Yesenia Perez-Cruz started her career as a designer at Happy Cog Philadelphia. From the first day, her design gifts were unmistakable. As her career progressed, she moved from one challenging role to another. At companies like Vox Media and Shopify, and at conferences around the world, she has been a design team leader, a popular […]

The post Expressive Design Systems appeared first on Zeldman on Web & Interaction Design.

It’s one thing to seek diverse talent to add to your team, another to retain the people you’ve hired. Why do so many folks we bring in to add depth and breadth of experience to our design and business decision-making process end up leaving? Hear thoughtful, useful answers to this question and other mysteries of […]

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Like 90s hip-hop, The Web We Lost™ retains a near-mystical hold on the hearts and minds of those who were lucky enough to be part of it. Luke Dorny’s recent, lovingly hand-carved redesign of his personal site encompasses several generations of that pioneering creative web. As such, it will repay your curiosity.

The post The Web We Lost: Luke Dorny Redesign appeared first on Zeldman on Web & Interaction Design.

Dino works six days a week as a porter in my apartment building, cleaning walls and floors, removing trash, distributing recyclables. He’s one of those essential workers who are suddenly on the front lines. We’ve always been friendly. I’ve been hibernating in my apartment for days, because it’s what we’re all supposed to do, and […]

The post A kindness appeared first on Zeldman on Web & Interaction Design.

To share upcoming changes with our partners in the React ecosystem, we’re establishing official prerelease channels. We hope this process will help us make changes to React with confidence, and give developers the opportunity to try out experimental features.

This post will be most relevant to developers who work on frameworks, libraries, or developer tooling. Developers who use React primarily to build user-facing applications should not need to worry about our prerelease channels.

React relies on a thriving open source community to file bug reports, open pull requests, and submit RFCs. To encourage feedback, we sometimes share special builds of React that include unreleased features.

Because the source of truth for React is our public GitHub repository, it’s always been possible to build a copy of React that includes the latest changes. However it’s much easier for developers to install React from npm, so we occasionally publish prerelease builds to the npm registry. A recent example is the 16.7 alpha, which included an early version of the Hooks API.

We would like to make it even easier for developers to test prerelease builds of React, so we’re formalizing our process with three separate release channels.

Release Channels

The information in this post is also available on our Release Channels page. We will update that document whenever there are changes to our release process.

Each of React’s release channels is designed for a distinct use case:

• Latest is for stable, semver React releases. It’s what you get when you install React from npm. This is the channel you’re already using today. Use this for all user-facing React applications.
• Next tracks the master branch of the React source code repository. Think of these as release candidates for the next minor semver release. Use this for integration testing between React and third party projects.
• Experimental includes experimental APIs and features that aren’t available in the stable releases. These also track the master branch, but with additional feature flags turned on. Use this to try out upcoming features before they are released.

All releases are published to npm, but only Latest uses semantic versioning. Prereleases (those in the Next and Experimental channels) have versions generated from a hash of their contents, e.g. 0.0.0-1022ee0ec for Next and 0.0.0-experimental-1022ee0ec for Experimental.

The only officially supported release channel for user-facing applications is Latest. Next and Experimental releases are provided for testing purposes only, and we provide no guarantees that behavior won’t change between releases. They do not follow the semver protocol that we use for releases from Latest.

By publishing prereleases to the same registry that we use for stable releases, we are able to take advantage of the many tools that support the npm workflow, like unpkg and CodeSandbox.

Latest Channel

Latest is the channel used for stable React releases. It corresponds to the latest tag on npm. It is the recommended channel for all React apps that are shipped to real users.

If you’re not sure which channel you should use, it’s Latest. If you’re a React developer, this is what you’re already using.

You can expect updates to Latest to be extremely stable. Versions follow the semantic versioning scheme. Learn more about our commitment to stability and incremental migration in our versioning policy.

Next Channel

The Next channel is a prerelease channel that tracks the master branch of the React repository. We use prereleases in the Next channel as release candidates for the Latest channel. You can think of Next as a superset of Latest that is updated more frequently.

The degree of change between the most recent Next release and the most recent Latest release is approximately the same as you would find between two minor semver releases. However, the Next channel does not conform to semantic versioning. You should expect occasional breaking changes between successive releases in the Next channel.

Do not use prereleases in user-facing applications.

Releases in Next are published with the next tag on npm. Versions are generated from a hash of the build’s contents, e.g. 0.0.0-1022ee0ec.

