on the HTML, let's compare:

• inlined.html with external URLs: 2,801 bytes (1,282 gzipped)
• inlined-datauris.html with data URLs: 32,289 bytes (17,177 gzipped)

Considering that gzip (accept-encoding: gzip,deflate) is almost always used by browsers, that means the page is 15KB more before it can be fully downloaded. (But, it's streamed so maybe the comparison is a bit flawed)

Analysis

WebPagetest.org results here. I love WebPagetest, but the results are usually a bit erratic to be a good enough for comparing. Maybe if you could do the visual comparison repeated times, but I don't think you can.

WebPagetest visual comparison

And the waterfalls...

With regular URLs

With data URLs

Fairly expected.

• With external image URLs, the browser will start to display the CSSOM before the images have downloaded. Meaning, the CSS is render-blocking, but the external images are not.

• The final result comes in sooner with data URLs.

• With data URLs you have to stare at a white screen longer.

Next up, using Google Chrome's Performance dev tools panel. Set to 6x CPU slowdown and online with Fast 3G.

I don't know how to demonstrate this other than screenshots:

Performance with external images

Performance with data URLs

Those screenshots are rough attempts at showing the area when it starts to display the images.

Whole Performance tab with external images

Whole Performance tab with data URLs

I ran these things 2 times and the results were pretty steady.

• With external images, fully loaded at about 2.5 seconds
• With data URLs, fully loaded at 1.9 seconds

I tried Lighthouse but the difference was indistinguishable.

Summary

Yes, inlining your CSS images is faster. But it's with a slim margin and the disadvantages aren't negligible.

This technique costs more CPU because there's a lot more base64 decoding to be done, and what if you have a big fat JavaScript bundle in there that wants a piece of the CPU? So ask yourself, how valuable is to not hog the CPU. Perhaps someone who understands the browser engines better can tell if the base64 decoding cost is spread nicely onto multiple CPUs or if it would stand in the way of the main thread.

What about anti-progressive rendering

When Facebook redesigned www.facebook.com in mid-2020 one of their conscious decisions was to inline the SVG glyphs into the JavaScript itself.

"To prevent flickering as icons come in after the rest of the content, we inline SVGs into the HTML using React rather than passing SVG files to <img> tags."

Although that comment was about SVGs in the DOM, from a JavaScript perspective, the point is nevertheless relevant to my experiment. If you look closely, at the screenshots above (or you open the URL yourself and hit reload with HTTP caching disabled) the net effect is that the late-loading images do cause a bit of "flicker". It's not flickering as in "now it's here", "now it's gone", "now it's back again". But it's flickering in that things are happening with progressive rendering. Your eyes might get tired and they say to your brain "Wake me up when the whole thing is finished. I can wait."

This topic quickly escalates into perceived performance which is a stratosphere of its own. And personally, I can only estimate and try to speak about my gut reactions.

In conclusion, there are advantages to using data URIs over external images in CSS. But please, first make sure you don't convert the image URLs in a big bloated .css file to data URLs if you're not sure they'll all be needed in the DOM.

Bonus!

If you're not convinced of the power of inlining the critical CSS, check out this WebPagetest run that includes the image where it references the whole bootstrap.min.css as before doing any other optimizations.

With baseline that isn't just the critical CSS

I'm working on a Firebase app called That's Groce! based on preact-cli, with TypeScript, and I wanted to see how it appears with or without Firestore and Authenticated lazy-loaded.

In the root, there's an app.tsx that used look like this:

import { FunctionalComponent, h } from "preact";
import { useState, useEffect } from "preact/hooks";

import firebase from "firebase/app";
import "firebase/auth";
import "firebase/firestore";

import { firebaseConfig } from "./firebaseconfig";

const app = firebase.initializeApp(firebaseConfig);

const App: FunctionalComponent = () => {
  const [auth, setAuth] = useState<firebase.auth.Auth | null>(null);
  const [db, setDB] = useState<firebase.firestore.Firestore | null>(null);

  useEffect(() => {
    const appAuth = app.auth();
    setAuth(appAuth);
    appAuth.onAuthStateChanged(authStateChanged);

    const db = firebase.firestore();
    setDB(db);
  }, []);

...

