GraphQL

Introducing the New Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Published Jun 2, 2023

Updated Jun 2, 2023

7 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This article was written over 18 months ago and may contain information that is out of date.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.

Introducing the new Serverless, GraphQL, Apollo Server, and Contentful Starter kit

The team at This Dot Labs has released a brand new starter kit which includes the Serverless Framework, GraphQL, Apollo Server and Contentful configured and ready to go. This article will walk through how to set up the new kit, the key technologies used, and reasons why you would consider using this kit.

How to get started setting up the kit
How to Create the Technology Model in Contentful
How to seed the database with demo data
How to work with the migration scripts
Technologies included in this starter kit
Why use the Jest testing framework?
Project structure
How to deploy your application
What can this starter kit be used for?
Conclusion

How to get started setting up the kit

Generate the project

In the command line, you will need to start the starter.dev CLI by running the npx @this-dot/create-starter command. You can then select the Serverless Framework, Apollo Server, and Contentful CMS kit and name your new project. Then you will need to cd into your new project directory and install the dependencies using the tool of your choice (npm, yarn, or pnpm). Next, you will need to Run cp .env.example .env to copy the contents of the .env.example file into the .env file.

Setup Contentful access

You will first need to create an account on Contentful, if you don't have one already. Once you are logged in, you will need to create a new space. From there, go to Settings -> API keys and click on the Content Management Tokens tab. Next, click on the Generate personal token button and give your token a name. Copy your new Personal Access Token, and add it to the CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN variable. Then, go to Settings -> General settings to get the CONTENTFUL_SPACE_ID. The last step is to add those CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN and CONTENTFUL_SPACE_ID values to your .env file.

Setting up Docker

You will first need to install Docker Desktop if you don't have it installed already. Once installed, you can start up the Docker container with the npm run infrastructure:up command.

Starting the local server

While the Docker container is running, open up a new tab in the terminal and run npm run dev to start the development server. Open your browser to http://localhost:3000/dev/graphql to open up Apollo server.

How to Create the Technology Model in Contentful

To get started with the example model, you will first need to create the model in Contentful.

Log into your Contentful account
Click on the Content Model tab
Click on the Design your Content Modal button if this is your first modal
Create a new model called Technology
Add three new text fields called displayName, description and url
Save your new model

How to seed the database with demo data

This starter kit comes with a seeding script that pre-populates data for the Technology Content type.

In the command line, run npm run db:seed which will add three new data entries into Contentful.

If you want to see the results from seeding the database, you can execute a small GraphQL query using Apollo server.

First, make sure Docker, and the local server(npm run dev) are running, and then navigate to http://localhost:3000/dev/graphql.

Add the following query:

query TechnologyQuery {
  technologies {
    description
    displayName
    url
  }
}

When you run the query, you should see the following output.

{
  "data": {
    "technologies": [
      {
        "description": "GraphQL provides a strong-typing system to better understand and utilize our API to retrieve and interact with our data.",
        "displayName": "GraphQL",
        "url": "https://graphql.framework.dev/"
      },
      {
        "description": "Node.js® is an open-source, cross-platform JavaScript runtime environment.",
        "displayName": "Node.js",
        "url": "https://nodejs.framework.dev/"
      },
      {
        "description": "Express is a minimal and flexible Node.js web application framework.",
        "displayName": "Express",
        "url": "https://www.npmjs.com/package/express"
      }
    ]
  }
}

How to work with the migration scripts

Migrations are a way to make changes to your content models and entries. This starter kit comes with a couple of migration scripts that you can study and run to make changes to the demo Technology model. These migration scripts are located in the scripts/migration directory.

To get started, you will need to first install the contentful-cli.

   npm i -g contentful-cli

You can then login to Contentful using the contentful-cli.

   contentful login

You will then need to choose the Contentful space where the Technology model is located.

   contentful space use

If you want to modify the existing demo content type, you can run the second migration script from the starter kit.

   contentful space migration scripts/migrations/02-edit-technology-contentType.js -y

If you want to build out more content models using the CLI, you can study the example code in the /scripts/migrations/01-create-technology-contentType.js file. From there, you can create a new migration file, and run the above contentful space migration command.

If you want to learn more about migration in Contentful, then please check out the documentation.

Technologies included in this starter kit

Why use GraphQL?

GraphQL is a query language for your API and it makes it easy to query all of the data you need in a single request. This starter kit uses GraphQL to query the data from our Contentful space.

Why use Contentful?

Contentful is a headless CMS that makes it easy to create and manage structured data. We have integrated Contentful into this starter kit to make it easy for you to create new entries in the database.

Why use Amazon Simple Queue Service (SQS)?

