PWA

Intro to PWA and Service Workers

Jan 17, 2020

4 min read

Progressive Web Apps

Progressive Web Apps, aka PWAs, are becoming more and more popular everyday. In this tutorial, I'm going to tell you what a progressive web app is, and how to create one using a service worker.

What is a PWA?

A PWA is an app that is intented to behave like a native app on your phone. It's a regular web app that, when you open it on your browser, displays a message saying "Add to Home Screen" once you click on the message, your app will be intalled on your phone and you will see the logo on your device's home screen.

PWAs are becoming very popular because they help incresing the user experience when visiting a website. When visiting a traditional website, it can take several seconds. A PWA's loading time, however, is significantly faster thanks to the use of Service Workers.

There are 3 types of apps you can have on your device:

-Native -Hybrid -PWA

Native: in my opinion unless you are using high graphics, and performing heavy user interaction with the a device (like playing games), I would go with a Native app. Native Apps are expensive to build since they require double effort and time - two separate apps need to be built for Android and iOS.

Hybrid: a good way to go, since you have one stack that creates separate builds for Android and iOS platforms from the same sources. I would use hybrid apps if you don't require high speed and graphics on your app. With hybrid apps, you do have access to the device hardware via plugins that are built with native code.

What about PWA?

PWA: They are cheaper than the previous types of apps when it comes to developing, quick to develop, and they work offline! The same way that you develop a regular responsive web app, you can develop a PWA with the difference of adding a service worker to enable offline support, and things like installing the app on your home screen.

What is a Service Worker?

A service worker is a script that is running separetly from our website - in the background. Service workers are awesome, they can help us reach different things like caching pages, caching API calls, push notifications, background sync, and more.

A service worker can cache network requests.
A service worker can handle how network requests are done in your website.
A service worker can make the use of the__ Background Sync API__ which allows you to defer actions until the user has stable connectivity.
A service worker can't access/interact the DOM directly.
A service worker can cache your things from your website, like static assests.
A service worker can receive push notifications when the app is not active.
A service worker stays inactive when it's not in use. When it gets the "signal" to be used, it reactivates again.
A service worker can be used to make your app work offline.

Service Worker's Lifecycle

-Register the service worker:

The first step into the service worker's lifecycle is to register the service worker. You can do so using the following code:

if ('serviceWorker' in navigator) {
  navigator.serviceWorker.register('/sw.js');
}

Jeff Posnick from Google taught me a little trick in one of his articles about service workers. This trick is a simple way to improve the registration of the service worker.

if ('serviceWorker' in navigator) {
  window.addEventListener('load', function() {
    navigator.serviceWorker.register('/service-worker.js');
  });
}

This code will register the service worker once the whole page has loaded, but keep in mind that you might delay the registration of the service worker this way.

-Installing the service worker: This process is only called once after the service worker has been loaded. If you modify the existing service worker, the browser will update, and install the service worker again.

There is an install event that gets emitted while the service worker is being installed. You can handle this event, and perform some async actions if needed (e.g., caching static assests). For this purpose, you can use event.waitUntil(). This method will keep the service worker in the install phase until the promise passed to event.waitUntil() is settled. Depending on whether that promise is resolved or rejected, the install phase will either finish successfully, or won't.

In order to install the service worker, we need to do the following:

Open a cache.
Cache our files.
Confirm whether all the required assets are cached or not.

const web_cache = 'web-app-cache-v1.0';
const filesToCache = [
  '/',
  '/styles/styles.css',
  '/script/main.js'
  ....
];

self.addEventListener('install', function(event) {
  event.waitUntil(
    caches.open(web_cache)
      .then((cache)=> {
        //Cache has been opened succesfully
        return cache.addAll(filesToCache);
      })
  );
});

The following are instruction on how to check if your service wokers have been successfully installed and registered directly in the browser.