While this works, it does make a really large bundle when both firebase/firestore and firebase/auth imported in the main bundle. In fact, it looks like this:

▶ ls -lh build/*.esm.js
-rw-r--r--  1 peterbe  staff   510K Sep  1 14:13 build/bundle.0438b.esm.js
-rw-r--r--  1 peterbe  staff   5.0K Sep  1 14:13 build/polyfills.532e0.esm.js

510K is pretty hefty to have to ask the client to download immediately. It's loaded like this (in build/index.html):

<script crossorigin="anonymous" src="/bundle.0438b.esm.js" type="module"></script>
<script nomodule src="/polyfills.694cb.js"></script>
<script nomodule defer="defer" src="/bundle.a4a8b.js"></script>

To lazy-load this

To lazy-load the firebase/firestore and firebase/auth you do this instead:

...

const App: FunctionalComponent = () => {
  const [auth, setAuth] = useState<firebase.auth.Auth | null>(null);
  const [db, setDB] = useState<firebase.firestore.Firestore | null>(null);

  useEffect(() => {
    import("firebase/auth")
      .then(() => {
        const appAuth = app.auth();
        setAuth(appAuth);
        appAuth.onAuthStateChanged(authStateChanged);
      })
      .catch((error) => {
        console.error("Unable to lazy-load firebase/auth:", error);
      });

    import("firebase/firestore")
      .then(() => {
        const db = firebase.firestore();
        setDB(db);
      })
      .catch((error) => {
        console.error("Unable to lazy-load firebase/firestore:", error);
      });
  }, []);

...

Now it looks like this instead:

▶ ls -lh build/*.esm.js
-rw-r--r--  1 peterbe  staff   173K Sep  1 14:24 build/11.chunk.b8684.esm.js
-rw-r--r--  1 peterbe  staff   282K Sep  1 14:24 build/12.chunk.3c1c4.esm.js
-rw-r--r--  1 peterbe  staff    56K Sep  1 14:24 build/bundle.7225c.esm.js
-rw-r--r--  1 peterbe  staff   5.0K Sep  1 14:24 build/polyfills.532e0.esm.js

The total sum of all (relevant) .esm.js files is the same (minus a difference of 430 bytes).

But what does it really look like? The app is already based around that

const [db, setDB] = useState<firebase.firestore.Firestore | null>(null);

so it knows to wait until db is truthy and it displays a <Loading/> component until it's ready.

To test how it loads I used the Chrome Performance devtools with or without the lazy-loading and it's fairly self-explanatory:

Before, no lazy-loading

After, with lazy-loading

Clearly, the lazy-loaded has a nicer pattern in that it breaks up the work by the main thread.

Conclusion

It's fairly simple to do and it works. The main bundle becomes lighter and allows the browser to start rendering the Preact component sooner. But it's not entirely obvious if it's that much better. The same amount of JavaScript needs to downloaded and parsed no matter what. It's clearly working as a pattern but it's still pretty hard to judge if it's worth it. Now there's more "swapping".

And the whole page is server-side rendered anyway so in terms of immediate first-render it's probably the same. Hopefully, HTTP2 loading does the right thing but it's not yet entirely clear if the complete benefit is there. I certainly hope that this can improve the "Total Blocking Time" and "Time to Interactive".

The other important thing is that not all imports from firebase/* work in Node because they depend on window. It works for firebase/firestore and firestore/auth but not for firestore/analytics and firestore/performance. Now, I can add those lazy-loaded in the client and have the page rendered in Node for that initial build/index.html.

<datalist> is an underrated HTML API. It's basically a native autocomplete widget that requires 0 JavaScript. What I didn't know is how great it is on mobile devices. Especially the iOS Safari platform which is, to be honest, the only mobile device I have.

What's cool about it is that it's easy to implement, from a developer's point-of-view. But most importantly, it works great for users. The problem usually on mobile devices and autocomplete is that it's hard to find a good spot to display the suggestions. Most autocomplete widgets are a styled form of <div class="results"><ul><li>Suggestion 1</li><li>Suggestion 2</li></ul></div> that usually follows the <input> element. Usually, this simply boils down to screen height real estate. Oftentimes you want to display so much more rich stuff in the autocomplete results but it's hard to fit in a nice list of results between the <input> and the native keyboard display. For example, on this page ...