Amazon Simple Queue Service (SQS) is a queuing service that allows you to decouple your components and process and store messages in a scalable way.

In this starter kit, an SQS message is sent by the APIGatewayProxyHandler using the sendMessage function, which is then stored in a queue called DemoJobQueue. The SQS handler sqs-handler polls this queue, and processes any message received.

import { APIGatewayProxyHandler } from "aws-lambda";
import { sendMessage } from "../utils/sqs";

export const handler: APIGatewayProxyHandler = async (event) => {
  const body = JSON.parse(event.body || "{}");
  const resp = await sendMessage({
    id: Math.ceil(Math.random() * 100),
    message: body.message,
  });

  return {
    statusCode: resp.success ? 200 : 400,
    body: JSON.stringify(resp.data),
  };
};

Why use Apollo Server?

Apollo Server is a production-ready GraphQL server that works with any GraphQL client, and data source. When you run npm run dev and open the browser to http://localhost:3000/dev/graphql, you will be able to start querying your Contentful data in no time.

Why use the Serverless Framework?

The Serverless Framework is used to help auto-scale your application by using AWS Lambda functions. In the starter kit, you will find a serverless.yml file, which acts as a configuration for the CLI and allows you to deploy your code to your chosen provider.

This starter kit also includes the following plugins:

serverless-offline - allows us to deploy our application locally to speed up development cycles.
serverless-plugin-typescript - allows the use of TypeScript with zero-config.
serverless-dotenv-plugin - preloads function environment variables into the Serverless Framework.

Why use Redis?

Redis is an open-source in-memory data store that stores data in the server memory. This starter kit uses Redis to cache the data to reduce the API response times and rate limiting. When you make a new request, those new requests will be retrieved from the Redis cache.

Why use the Jest testing framework?

Jest is a popular testing framework that works well for creating unit tests.

You can see some example test files under the src/schema/technology directory. You can use the npm run test command to run all of the tests.

Project structure

Inside the src directory, you will find the following structure:

.
├── generated
│   └── graphql.ts
├── handlers
│   ├── graphql.ts
│   ├── healthcheck.spec.ts
│   ├── healthcheck.ts
│   ├── sqs-generate-job.spec.ts
│   ├── sqs-generate-job.ts
│   ├── sqs-handler.spec.ts
│   └── sqs-handler.ts
├── models
│   └── Technology
│       ├── create.spec.ts
│       ├── create.ts
│       ├── getAll.spec.ts
│       ├── getAll.ts
│       ├── getById.spec.ts
│       ├── getById.ts
│       ├── index.ts
│       ├── TechnologyModel.spec.ts
│       └── TechnologyModel.ts
├── schema
│   ├── technology
│   │   ├── index.ts
│   │   ├── technology.resolver.spec.ts
│   │   ├── technology.resolvers.ts
│   │   └── technology.typedefs.ts
│   └── index.ts
└── utils
    ├── contentful
    │   ├── contentful-healthcheck.spec.ts
    │   ├── contentful-healthcheck.ts
    │   ├── contentful.spec.ts
    │   ├── contentful.ts
    │   └── index.ts
    ├── redis
    │   ├── index.ts
    │   ├── redis-healthcheck.spec.ts
    │   ├── redis-healthcheck.ts
    │   ├── redis.spec.ts
    │   └── redis.ts
    ├── sqs
    │   ├── client.spec.ts
    │   ├── client.ts
    │   ├── getQueueUrl.spec.ts
    │   ├── getQueueUrl.ts
    │   ├── index.ts
    │   ├── is-offline.spec.ts
    │   ├── is-offline.ts
    │   ├── sendMessage.spec.ts
    │   └── sendMessage.ts
    └── test
        └── mocks
            ├── contentful
            │   ├── entry.ts
            │   └── index.ts
            ├── aws-lambda-handler-context.ts
            ├── graphql.ts
            ├── index.ts
            └── sqs-record.ts

This given structure makes it easy to find all of the code and tests related to that specific component. This structure also follows the single responsibility principle which means that each file has a single purpose.

How to deploy your application

The Serverless Framework needs access to your cloud provider account so that it can create and manage resources on your behalf. You can follow the guide to get started.

Steps to get started:

Sign up for an AWS account
Create an IAM User and Access Key

Export your AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY credentials.

   export AWS_ACCESS_KEY_ID=<your-key-here>
   export AWS_SECRET_ACCESS_KEY=<your-secret-key-here>

Deploy your application on AWS Lambda:
```
   npm run deploy
```

To deploy a single function, run:

   npm run deploy function --function myFunction

To stop your Serverless application, run:

   serverless remove

For more information on Serverless deployment, check out this article.