Using the Next Channel for Integration Testing

The Next channel is designed to support integration testing between React and other projects.

All changes to React go through extensive internal testing before they are released to the public. However, there are myriad environments and configurations used throughout the React ecosystem, and it’s not possible for us to test against every single one.

If you’re the author of a third party React framework, library, developer tool, or similar infrastructure-type project, you can help us keep React stable for your users and the entire React community by periodically running your test suite against the most recent changes. If you’re interested, follow these steps:

• Set up a cron job using your preferred continuous integration platform. Cron jobs are supported by both CircleCI and Travis CI.

• In the cron job, update your React packages to the most recent React release in the Next channel, using next tag on npm. Using the npm cli:

  npm update react@next react-dom@next

  Or yarn:

  yarn upgrade react@next react-dom@next

• Run your test suite against the updated packages.
• If everything passes, great! You can expect that your project will work with the next minor React release.
• If something breaks unexpectedly, please let us know by filing an issue.

A project that uses this workflow is Next.js. (No pun intended! Seriously!) You can refer to their CircleCI configuration as an example.

Experimental Channel

Like Next, the Experimental channel is a prerelease channel that tracks the master branch of the React repository. Unlike Next, Experimental releases include additional features and APIs that are not ready for wider release.

Usually, an update to Next is accompanied by a corresponding update to Experimental. They are based on the same source revision, but are built using a different set of feature flags.

Experimental releases may be significantly different than releases to Next and Latest. Do not use Experimental releases in user-facing applications. You should expect frequent breaking changes between releases in the Experimental channel.

Releases in Experimental are published with the experimental tag on npm. Versions are generated from a hash of the build’s contents, e.g. 0.0.0-experimental-1022ee0ec.

What Goes Into an Experimental Release?

Experimental features are ones that are not ready to be released to the wider public, and may change drastically before they are finalized. Some experiments may never be finalized — the reason we have experiments is to test the viability of proposed changes.

For example, if the Experimental channel had existed when we announced Hooks, we would have released Hooks to the Experimental channel weeks before they were available in Latest.

You may find it valuable to run integration tests against Experimental. This is up to you. However, be advised that Experimental is even less stable than Next. We do not guarantee any stability between Experimental releases.

How Can I Learn More About Experimental Features?

Experimental features may or may not be documented. Usually, experiments aren’t documented until they are close to shipping in Next or Stable.

If a feature is not documented, they may be accompanied by an RFC.

We will post to the React blog when we’re ready to announce new experiments, but that doesn’t mean we will publicize every experiment.

You can always refer to our public GitHub repository’s history for a comprehensive list of changes.

At React Conf 2019 we announced an experimental release of React that supports Concurrent Mode and Suspense. In this post we’ll introduce best practices for using them that we’ve identified through the process of building the new facebook.com.

This post will be most relevant to people working on data fetching libraries for React.

It shows how to best integrate them with Concurrent Mode and Suspense. The patterns introduced here are based on Relay — our library for building data-driven UIs with GraphQL. However, the ideas in this post apply to other GraphQL clients as well as libraries using REST or other approaches.

This post is aimed at library authors. If you’re primarily an application developer, you might still find some interesting ideas here, but don’t feel like you have to read it in its entirety.

Talk Videos

If you prefer to watch videos, some of the ideas from this blog post have been referenced in several React Conf 2019 presentations:

• Data Fetching with Suspense in Relay by Joe Savona
• Building the New Facebook with React and Relay by Ashley Watkins
• React Conf Keynote by Yuzhi Zheng

This post presents a deeper dive on implementing a data fetching library with Suspense.

Putting User Experience First

The React team and community has long placed a deserved emphasis on developer experience: ensuring that React has good error messages, focusing on components as a way to reason locally about app behavior, crafting APIs that are predictable and encourage correct usage by design, etc. But we haven’t provided enough guidance on the best ways to achieve a great user experience in large apps.

For example, the React team has focused on framework performance and providing tools for developers to debug and tune application performance (e.g. React.memo). But we haven’t been as opinionated about the high-level patterns that make the difference between fast, fluid apps and slow, janky ones. We always want to ensure that React remains approachable to new users and supports a variety of use-cases — not every app has to be “blazing” fast. But as a community we can and should aim high. We should make it as easy as possible to build apps that start fast and stay fast, even as they grow in complexity, for users on varying devices and networks around the world.