Note how the search results get hidden underneath the keyboard.

Demo

The cool thing about <datalist> is that gets embedded in the native mobile keyboard in a sense. But what's extra cool is that the browser will do an OK job of filtering all the options for you, so that you, as a developer, just need to supply all options and the browser will take care of the rest.

I put together a dead-simple app here: https://cnfyl.csb.app/ (source here) which looks like this on iOS:

Caveats

The space that the keyboard now populates with suggestions is usually reserved for helping you autocomplete regular words. It still does if you start typing a word that isn't an option. So arguably, the <datalist> options are primarily helping you when it's very likely that the user will type one of the suggestions.

The matching isn't great in my opinion. If you type "ea" it will match "Peaches" but I find it extremely unlikely that that's helping users. (What do you think?) If you've started typing "ea" if there's no match called "Each" or "North East" then it's probably better with no match at all.

Mind you, check out this hack (source here) which takes control of the <option> tags inside the <datalist> by having an event listener on the input. So if the input is "ea" it only matches expressions that are left-word-delimited and discard the rest.

Default/Native filtering

Custom filtering

Conclusion

It is without a doubt the simplest autocomplete functionality you can buy. I would buy it again.

Perhaps it's not right for every application. Perhaps it's important to be able to include images in your autocomplete suggestions. Either way, the best thing to do is to park this in the back of your mind till next time you're up against the need for some sort of assisted search or choice. Especially if you predict you'll have a lot of users on mobile devices.

minimalcss requires that you have your HTML in a serving HTTP web page so that puppeteer can open it to find out the CSS within. Suppose, in your build system, you don't yet really have a server. Well, what you can do is start one on-the-fly and shut it down as soon as you're done.

Suppose you have .html file

First install all the stuff:

yarn add minimalcss http-server

Then run it:

const path = require("path");

const minimalcss = require("minimalcss");
const httpServer = require("http-server");

const HTML_FILE = "index.html";  // THIS IS YOURS

(async () => {
  const server = httpServer.createServer({
    root: path.dirname(path.resolve(HTML_FILE)),
  });
  server.listen(8080);

  let result;
  try {
    result = await minimalcss.minimize({
      urls: ["http://0.0.0.0:8080/" + HTML_FILE],
    });
  } catch (err) {
    console.error(err);
    throw err;
  } finally {
    server.close();
  }

  console.log(result.finalCss);
})();

And the index.html file:

<!DOCTYPE html>
<html>
    <head>
        <link rel="stylesheet" href="styles.css">
    </head>
    <body>
        <p>Hi @peterbe</p>
    </body>
</html>

And the styles.css file:

h1 {
  color: red;
}
p,
li {
  font-weight: bold;
}

And the output from running that Node script:

p{font-weight:700}

It works!

Suppose all you have is the HTML string and the CSS blob(s)

Suppose all you have is a string of HTML and a list of strings of CSS:

const fs = require("fs");
const path = require("path");

const minimalcss = require("minimalcss");
const httpServer = require("http-server");

const HTML_BODY = `
<p>Hi Peterbe</p>
`;

const CSSes = [
  `
h1 {
  color: red;
}
p,
li {
  font-weight: bold;
}
`,
];

(async () => {
  const csses = CSSes.map((css, i) => {
    fs.writeFileSync(`${i}.css`, css);
    return `<link rel="stylesheet" href="${i}.css">`;
  });
  const html = `<!doctype html><html>
  <head>${csses}</head>
  <body>${HTML_BODY}</body>
  </html>`;
  const fp = path.resolve("./index.html");
  fs.writeFileSync(fp, html);
  const server = httpServer.createServer({
    root: path.dirname(fp),
  });
  server.listen(8080);

  let result;
  try {
    result = await minimalcss.minimize({
      urls: ["http://0.0.0.0:8080/" + path.basename(fp)],
    });
  } catch (err) {
    console.error(err);
    throw err;
  } finally {
    server.close();
    fs.unlinkSync(fp);
    CSSes.forEach((_, i) => fs.unlinkSync(`${i}.css`));
  }

  console.log(result.finalCss);
})();

Truth be told, you'll need a good pinch of salt to appreciate that example code. It works but most likely, if you're into web performance so much that you're even doing this, your parameters are likely to be more complex.