What can this starter kit be used for?

This starter kit is very versatile, and can be used with a front-end application for a variety of situations.

Here are some examples:

personal developer blog
small e-commerce application

Conclusion

In this article, we looked at how we can get started using the Serverless, GraphQL, Apollo Server, and Contentful Starter kit. We also looked at the different technologies used in the kit, and why they were chosen. Lastly, we looked at how to deploy our application using AWS.

I hope you enjoy working with our new starter kit!

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.

About the author(s)

Jessica Wilkins
Jessica Wilkins is a classical musician turned Software Engineer. Prior to joining the tech industry, she spent her time running her own sheet music company (JDW Sheet Music) as well as performing and teaching in Los Angeles, CA. She enjoys working with React and TypeScript. She is also a prolific technical writer for freeCodeCamp.
@codergirl1991 @jdwilkin4

Leveraging GraphQL Scalars to Enhance Your Schema

Introduction GraphQL has revolutionized the way developers approach application data and API layers, gaining well-deserved momentum in the tech world. Yet, for all its prowess, there's room for enhancement, especially when it comes to its scalar types. By default, GraphQL offers a limited set of these primitives — Int, Float, String, Boolean, and ID — that underpin every schema. While these types serve most use cases, there are scenarios where they fall short, leading developers to yearn for more specificity in their schemas. Enter graphql-scalars, a library designed to bridge this gap. By supplementing GraphQL with a richer set of scalar types, this tool allows for greater precision and flexibility in data representation. In this post, we'll unpack the potential of enhanced Scalars, delve into the extended capabilities provided by graphql-scalars, and demonstrate its transformative power using an existing starter project. Prepare to redefine the boundaries of what your GraphQL schema can achieve. Benefits of Using Scalars GraphQL hinges on the concept of "types." Scalars, being the foundational units of GraphQL's type system, play a pivotal role. While the default Scalars — Int, Float, String, Boolean, and ID — serve many use cases, there's an evident need for more specialized types in intricate web development scenarios. 1. Precision: Using default Scalars can sometimes lack specificity. Consider representing a date or time in your application with a String; this might lead to ambiguities in format interpretation and potential inconsistencies. 2. Validation: Specialized scalar types introduce inherent validation. Instead of using a String for an email or a URL, for example, distinct types ensure the data meets expected formats at the query level itself. 3. Expressiveness: Advanced Scalars provide clearer intentions. They eliminate ambiguity inherent in generic types, making the schema more transparent and self-explanatory. Acknowledging the limitations of the default Scalars, tools like graphql-scalars have emerged. By broadening the range of available data types, graphql-scalars allows developers to describe their data with greater precision and nuance. Demonstrating Scalars in Action with Our Starter Project To truly grasp the transformative power of enhanced Scalars, seeing them in action is pivotal. For this, we'll leverage a popular starter kit: the Serverless framework with Apollo and Contentful. This kit elegantly blends the efficiency of serverless functions with the power of Apollo's GraphQL and Contentful's content management capabilities. Setting Up the Starter: 1. Initialize the Project: ` 2. When prompted, name your project enhance-with-graphql-scalars. ` 3. For a detailed setup, including integrating with Contentful and deploying your serverless functions, please follow the comprehensive guide provided in the starter kit here. 4. And we add the graphql-scalars package ` Enhancing with graphql-scalars: Dive into the technology.typedefs.ts file, which is the beating heart of our GraphQL type definitions for the project. Initially, these are the definitions we encounter: ` Our enhancement strategy is straightforward: * Convert the url field from a String to the URL scalar type, bolstering field validation to adhere strictly to the URL format. Post-integration of graphql-scalars, and with our adjustments, the revised type definition emerges as: ` To cap it off, we integrate the URL type definition along with its resolvers (sourced from graphql-scalars) in the schema/index.ts file: ` This facelift doesn't just refine our GraphQL schema but infuses it with innate validation, acting as a beacon for consistent and accurate data. Testing in the GraphQL Sandbox Time to witness our changes in action within the GraphQL sandbox. Ensure your local server is humming along nicely. Kick off with verifying the list query: ` Output: ` Success! Each url in our dataset adheres to the pristine URL format. Any deviation would've slapped us with a format error. Now, let's court danger. Attempt to update the url field with a _wonky_ format: ` As anticipated, the API throws up a validation roadblock: ` For the final act, re-run the initial query to reassure ourselves that the original dataset remains untarnished. Conclusion Enhancing your GraphQL schemas with custom scalars not only amplifies the robustness of your data structures but also streamlines validation and transformation processes. By setting foundational standards at the schema level, we ensure error-free, consistent, and meaningful data exchanges right from the start. The graphql-scalars library offers an array of scalars that address common challenges developers face. Beyond the URL scalar we explored, consider diving into other commonly used scalars such as: * DateTime: Represents date and time in the ISO 8601 format. * Email: Validates strings as email addresses. * PositiveInt: Ensures integer values are positive. * NonNegativeFloat: Guarantees float values are non-negative. As a potential next step, consider crafting your own custom scalars tailored to your project's specific requirements. Building a custom scalar not only offers unparalleled flexibility but also provides deeper insights into GraphQL's inner workings and its extensibility. Remember, while GraphQL is inherently powerful, the granular enhancements like scalars truly elevate the data-fetching experience for developers and users alike. Always evaluate your project's needs and lean into enhancements that bring the most value. To richer and more intuitive GraphQL schemas!...