Concurrent Mode and Suspense are experimental features that can help developers achieve this goal. We first introduced them at JSConf Iceland in 2018, intentionally sharing details very early to give the community time to digest the new concepts and to set the stage for subsequent changes. Since then we’ve completed related work, such as the new Context API and the introduction of Hooks, which are designed in part to help developers naturally write code that is more compatible with Concurrent Mode. But we didn’t want to implement these features and release them without validating that they work. So over the past year, the React, Relay, web infrastructure, and product teams at Facebook have all collaborated closely to build a new version of facebook.com that deeply integrates Concurrent Mode and Suspense to create an experience with a more fluid and app-like feel.

Thanks to this project, we’re more confident than ever that Concurrent Mode and Suspense can make it easier to deliver great, fast user experiences. But doing so requires rethinking how we approach loading code and data for our apps. Effectively all of the data-fetching on the new facebook.com is powered by Relay Hooks — new Hooks-based Relay APIs that integrate with Concurrent Mode and Suspense out of the box.

Relay Hooks — and GraphQL — won’t be for everyone, and that’s ok! Through our work on these APIs we’ve identified a set of more general patterns for using Suspense. Even if Relay isn’t the right fit for you, we think the key patterns we’ve introduced with Relay Hooks can be adapted to other frameworks.

Best Practices for Suspense

It’s tempting to focus only on the total startup time for an app — but it turns out that users’ perception of performance is determined by more than the absolute loading time. For example, when comparing two apps with the same absolute startup time, our research shows that users will generally perceive the one with fewer intermediate loading states and fewer layout changes as having loaded faster. Suspense is a powerful tool for carefully orchestrating an elegant loading sequence with a few, well-defined states that progressively reveal content. But improving perceived performance only goes so far — our apps still shouldn’t take forever to fetch all of their code, data, images, and other assets.

The traditional approach to loading data in React apps involves what we refer to as “fetch-on-render”. First we render a component with a spinner, then fetch data on mount (componentDidMount or useEffect), and finally update to render the resulting data. It’s certainly possible to use this pattern with Suspense: instead of initially rendering a placeholder itself, a component can “suspend” — indicate to React that it isn’t ready yet. This will tell React to find the nearest ancestor <Suspense fallback={<Placeholder/>}>, and render its fallback instead. If you watched earlier Suspense demos this example may feel familiar — it’s how we originally imagined using Suspense for data-fetching.

It turns out that this approach has some limitations. Consider a page that shows a social media post by a user, along with comments on that post. That might be structured as a <Post> component that renders both the post body and a <CommentList> to show the comments. Using the fetch-on-render approach described above to implement this could cause sequential round trips (sometimes referred to as a “waterfall”). First the data for the <Post> component would be fetched and then the data for <CommentList> would be fetched, increasing the time it takes to show the full page.

There’s also another often-overlooked downside to this approach. If <Post> eagerly requires (or imports) the <CommentList> component, our app will have to wait to show the post body while the code for the comments is downloading. We could lazily load <CommentList>, but then that would delay fetching comments data and increase the time to show the full page. How do we resolve this problem without compromising on the user experience?

Render As You Fetch

The fetch-on-render approach is widely used by React apps today and can certainly be used to create great apps. But can we do even better? Let’s step back and consider our goal.

In the above <Post> example, we’d ideally show the more important content — the post body — as early as possible, without negatively impacting the time to show the full page (including comments). Let’s consider the key constraints on any solution and look at how we can achieve them:

• Showing the more important content (the post body) as early as possible means that we need to load the code and data for the view incrementally. We don’t want to block showing the post body on the code for <CommentList> being downloaded, for example.
• At the same time we don’t want to increase the time to show the full page including comments. So we need to start loading the code and data for the comments as soon as possible, ideally in parallel with loading the post body.

This might sound difficult to achieve — but these constraints are actually incredibly helpful. They rule out a large number of approaches and spell out a solution for us. This brings us to the key patterns we’ve implemented in Relay Hooks, and that can be adapted to other data-fetching libraries. We’ll look at each one in turn and then see how they add up to achieve our goal of fast, delightful loading experiences:

1. Parallel data and view trees
2. Fetch in event handlers
3. Load data incrementally
4. Treat code like data

Parallel Data and View Trees

One of the most appealing things about the fetch-on-render pattern is that it colocates what data a component needs with how to render that data. This colocation is great — an example of how it makes sense to group code by concerns and not by technologies. All the issues we saw above were due to when we fetch data in this approach: upon rendering. We need to be able to fetch data before we’ve rendered the component. The only way to achieve that is by extracting the data dependencies into parallel data and view trees.