Suppose you have your own puppeteer instance

In the first example above, minimalcss will create an instance of puppeteer (e.g. const browser = await puppeteer.launch()) but that means you have less control over which version of puppeteer or which parameters you need. Also, if you have to run minimalcss on a bunch of pages it's costly to have to create and destroy puppeteer browser instances repeatedly.

To modify the original example, here's how you use your own instance of puppeteer:

  const path = require("path");

+ const puppeteer = require("puppeteer");
  const minimalcss = require("minimalcss");
  const httpServer = require("http-server");

  const HTML_FILE = "index.html"; // THIS IS YOURS

  (async () => {
    const server = httpServer.createServer({
      root: path.dirname(path.resolve(HTML_FILE)),
    });
    server.listen(8080);

+   const browser = await puppeteer.launch(/* your special options */);
+
    let result;
    try {
      result = await minimalcss.minimize({
        urls: ["http://0.0.0.0:8080/" + HTML_FILE],
+       browser,
      });
    } catch (err) {
      console.error(err);
      throw err;
    } finally {
+     await browser.close();
      server.close();
    }

    console.log(result.finalCss);
  })();

Note that this doesn't buy us anything in this particular example. But that's where your imagination comes in!

Conclusion

You can see the code here as a git repo if that helps.

The point is that this might solve some of the chicken-and-egg problem you might have is that you're building your perfect HTML + CSS and you want to perfect it before you ship it.

Note also that there are other ways to run minimalcss other than programmatically. For example, minimalcss-server is minimalcss wrapped in a express server.

Another thing that you might have is that you have multiple .html files that you want to process. The same technique applies but you just need to turn it into a loop and make sure you call server.close() (and optionally await browser.close()) when you know you've processed the last file. Exercise left to the reader?

Django's Form framework is excellent. It's intuitive and versatile and, best of all, easy to use. However, one little thing that is not so intuitive is how do you render a bound form with default/initial values when the form is never rendered unbound.

If you do this in Django:

class MyForm(forms.Form):
    name = forms.CharField(required=False)

def view(request):
    form = MyForm(initial={'name': 'Peter'})
    return render(request, 'page.html', form=form)

# Imagine, in 'page.html' that it does this:
#  <label>Name:</label>
#  {{ form.name }}

...it will render out this:

<label>Name:</label>
<input type="text" name="name" value="Peter">

The whole initial trick is something you can set on the whole form or individual fields. But it's only used in UN-bound forms when rendered.

If you change your view function to this:

def view(request):
    form = MyForm(request.GET, initial={'name': 'Peter'}) # data passed!
    if form.is_valid():  # makes it bound!
        print(form.cleaned_data['name'])
    return render(request, 'page.html', form=form)

Now, the form is bound and the initial stuff is essentially ignored.
Because name is not present in request.GET. And if it was present, but an empty string, it wouldn't be able to benefit for the default value.

My solution

I tried many suggestions and tricks (based on rapid Stackoverflow searching) and nothing worked.

I knew one thing: Only the view should know the actual initial values.

Here's what works:

import copy


class MyForm(forms.Form):
    name = forms.CharField(required=False)

    def __init__(self, data, **kwargs):
        initial = kwargs.get('initial', {})
        data = {**initial, **data}
        super().__init__(data, **kwargs)

Now, suppose you don't have ?name=something in request.GET the line print(form.cleaned_data['name']) will print Peter and the rendered form will look like this:

<label>Name:</label>
<input type="text" name="name" value="Peter">

And, as expected, if you have ?name=Ashley in request.GET it will print Ashley and produce this rendered HTML too:

<label>Name:</label>
<input type="text" name="name" value="Ashley">

UPDATE June 2020

If data is a QueryDict object (e.g. <QueryDict: {'days': ['90']}>), and initial is a plain dict (e.g. {'days': 30}),
then you can merge these with {**data, **initial} because it produces a plain dict of value {'days': [90]} which Django's form stuff doesn't know is supposed to be "flattened".