In Chrome (duhhh)

Open dev tools
In the toolbar click on Application
On your left side, you will see a sidebar. Click on service worker.
You should see something like the following:

-Activating the service worker:

The service worker can get into the activating state in different scenarios. Here are some of those scenarios:

we don't have the existing service worker in our app
we run the self.skipWaiting() method in the service worker
the user has navigated away from the page, releasing the previous active worker
a set period of time has passed, releasing the previous active worker

-Active service worker:

When a service worker is active, you can check its status inside of the register object.

navigator.serviceWorker.register('./service-worker.js').then((registration)=> {
    if (registration.active) {
        console.log('Service worker is active');
    }
})

-Redundant service worker:

A Service Worker can be redundant (aka something went WRONG) because of the following reasons:

the installation of the service worker failed
the service worker failed when it was getting activated
a new service worker replaces the existing service worker as the active service worker

That's all folks!

Sources:

-https://developers.google.com/web/fundamentals/primers/service-workers -https://bitsofco.de/the-service-worker-lifecycle/ -https://developers.google.com/web/fundamentals/primers/service-workers/lifecycle

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.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

Overview of the New Signal APIs in Angular

Overview of the New Signal APIs in Angular Google's Minko Gechev and Jeremy Elbourn announced many exciting things at NG Conf 2024. Among them is the addition of several new signal-based APIs. They are already in developer preview, so we can play around with them. Let's dig into it, starting with signal-based inputs and the new matching outputs API. Signal Based Inputs Discussions about signal-based inputs have been taking place in the Angular community for some time now, and they are finally here. Until now, you used the @Input() decorator to define inputs. This is what you'd have to write in your component to declare an optional and a required input: ` With the new signal-based inputs, you can write much less boilerplate code. Here is how you can define the same inputs using the new syntax: ` It's not only less boilerplate, but because the values are signals, you can also use them directly in computed signals and effects. That, effectively, means you get to avoid computing combined values in ngOnChanges or using setters for your inputs to be able to compute derived values. In addition, input signals are read-only. The New Output I intentionally avoid calling them signal-based outputs because they are not. They still work the same way as the old outputs. The Angular team has just introduced a new output API that is more consistent with the latest inputs API and allows you to write less boilerplate, similar to the new input API. Here is how you would define an output until now: ` Here is how you can define the same output using the new syntax: ` The thing I like about the new output API is that sometimes it happens to me that I forget to instantiate the EventEmitter because I do this instead: ` You won't forget to instantiate the output with the new syntax because the output function does it for you. Signal Queries I am sure most readers know the @ViewChild, @ViewChildren, @ContentChild, and @ContentChildren decorators very well and have experienced the pain of triggering the infamous ExpressionChangedAfterItHasBeenCheckedError or having the values unavailable when needed. Here is a refresher on how you would use these decorators until now: ` With the new signal queries, similar to the new input API, the values are signals, and you can use them directly in computed signals and effects. You can define the same queries using the new syntax: ` Jeremy Elbourn mentioned that the new signal queries have better type inference and are more consistent with the new input and output APIs. He also showcased a brand new feature not available with the old queries. You can now define a query as required, and the Angular compiler will throw an error if the query has no result, guaranteeing that the value won't be undefined. Here is how you can define a required query: ` Model Inputs Jeremy and Minko announced the last new feature is the model inputs. The name is vague, but the feature is cool—it simplifies the definition of two-way bindings. Until now, to achieve two-way binding, you would have to define an input and an output following a given naming convention. @Input and @Output had to be defined with the same name (followed by "Change" in the case of the output). Then, you could use the template's [()] syntax. ` ` That way, you could keep the value in sync between the parent and the child component. With the new model inputs, you can define a two-way binding with a single line of code. Here is how you can define the same two-way binding using the new syntax: ` The html template stays the same: ` The model function returns a writable signal that can be updated directly. The value will be propagated back to any two-way bindings. Conclusion The new signal-based APIs are a great addition to Angular. They allow you to write less boilerplate code and make your components more reactive. The new APIs are already in developer preview, so you can start playing around with them today. I look forward to seeing how the community will adopt these new features and what other exciting things the Angular team has in store for us, such as zoneless apps by default....