Here’s how that works in Relay Hooks. Continuing our example of a social media post with body and comments, here’s how we might define it with Relay Hooks:

// Post.js
function Post(props) {
  // Given a reference to some post - `props.post` - *what* data
  // do we need about that post?
  const postData = useFragment(graphql`
    fragment PostData on Post @refetchable(queryName: "PostQuery") {
      author
      title
      # ...  more fields ...
    }
  `, props.post);

  // Now that we have the data, how do we render it?
  return (
    <div>
      <h1>{postData.title}</h1>
      <h2>by {postData.author}</h2>
      {/* more fields  */}
    </div>
  );
}

Although the GraphQL is written within the component, Relay has a build step (Relay Compiler) that extracts these data-dependencies into separate files and aggregates the GraphQL for each view into a single query. So we get the benefit of colocating concerns, while at runtime having parallel data and view trees. Other frameworks could achieve a similar effect by allowing developers to define data-fetching logic in a sibling file (maybe Post.data.js), or perhaps integrate with a bundler to allow defining data dependencies with UI code and automatically extracting it, similar to Relay Compiler.

The key is that regardless of the technology we’re using to load our data — GraphQL, REST, etc — we can separate what data to load from how and when to actually load it. But once we do that, how and when do we fetch our data?

Fetch in Event Handlers

Imagine that we’re about to navigate from a list of a user’s posts to the page for a specific post. We’ll need to download the code for that page — Post.js — and also fetch its data.

Waiting until we render the component has problems as we saw above. The key is to start fetching code and data for a new view in the same event handler that triggers showing that view. We can either fetch the data within our router — if our router supports preloading data for routes — or in the click event on the link that triggered the navigation. It turns out that the React Router folks are already hard at work on building APIs to support preloading data for routes. But other routing frameworks can implement this idea too.

Conceptually, we want every route definition to include two things: what component to render and what data to preload, as a function of the route/url params. Here’s what such a route definition might look like. This example is loosely inspired by React Router’s route definitions and is primarily intended to demonstrate the concept, not a specific API:

// PostRoute.js (GraphQL version)

// Relay generated query for loading Post data
import PostQuery from './__generated__/PostQuery.graphql';

const PostRoute = {
  // a matching expression for which paths to handle
  path: '/post/:id',

  // what component to render for this route
  component: React.lazy(() => import('./Post')),

  // data to load for this route, as function of the route
  // parameters
  prepare: routeParams => {
    // Relay extracts queries from components, allowing us to reference
    // the data dependencies -- data tree -- from outside.
    const postData = preloadQuery(PostQuery, {
      postId: routeParams.id,
    });

    return { postData };
  },
};

export default PostRoute;

Given such a definition, a router can:

• Match a URL to a route definition.
• Call the prepare() function to start loading that route’s data. Note that prepare() is synchronous — we don’t wait for the data to be ready, since we want to start rendering more important parts of the view (like the post body) as quickly as possible.
• Pass the preloaded data to the component. If the component is ready — the React.lazy dynamic import has completed — the component will render and try to access its data. If not, React.lazy will suspend until the code is ready.

This approach can be generalized to other data-fetching solutions. An app that uses REST might define a route like this:

// PostRoute.js (REST version)

// Manually written logic for loading the data for the component
import PostData from './Post.data';

const PostRoute = {
  // a matching expression for which paths to handle
  path: '/post/:id',

  // what component to render for this route
  component: React.lazy(() => import('./Post')),

  // data to load for this route, as function of the route
  // parameters
  prepare: routeParams => {
    const postData = preloadRestEndpoint(
      PostData.endpointUrl, 
      {
        postId: routeParams.id,
      },
    );
    return { postData };
  },
};

export default PostRoute;

This same approach can be employed not just for routing, but in other places where we show content lazily or based on user interaction. For example, a tab component could eagerly load the first tab’s code and data, and then use the same pattern as above to load the code and data for other tabs in the tab-change event handler. A component that displays a modal could preload the code and data for the modal in the click handler that triggers opening the modal, and so on.

Once we’ve implemented the ability to start loading code and data for a view independently, we have the option to go one step further. Consider a <Link to={path} /> component that links to a route. If the user hovers over that link, there’s a reasonable chance they’ll click it. And if they press the mouse down, there’s an even better chance that they’ll complete the click. If we can load code and data for a view after the user clicks, we can also start that work before they click, getting a head start on preparing the view.