The solution is to use:

from django.utils.datastructures import MultiValueDict

...

    def __init__(self, data, **kwargs):
        initial = kwargs.get("initial", {})
        data = MultiValueDict({**{k: [v] for k, v in initial.items()}, **data})
        super().__init__(data, **kwargs)

(To be honest; this might work in the app I'm currently working on but I don't feel confident that this is covering all cases)

Heroku is great but it's sometimes painful when your app isn't just in one single language. What I have is a project where the backend is Python (Django) and the frontend is JavaScript (Preact). The folder structure looks like this:

/
  - README.md
  - manage.py
  - requirements.txt
  - my_django_app/
     - settings.py
     - asgi.py
     - api/
        - urls.py
        - views.py
  - frontend/
     - package.json
     - yarn.lock
     - preact.config.js
     - build/
        ...
     - src/
        ...

A bunch of things omitted for brevity but people familiar with Django and preact-cli/create-create-app should be familiar.
The point is that the root is a Python app and the front-end is exclusively inside a sub folder.

When you do local development, you start two servers:

• ./manage.py runserver - starts http://localhost:8000
• cd frontend && yarn start - starts http://localhost:3000

The latter is what you open in your browser. That preact app will do things like:

const response = await fetch('/api/search');

and, in preact.config.js I have this:

export default (config, env, helpers) => {

  if (config.devServer) {
    config.devServer.proxy = [
      {
        path: "/api/**",
        target: "http://localhost:8000"
      }
    ];
  }

};

...which is hopefully self-explanatory. So, calls like GET http://localhost:3000/api/search actually goes to http://localhost:8000/api/search.

That's when doing development. The interesting thing is going into production.

Before we get into Heroku, let's first "merge" the two systems into one and the trick used is Whitenoise. Basically, Django's web server will be responsibly not only for things like /api/search but also static assets such as / --> frontend/build/index.html and /bundle.17ae4.js --> frontend/build/bundle.17ae4.js.

This is basically all you need in settings.py to make that happen:

MIDDLEWARE = [
    "django.middleware.security.SecurityMiddleware",
    "whitenoise.middleware.WhiteNoiseMiddleware",
    ...
]

WHITENOISE_INDEX_FILE = True

STATIC_URL = "/"
STATIC_ROOT = BASE_DIR / "frontend" / "build"

However, this isn't quite enough because the preact app uses preact-router which uses pushState() and other code-splitting magic so you might have a URL, that users see, like this: https://myapp.example.com/that/thing/special and there's nothing about that in any of the Django urls.py files. Nor is there any file called frontend/build/that/thing/special/index.html or something like that.
So for URLs like that, we have to take a gamble on the Django side and basically hope that the preact-router config knows how to deal with it. So, to make that happen with Whitenoise we need to write a custom middleware that looks like this:

from whitenoise.middleware import WhiteNoiseMiddleware


class CustomWhiteNoiseMiddleware(WhiteNoiseMiddleware):
    def process_request(self, request):
        if self.autorefresh:
            static_file = self.find_file(request.path_info)
        else:
            static_file = self.files.get(request.path_info)

            # These two lines is the magic.
            # Basically, the URL didn't lead to a file (e.g. `/manifest.json`)
            # it's either a API path or it's a custom browser path that only
            # makes sense within preact-router. If that's the case, we just don't
            # know but we'll give the client-side preact-router code the benefit
            # of the doubt and let it through.
            if not static_file and not request.path_info.startswith("/api"):
                static_file = self.files.get("/")

        if static_file is not None:
            return self.serve(static_file, request)

And in settings.py this change:

MIDDLEWARE = [
    "django.middleware.security.SecurityMiddleware",
-   "whitenoise.middleware.WhiteNoiseMiddleware",
+   "my_django_app.middleware.CustomWhiteNoiseMiddleware",
    ...
]

Now, all traffic goes through Django. Regular Django view functions, static assets, and everything else fall back to frontend/build/index.html.

Heroku

Heroku tries to make everything so simple for you. You basically, create the app (via the cli or the Heroku web app) and when you're ready you just do git push heroku master. However that won't be enough because there's more to this than Python.