Apr 3, 2024

3 mins

AngularJavaScript

How to create and use custom GraphQL Scalars

How to create and use custom GraphQL Scalars In the realm of GraphQL, scalars form the bedrock of the type system, representing the most fundamental data types like strings, numbers, and booleans. As explored in our previous post, "Leveraging GraphQL Scalars to Enhance Your Schema," scalars play a pivotal role in defining how data is structured and validated. But what happens when the default scalars aren't quite enough? What happens when your application demands a data type as unique as its requirements? Enter the world of custom GraphQL scalars. These data types go beyond the conventional, offering the power and flexibility to tailor your schema to precisely match your application's unique needs. Whether handling complex data structures, enforcing specific data formats, or simply bringing clarity to your API, custom scalars open up a new realm of possibilities. In this post, we'll explore how to understand, create, and effectively utilize custom scalars in GraphQL. From conceptualization to implementation, we'll cover the essentials of extending your GraphQL toolkit, empowering you to transform abstract ideas into concrete, practical solutions. So, let's embark together on the journey of understanding and utilizing custom GraphQL scalars, enhancing and expanding the capabilities of your GraphQL schema. Understanding Custom Scalars Custom scalars in GraphQL extend beyond basic types like String or Int, allowing data to be defined, validated, and processed more precisely. They're instrumental when default types don't quite capture the complexity or specificity of the data, such as with specialized date formats or unique identifiers. The use of custom scalars brings several benefits: * Enhanced Clarity: They offer a clearer representation of what data looks like and how it behaves. * Built-in Validation: Data integrity is bolstered at the schema level. * Flexibility: They can be tailored to specific data handling needs, making your schema more adaptable and robust. With this understanding, we'll explore creating and integrating custom scalars into a GraphQL schema, turning theory into practice. Creating a Custom Scalar Defining a Custom Scalar in TypeScript: Creating a custom scalar in GraphQL with TypeScript involves defining its behavior through parsing, serialization, and validation functions. * Parsing: Transforms input data from the client into a server-understandable format. * Serializing: Converts server data back to a client-friendly format. * Validation: Ensures data adheres to the defined format or criteria. Example: A 'Color' Scalar in TypeScript The Color scalar will ensure that every color value adheres to a valid hexadecimal format, like #FFFFFF for white or #000000 for black: ` In this TypeScript implementation: * validateColors: a function that checks if the provided string matches the hexadecimal color format. * parseValue: a method function that converts the scalar’s value from the client into the server’s representation format - this method is called when a client provides the scalar as a variable. See parseValue docs for more information * serialize: a method function that converts the scalar’s server representation format to the client format, see serialize docs for more information * parseLiteral: similar to parseValue, this method function converts the scalar’s value from the client to the server’s representation format. Still, this method is called when the scalar is provided as a hard-coded argument (inline). See parseLiteral docs for more information In the upcoming section, we'll explore how to incorporate and validate these custom scalars within your schema, ensuring they function seamlessly in real-world scenarios. Integrating Custom Scalars into a Schema Incorporating the 'Color' Scalar After defining your custom Color scalar, the next crucial step is effectively integrating it into your GraphQL schema. This integration ensures that your GraphQL server recognizes and correctly utilizes the scalar. Step-by-Step Integration 1. Add the scalar to Type Definitions: Include the Color scalar in your GraphQL type definitions. This inclusion informs GraphQL about this new scalar type. 2. Resolver Mapping: Map your custom scalar type to its resolver. This connection is key for GraphQL to understand how to process this type during queries and mutations. ` 1. Use the scalar: Update your type to use the new custom scalar ` Testing the Integration With your custom Color scalar integrated, conducting thorough testing is vital. Ensure that your GraphQL server correctly handles the Color scalar, particularly in terms of accepting valid color formats and rejecting invalid ones. For demonstration purposes, I've adapted a creation mutation to include the primaryColor field. To keep this post focused and concise, I won't detail all the code changes here, but the following screenshots illustrate the successful implementation and error handling. Calling the mutation (createTechnology) successfully: Calling the mutation with forced fail (bad color hex): Conclusion The journey into the realm of custom GraphQL scalars reveals a world where data types are no longer confined to the basics. By creating and integrating scalars like the Color type, we unlock precision and specificity in our GraphQL schemas, which significantly enhance our applications' data handling capabilities. Custom scalars are more than just a technical addition; they testify to GraphQL's flexibility and power. They allow developers to express data meaningfully, ensuring that APIs are functional, intuitive, and robust. As we've seen, defining, integrating, and testing these scalars requires a blend of creativity and technical acumen. It encourages a deeper understanding of how data flows through your application and offers a chance to tailor that experience to your project's unique needs. So, as you embark on your GraphQL journey, consider the potential of custom scalars. Whether you're ensuring data integrity, enhancing API clarity, or simply making your schema a perfect fit for your application, the possibilities are as vast as they are exciting. Embrace the power of customization, and let your GraphQL schemas shine!...