Best of all, we can centralize that logic in a few key places — a router or core UI components — and get any performance benefits automatically throughout our app. Of course preloading isn’t always beneficial. It’s something an application would tune based on the user’s device or network speed to avoid eating up user’s data plans. But the pattern here makes it easier to centralize the implementation of preloading and the decision of whether to enable it or not.

Load Data Incrementally

The above patterns — parallel data/view trees and fetching in event handlers — let us start loading all the data for a view earlier. But we still want to be able to show more important parts of the view without waiting for all of our data. At Facebook we’ve implemented support for this in GraphQL and Relay in the form of some new GraphQL directives (annotations that affect how/when data is delivered, but not what data). These new directives, called @defer and @stream, allow us to retrieve data incrementally. For example, consider our <Post> component from above. We want to show the body without waiting for the comments to be ready. We can achieve this with @defer and <Suspense>:

// Post.js
function Post(props) {
  const postData = useFragment(graphql`
    fragment PostData on Post {
      author
      title

      # fetch data for the comments, but don't block on it being ready
      ...CommentList @defer
    }
  `, props.post);

  return (
    <div>
      <h1>{postData.title}</h1>
      <h2>by {postData.author}</h2>
      {/* @defer pairs naturally with <Suspense> to make the UI non-blocking too */}
      <Suspense fallback={<Spinner/>}>
        <CommentList post={postData} />
      </Suspense>
    </div>
  );
}

Here, our GraphQL server will stream back the results, first returning the author and title fields and then returning the comment data when it’s ready. We wrap <CommentList> in a <Suspense> boundary so that we can render the post body before <CommentList> and its data are ready. This same pattern can be applied to other frameworks as well. For example, apps that call a REST API might make parallel requests to fetch the body and comments data for a post to avoid blocking on all the data being ready.

Treat Code Like Data

But there’s one thing that’s still missing. We’ve shown how to preload data for a route — but what about code? The example above cheated a bit and used React.lazy. However, React.lazy is, as the name implies, lazy. It won’t start downloading code until the lazy component is actually rendered — it’s “fetch-on-render” for code!

To solve this, the React team is considering APIs that would allow bundle splitting and eager preloading for code as well. That would allow a user to pass some form of lazy component to a router, and for the router to trigger loading the code alongside its data as early as possible.

Putting It All Together

To recap, achieving a great loading experience means that we need to start loading code and data as early as possible, but without waiting for all of it to be ready. Parallel data and view trees allow us to load the data for a view in parallel with loading the view (code) itself. Fetching in an event handler means we can start loading data as early as possible, and even optimistically preload a view when we have enough confidence that a user will navigate to it. Loading data incrementally allows us to load important data earlier without delaying the fetching of less important data. And treating code as data — and preloading it with similar APIs — allows us to load it earlier too.

Using These Patterns

These patterns aren’t just ideas — we’ve implemented them in Relay Hooks and are using them in production throughout the new facebook.com (which is currently in beta testing). If you’re interested in using or learning more about these patterns, here are some resources:

• The React Concurrent docs explore how to use Concurrent Mode and Suspense and go into more detail about many of these patterns. It’s a great resource to learn more about the APIs and use-cases they support.
• The experimental release of Relay Hooks implements the patterns described here.

• We’ve implemented two similar example apps that demonstrate these concepts:

  • The Relay Hooks example app uses GitHub’s public GraphQL API to implement a simple issue tracker app. It includes nested route support with code and data preloading. The code is fully commented — we encourage cloning the repo, running the app locally, and exploring how it works.
  • We also have a non-GraphQL version of the app that demonstrates how these concepts can be applied to other data-fetching libraries.

While the APIs around Concurrent Mode and Suspense are still experimental, we’re confident that the ideas in this post are proven by practice. However, we understand that Relay and GraphQL aren’t the right fit for everyone. That’s ok! We’re actively exploring how to generalize these patterns to approaches such as REST, and are exploring ideas for a more generic (ie non-GraphQL) API for composing a tree of data dependencies. In the meantime, we’re excited to see what new libraries will emerge that implement the patterns described in this post to make it easier to build great, fast user experiences.

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One of the most common problems that I run into when using Redux is trying to figure out why an action is not being captured by a reducer. For someone just getting starting with Redux, debugging this issue can be especially overwhelming because of how Redux manages data flow. So before you start pouring over configuration code, or the logic contained in your action creators and reducers, please, make sure your action types are defined and spelled correctly.