Unfortunately, I didn't take notes of my hair-pulling excruciating journey of trying to add buildpacks and hacks and Procfiles and custom buildpacks. Nothing seemed to work. Perhaps the answer was somewhere in this issue: "Support running an app from a subdirectory" but I just couldn't figure it out. I still find buildpacks confusing when it's beyond Hello World. Also, I didn't want to run Node as a service, I just wanted it as part of the "build process".

Docker to the rescue

Finally I get a chance to try "Deploying with Docker" in Heroku which is a relatively new feature. And the only thing that scared me was that now I need to write a heroku.yml file which was confusing because all I had was a Dockerfile. We'll get back to that in a minute!

So here's how I made a Dockerfile that mixes Python and Node:

FROM node:12 as frontend

COPY . /app
WORKDIR /app
RUN cd frontend && yarn install && yarn build


FROM python:3.8-slim

WORKDIR /app

RUN groupadd --gid 10001 app && useradd -g app --uid 10001 --shell /usr/sbin/nologin app
RUN chown app:app /tmp

RUN apt-get update && \
    apt-get upgrade -y && \
    apt-get install -y --no-install-recommends \
    gcc apt-transport-https python-dev

# Gotta try moving this to poetry instead!
COPY ./requirements.txt /app/requirements.txt
RUN pip install --upgrade --no-cache-dir -r requirements.txt

COPY . /app
COPY --from=frontend /app/frontend/build /app/frontend/build

USER app

ENV PORT=8000
EXPOSE $PORT

CMD uvicorn gitbusy.asgi:application --host 0.0.0.0 --port $PORT

If you're not familiar with it, the critical trick is on the first line where it builds some Node with as frontend. That gives me a thing I can then copy from into the Python image with COPY --from=frontend /app/frontend/build /app/frontend/build.

Now, at the very end, it starts a uvicorn server with all the static .js, index.html, and favicon.ico etc. available to uvicorn which ultimately runs whitenoise.

To run and build:

docker build . -t my_app
docker run -t -i --rm --env-file .env -p 8000:8000 my_app

Now, opening http://localhost:8000/ is a production grade app that mixes Python (runtime) and JavaScript (static).

Heroku + Docker

Heroku says to create a heroku.yml file and that makes sense but what didn't make sense is why I would add cmd line in there when it's already in the Dockerfile. The solution is simple: omit it. Here's what my final heroku.yml file looks like:

build:
  docker:
    web: Dockerfile

Check in the heroku.yml file and git push heroku master and voila, it works!

To see a complete demo of all of this check out https://github.com/peterbe/gitbusy and https://gitbusy.herokuapp.com/

Recently I've been playing with the content of MDN as a whole. MDN has ~140k documents in its Wiki. About ~70k of them are redirects which is the result of many years of switching tech and switching information architecture and at the same time being good Internet citizens and avoiding 404s. So, out of the ~70k documents, how do they spread? To answer that I wrote a Python script that evaluates size as a matter of the sum of all the files in sub-trees including pictures.

Here are the screenshots:

All locales

Specifically en-US

The code that puts this together uses Toast UI which seems cool but I didn't spend much time worrying about how to use it.

Be warned! Opening this link will make your browser sweat: https://8mw9v.csb.app/

You can fork it here: https://codesandbox.io/s/zen-swirles-8mw9v

tl;dr; The previous (React) total JavaScript bundle size was: 36.2K Brotli compressed. The new (Preact) JavaScript bundle size was: 5.9K. I.e. 6 times smaller. Also, it appears to load faster in WebPageTest.

I have this page that is a Django server-side rendered page that has on it a form that looks something like this:

<div id="root">  
  <form action="https://songsear.ch/q/">  
    <input type="search" name="term" placeholder="Type your search here..." />
    <button>Search</button>
  </form>  
</div>

It's a simple search form. But, to make it a bit better for users, I wrote a React widget that renders, into this document.querySelector('#root'), a near-identical <form> but with autocomplete functionality that displays suggestions as you type.

Anyway, I built that React bundle using create-react-app. I use the yarn run build command that generates...