Apr 10, 2024

5 mins

GraphQLJavaScript

End-to-end type-safety with JSON Schema

End-to-end type-safety with JSON Schema I recently wrote an introduction to JSON Schema post. If you’re unfamiliar with it, check out the post, but TLDR: It’s a schema specification that can be used to define the input and output data for your JSON API. In my post, I highlight many fantastic benefits you can reap from defining schemas for your JSON API. One of the more interesting things you can achieve with your schemas is end-to-end type safety from your backend API to your client application(s). In this post, we will explore how this can be accomplished slightly deeper. Overview The basic idea of what we want to achieve is: * a JSON API server that validates input and output data using JSON Schema * The JSON Schema definitions that our API uses transformed into TypeScript types With those pieces in place, we can achieve type safety on our API server and the consuming client application. The server side is pretty straightforward if you’re using a server like Fastify with already enabled JSON Schema support. This post will focus on the concepts more than the actual implementation details though. Here’s a simple diagram illustrating the high-level concept: We can share the schema and type declaration between the client and server. In that case, we can make a request to an endpoint where we know its type and schema, and assuming the server validates the data against the schema before sending it back to the client, our client can be confident about the type of the response data. Marrying JSON Schema and TypeScript There are a couple of different ways to accomplish this: * Generating types from schema definitions using code generation tools * Creating TypeBox definitions that can infer TypeScript types and be compiled to JSON Schema I recommend considering both and figuring out which would better fit your application and workflows. Like anything else, each has its own set of trade-offs. In my experience, I’ve found TypeBox to be the most compelling if you want to go deep with this pattern. Code generation A couple of different packages are available for generating TS types from JSON Schema definitions. * https://github.com/bcherny/json-schema-to-typescript * https://github.com/vega/ts-json-schema-generator They are CLI tools that you can provide a glob path to where your schema files are located and will generate TS declaration files to a specified output path. You can set up an npm hook or a similar type of script that will generate types for your development environment. TypeBox TypeBox is a JSON Schema type builder. With this approach, instead of json files, we define schemas in code using the TypeBox API. The TypeBox definitions infer to TypeScript types directly, which eliminates the code generation step described above. Here’s a simple example from the documentation of a JSON Schema definition declared with TypeBox: ` This can then be inferred as a TypeScript type: ` Aside from schemas and types, TypeBox can do a lot more to help us on our type-safety journey. We will explore it a bit more in upcoming sections. Sharing schemas between client and server applications Sharing our JSON Schema between our server and client app is the main requirement for end-to-end type-safety. There are a couple of different ways to accomplish this, but the simplest would be to set up our codebase as a monorepo that contains both the server and client app. Some popular options for TypeScript monorepos are: PNPM, Turborepo, and NX. If a monorepo is not an option, you can publish your schema and types as a package that can be installed in both projects. However, this setup would require a lot more maintenance work. Ultimately, as long as you can import your schemas and types from the client and server app, you are in good shape. Server-to-client validation and type-safety For the sake of simplicity, let's focus on data flowing from the server to the client for now. Generally speaking, the concepts also apply in reverse, as long as your JSON API server validates your inputs and outputs. We’ll look at the most basic version of having strongly typed data on the client from a request to our server. Type-safe client requests In our server application, if we validate the /users endpoint with a shared schema - on the client side, when we make the request to the endpoint, we know that the response data is validated using the user schema. As long as we are confident of this fact, we can use the generated type from that schema as the return type on our client fetch call. Here’s some pseudocode: ` Our server endpoint would look something like this: ` You could get creative and build out a map that defines all of your endpoints, their metadata, and schemas, and use the map to define your server endpoints and create an API client. Transforming data over the wire Everything looks stellar, but we can still take our efforts a bit further. To this point, we are still limited to serialized JSON data. If we have a created_at field (number or ISO string) tied to our user, and we want it to be a Date object when we get a hold of it on the client side - additional work and consideration are required. There are some different strategies out there for deserializing JSON data. The great thing about having shared schemas between our client and server is that we can encode our type information in the schema without sending additional metadata from our server to the client. Using format to declare type data In my initial JSON Schema blog post, I touched on the format field of the specification. In our schema, if the actual type of our date is a string in ISO8601 format, we can declare our format to be "date-time". We can use this information on the client to transform the field into a proper Date object. ` Transforming serialized JSON Data This can be a little bit tricky; again, there are many ways to accomplish it. To demonstrate the concept, we’ll use TypeBox to define our schemas as discussed above. TypeBox provides a Transform type that you can use to declare, encode, and decode methods for your schema definition. ` It even provides helpers to statically generate the decoded and encoded types for your schema ` If you declare your decode and encode functions for your schemas, you can then use the TypeBox API to handle decoding the serialized values returned from your JSON API. Here’s what the concept looks like in practice fetching a user from our API: ` Nice. You could use a validation library like Zod to achieve a similar result but here we aren’t actually doing any validation on our client side. That happened on the server. We just know the types based on the schema since both ends share them. On the client, we are just transforming our serialized JSON into what we want it to be in our client application. Summary There are a lot of pieces in play to accomplish end-to-end type safety. With the help of JSON Schema and TypeBox though, it feels like light work for a semi-roll-your-own type of solution. Another great thing about it is that it’s flexible and based on pretty core concepts like a JSON API paired with a TypeScript-based client application. The number of benefits that you can reap from defining JSON Schemas for your APIs is really great. If you’re like me and wanna keep it simple by avoiding GraphQL or other similar tools, this is a great approach....