One of the most common problems that I run into when using Redux is trying to figure out why an action is not being captured by a reducer. For someone just getting starting with Redux, debugging this issue can be especially overwhelming because of how Redux manages data flow. So before you start pouring over configuration code, or the logic contained in your action creators and reducers, please, make sure your action types are defined and spelled correctly.

In any application that I have built, most bugs that I have run into are simply due to typos. However, the solution to this particular problem is harder to spot because no errors are raised when the application is run. Take a look at the snippet below.

// actionTypes.js

export const FETCH_FILE_REQUEST = 'fetch_file_request';
export const FETCH_FILE_SUCCESS = 'fetch_file_success';
export const FETCH_FILE_FAIL = 'fetch_file_fail';


// filesReducer.js

import {
  FETCH_FILE_REQUEST,
  FETCH_FILE_SUCESS,
  FETCH_FILE_FAIL
} from '../actions/actionTypes';

const filesReducer = (state = {}, action) => {
  switch (action.type) {
    case FETCH_FILE_SUCESS:
      return { ...state, file: action.payload };
    default:
      return state;
  }
}

export default filesReducer;

Assuming we dispatched an action with type FETCH_FILE_SUCCESS, the filesReducer should catch the action before the default case is returned. But what if that is not happening? Where do we start the debugging process. There does not appear to be anything wrong with the code in the reducer; the action type was imported and matches the case in the switch statement. There are no errors in the browser. Where is the issue?

You may have noticed that I misspelled SUCCESS in filesReducer.js, but the reason this can be hard to catch is because importing undefined types does not cause an error, so when we import FETCH_FILE_SUCESS, its value is actually undefined, so our reducer always hits the default case.

It would be nice if the existing import/export system could help us catch this. Unfortunately, since action types are just strings, validating their existence is challenging. Luckily, we have another option.

Enter Proxies

Proxies are a feature of ES2015 that allow us to customize operations on a object. They can be used in many different ways, and you can find some useful examples here and here. For our problem, this example from Mozilla looks promising:

let validator = {
  set: function(obj, prop, value) {
    if (prop === 'age') {
      if (!Number.isInteger(value)) {
        throw new TypeError('The age is not an integer');
      }
      if (value > 200) {
        throw new RangeError('The age seems invalid');
      }
    }

    // The default behavior to store the value
    obj[prop] = value;

    // Indicate success
    return true;
  }
};

let person = new Proxy({}, validator);

person.age = 100;
console.log(person.age); // 100
person.age = 'young'; // Throws an exception
person.age = 300; // Throws an exception

So if proxies can be used to validate that properties assigned to an object are of a certain type and value, we should definitely be able to ensure that our action types are never undefined, or else throw an error that will be easy for us to fix. Let’s refactor our actionTypes.js file.

// actionTypes.js

const types = {
  FETCH_FILE_REQUEST: 'fetch_file_request',
  FETCH_FILE_SUCCESS: 'fetch_file_success',
  FETCH_FILE_FAIL: 'fetch_file_fail'
}

const typeValidator = {
  get(obj, prop) {
    if (obj[prop]) {
      return prop;
    } else {
      throw new TypeError(`${prop} is not a valid action type`);
    }
  }
}

module.exports = new Proxy(types, typeValidator);

First, we define a object containing all our action types. Then we define our validator handler typeValidator. The get method inside our handler is called a trap, and provides access to the properties of a object. If the property we are looking for, an action type, in this case, exists in the types object, return that prop, unmodified. Otherwise, throw an error because the prop does not exist.

Finally, export a new proxy, passing the types object as the target and the typeValidator as the handler. However, it is important to note that the ES2015 module system does not work well with proxies, so module.exports and require() must be used for exporting and importing the types.

Barely any code needs to change in the reducer and action creator files, but in order for the action types to be imported successfully, we just need couple lines of code in a new file:

// actionTypesProxy.js

export const {
  FETCH_FILE_REQUEST,
  FETCH_FILE_SUCCESS,
  FETCH_FILE_FAIL,
} = require('./actionTypes');

// in the reducer and action creator files
// change '../actions/actionTypes' to
// '../actions/actionTypesProxy'

By creating a proxy to verify the existence of an action type, we no longer have to worry about correctly naming a property upon import because an error will be thrown in the browser console as soon as the application starts. So, reduce the number headaches you get when developing an application using Redux and start using proxies.

Interested in learning how to build applications using Redux with ReactJS. Check out this online course! Modern React with Redux