• css/main.83463791.chunk.css - 1.4K
• js/main.ec6364ab.chunk.js - 9.0K (gzip 2.8K, br 2.5K)
• js/runtime~main.a8a9905a.js - 1.5K (gzip 754B, br 688B)
• js/2.b944397d.chunk.js - 119K (gzip 36K, br 33K)

Then, in Python, a piece of post-processing code copies the files from the build/static/ directory and inserts it into the rendered HTML file. The CSS gets injected as an inline <style> tag.

It's a simple little widget. No need for any service-workers or react-router or any global state stuff. (Actually, it only has 1 single runtime dependency outside the framework) I thought, how about moving this to Preact?

In comes preact-cli

The app used a couple of React hooks but they were easy to transform into class components. Now I just needed to run:

npx preact create --yarn widget name-of-my-preact-project
cd name-of-my-preact-project
mkdir src
cp ../name-of-React-project/src/App.js src/
code src/App.js

Then, I slowly moved over the src/App.js from the create-react-app project and slowly by slowly I did the various little things that you need to do. For example, to learn to build with preact build --no-prerender --no-service-worker and how I can override the default template.

Long story short, the new built bundles look like this:

• style.82edf.css - 1.4K
• bundle.d91f9.js - 18K (gzip 6.4K, br 5.9K)
• polyfills.9168d.js - 4.5K (gzip 1.8K, br 1.6K)

(The polyfills.9168d.js gets injected as a script tag if window.fetch is falsy)

Unfortunately, when I did the move from React to Preact I did make some small fixes. Doing the "migration" I noticed a block of code that was never used so that gives the build bundle from Preact a slight advantage. But I think it's nominal.

In conclusion: The previous total JavaScript bundle size was: 36.2K (Brotli compressed). The new JavaScript bundle size was: 5.9K (Brotli compressed). I.e. 6 times smaller. But if you worry about the total amount of JavaScript to parse and execute, the size difference uncompressed was 129K vs. 18K. I.e. 7 times smaller. I can only speculate but I do suspect you need less CPU/battery to process 18K instead of 129K if CPU/batter matters more (or closer to) than network I/O.

Rendering speed difference

Rendering speed is so darn hard to measure on the web because the app is so small. Plus, there's so much else going on that matters.

However, using WebPageTest I can do a visual comparison with the "Mobile - Slow 3G" preset. It'll be a somewhat decent measurement of the total time of downloading, parsing and executing. Thing is, the server-side rended HTML form has a button. But the React/Preact widget that takes over the DOM hides that submit button. So, using the screenshots that WebPageTest provides, I can deduce that the Preact widget completes 0.8 seconds faster than the React widget. (I.e. instead of 4.4s it became 3.9s)

Truth be told, I'm not sure how predictable or reproducible is. I ran that WebPageTest visual comparison more than once and the results can vary significantly. I'm not even sure which run I'm referring to here (in the screenshot) but the React widget version was never faster.

Conclusion and thoughts

Unsurprisingly, Preact is smaller because you simply get less from that framework. E.g. synthetic events. I was lucky. My app uses onChange which I could easily "migrate" to onInput and I managed to get it to work pretty easily. I'm glad the widget app was so small and that I don't depend on any React specific third-party dependencies.

But! In WebPageTest Visual Comparison it was on "Mobile - Slow 3G" which only represents a small portion of the traffic. Mobile is a huge portion of the traffic but "Slow 3G" is not. When you do a Desktop comparison the difference is roughtly 0.1s.

Also, in total, that page is made up of 3 major elements

1. The server-side rendered HTML
2. The progressive JavaScript widget (what this blog post is about)
3. A piece of JavaScript initiated banner ad

That HTML controls the "First Meaningful Paint" which takes 3 seconds. And the whole shebang, including the banner ad, takes a total of about 9s. So, all this work of rewriting a React app to Preact saved me 0.8s out of the total of 9s.

Web performance is hard and complicated. Every little counts, but keep your eye on the big ticket items assuming there's something you can do about them.

At the time of writing, preact-cli uses Preact 8.2 and I'm eager to see how Preact X feels. Apparently, since April 2019, it's in beta. Looking forward to giving it a try!