Apr 17, 2024

6 mins

JSONTypeScript

Transforming Platform Engineering Through Chargeback Programs with Shuchi Mittal

Shuchi Mittal, the Head of Cloud enablement at Honeywell, discusses how she as a leader in platform engineering has been able to transform internal platform engineering teams at other organizations by providing teams with value-added services, and how they effectively managed costs. She talks about how to treat these teams as customers, and delivering services that meet the customer needs, but also charging them for their usage. By providing policy-compliant infrastructure and unique services tailored to their requirements, platform engineering teams can showcase their commitment to supporting the success of other teams within an organization. This approach not only fosters trust but also positions platform engineering as a strategic partner rather than just a service provider. By going beyond the basic infrastructure provisioning, the platform engineering team can offer services that help development and product teams streamline their processes and improve efficiency. Shuchi shares her work at Fiserv where she implemented a chargeback system to track usage and costs effectively, incentivizing better development practices. This not only helped in managing costs but also encouraged teams to optimize their resource utilization, leading to improved overall efficiency and more easily scalable systems. Her platform engineering team recognized the importance of effectively managing financial aspects to secure upfront investment for product development. By implementing a billing system based on gigabyte hours, they aligned costs with usage, enabling teams to have a clear understanding of their resource consumption. This approach not only provided transparency but also incentivized teams to adopt cost-effective practices. By strategically managing financial aspects, the platform engineering team gained the trust of stakeholders and secured the necessary resources to drive innovation and deliver value to the organization. Shuchi’s journey of platform engineering serves as a valuable example of how a team can transition from being a basic service provider to becoming an innovation partner within an organization. By building trust, offering value-added services, and strategically managing financial aspects, the platform engineering team successfully elevated their role and became a strategic enabler of innovation. Download this episode here....

Apr 16, 2024

2 mins

Engineering LeadershipSoftware Engineering

Intro to PWA and Service Workers

Progressive Web Apps

What is a PWA?

What is a Service Worker?

Service Worker's Lifecycle

You might also like

Overview of the New Signal APIs in Angular

How to create and use custom GraphQL Scalars

End-to-end type-safety with JSON Schema

Transforming Platform Engineering Through Chargeback Programs with Shuchi Mittal

You might also like

Overview of the New Signal APIs in Angular

How to create and use custom GraphQL Scalars

End-to-end type-safety with JSON Schema

Transforming Platform Engineering Through Chargeback Programs with Shuchi Mittal