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WebGL Morph Targets and Ginger: Modernizing for Today's Web

Published Jul 17, 2020

Updated Apr 19, 2023

3 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

WebGL Morph Targets and Ginger: Modernizing for Today's Web

Ginger is a morph target demo that was initially written in 2011 for chrome experiments, and then rewritten to work as progressive web app in early 2016. In essence, Ginger is a customizable head that uses a slider to tweak various facial features, much like a character customization system one would find in a video game. It also supports taking screenshots, and generating share links that can be used to share your creation with others.

The rewrite in 2016 focused on improving performance, and supporting modern web features. Notable improvements were made in the following areas:

Utilizing HTTP/2 multiplexing to load model data concurrently, and efficiently.
Utilize manifest.json for "Add to Homescreen" capability.
Offering a full offline experience with service workers.
Performance improvements from the previous version.

We inherited the project with our recent acquisition of Stickman Ventures, and thought it appropriate to showcase. Since it was rewritten in 2016, Ginger hasn't been touched, and has fallen into neglect, though it still worked fine in modern browsers. It was using an old version of Polymer 2 which has long since fallen out of use.

The Refactor

The first order of business was replacing Polymer 2 usage with something more modern. Polymer 2 was bleeding edge at the time, and used many proposed browser features that have since been adopted or dropped in favor for other standards (modules vs HTML imports).

Replacing Polymer with lit-html

To reduce friction, we opted to refactor Ginger's web components to use lit-html from the Polymer Project. Lit isn't a framework necessarily, it just makes it easier to write standard web components that are efficient, and work across all major browsers.

Transitioning from Polymer to lit was not a very complicated process, as they both use the same element lifecycle. The most notable difference was the transition from HTML imports to modules. With Polymer 2, elements are defined inside of HTML files, and are imported directly using HTML imports; however lit-html allows defining elements using a modern class-based approach.

class GingerApp extends LitElement {
  static get properties() {
    ...
  }

  static get styles() {
    ...css here
  }

  render() {
    ...html here
  }

  ...
}

With lit-html, it's very easy to define custom elements by extending off of the LitElement class, and registering it using either customElements.define or the decorator. We then use rollup.js and babel to create a bundle that works well across browsers.

Using Workbox to Support Offline Usage

Ginger is able to work completely offline thanks to its use of service workers. The previous iteration of Ginger used a service worker that came from platinum elements. Platinum elements made it possible to add offline capabilities to an application with very little effort. However, they've been deprecated in favor of workbox and sw-toolbox. We chose to go with workbox as it supports all of the features that we need, and I was already familiar with it.

The service worker uses caching strategies to cache images, fonts, code and model data. Data that won't change often or at all uses the "cache first" strategy, while code uses the "stale while revalidate" strategy.

The cache first strategy will grab the requested file from the cache if it exists, and do nothing else. The stale while revalidate strategy will return the cached data similar to the cache first strategy; however, it will also download a newer version from the server in the background.

Here's some basic examples taken directly from Ginger.

// Here we use 'StaleWhileRevalidate' for caching scripts and HTML documents. We
// check the destination to find out what kind of resource is being processed.
// Destinations are similar to mime types but aren't quite the same.
registerRoute(
  ({ request }) =>
    request.destination === 'script' || request.destination == 'document',
  new StaleWhileRevalidate({
    cacheName: 'script-cache',
  })
);

// The image cache definition looks very similar. You'll see the cache class
// being used has changed to 'CacheFirst' and that 'ExpirationPlugin' is used to
// control how long we'll keep the images.
registerRoute(
  ({ request }) => request.destination === 'image',
  new CacheFirst({
    cacheName: 'image-cache',
    plugins: [
      new ExpirationPlugin({
        maxEntries: 20,
        maxAgeSeconds: 7 * 24 * 60 * 60, // 1 week
      }),
    ],
  })
);

You can find out more about other caching strategies that workbox offers in their docs.

Closing

You can find Ginger's source code on Github, and it's available under an Apache 2 license.

The Ginger model was created, and is copyright of David Steele. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

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About the author(s)

Jamie Kuppens
I’m a software engineer with an interest in web development and some more esoteric things like emulator development.
@Reshurum @Reshurum

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. 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However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

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WebGL Morph Targets and Ginger: Modernizing for Today's Web

WebGL Morph Targets and Ginger: Modernizing for Today's Web

The Refactor

Replacing Polymer with lit-html

Using Workbox to Support Offline Usage

Closing

Jamie Kuppens

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