Nov 8, 2023

4 mins

GraphQL

How to Leverage Apollo Client Fetch Policies Like the Pros

Apollo Client provides a rich ecosystem and cache for interfacing with your GraphQL APIs. You write your query and leverage the useQuery hook to fetch your data. It provides you with some state context and eventually resolves your query. That data is stored in a local, normalized, in-memory cache, which allows Apollo Client to respond to most previously run requests near instantaneously. This has huge benefits for client performance and the feel of your apps. However, sometimes Apollo's default doesn't match the user experience you want to provide. They provide fetch policies to allow you to control this behavior on each query you execute. In this article, we'll explore the different fetch policies and how you should leverage them in your application. cache-first This is the default for Apollo Client. Apollo will execute your query against the cache. If the cache can fully fulfill the request, then that's it, and we return to the client. If it can only partially match your request or cannot find any of the related data, the query will be run against your GraphQL server. The response is cached for the next query and returned to the handler. This method prioritizes minimizing the number of requests sent to your server. However, it has an adverse effect on data that changes regularly. Think of your social media feeds - they typically contain constantly changing information as new posts are generated. Or a real-time dashboard app tracking data as it moves through a system. cache-first is probably not the best policy for these use cases as you won't fetch the latest data from the upstream source. You can lower the cache time of items for the dashboard to avoid the staleness issue and still minimize the requests being made, but this problem will persist for social media feeds. The cache-first policy should be considered for data that does not change often in your system or data that the current user fully controls. Data that doesn't change often is easily cached, and that's a recommended pattern. For data that the user controls, we need to consider how that data changes. If only the current user can change it, we have 2 options: Return the updated data in the response of any mutation which is used to update the cache Use cache invalidation methods like refetchQueries or onQueryUpdated These methods will ensure that our cache stays in sync with our server allowing the policy to work optimally. However, if other users in the system can make changes that impact the current user's view, then we can not invalidate the cache properly using these strategies which makes this policy unideal. network-only This policy skips the cache lookup and goes to the server to fetch the results. The results are stored in the cache for other operations to leverage. Going back to the example I gave in my explanation cache-first of a social media feed, the network-only policy would be a great way to implement the feed itself as it's ever-changing, and we'll likely even want to poll for changes every 10s or so. The following is an example of what this component could look like: ` Whenever this SocialFeed component is rendered, we always fetch the latest results from the GraphQL server ensuring we're looking at the current data. The results are put in the cache which we can leverage in some children components. cache-only cache-only only checks the cache for the requested data and never hits the server. It throws an error if the specified cache items cannot be found. At first glance, this cache policy may seem unhelpful because it's unclear if our cache is seeded with our data. However, in combination with the network-only policy above, this policy becomes helpful. This policy is meant for components down tree from network-only level query. This method is for you if you're a fan of React components' compatibility. We can modify the return of our previous example to be as follows: ` Notice we're not passing the full post object as a prop. This simplifies our Post component types and makes later refactors easier. The Post would like like the following: ` In this query, we're grabbing the data directly from our cache every time because our top-level query should have fetched it. Now, a small bug here makes maintainability a bit harder. Our top-level GetFeed query doesn't guarantee fetching the same fields. Notice how our Post component exports a fragment. Fragments are a feature Apollo supports to share query elements across operations. In our SocialFeed component, we can change our query to be: ` Now, as we change our Post to use new fields and display different data, the refactoring is restricted to just that component, and the upstream components will detect the changes and handle them for us making our codebase more maintainable. Because the upstream component is always fetching from the network, we can trust that the cache will have our data, making this component safe to render. With these examples, though, our users will likely have to see a loading spinner or state on every render unless we add some server rendering. cache-and-network This is where cache-and-network comes to play. With this policy, Apollo Client will run your query against your cache and your GraphQL server. This further simplifies our example above if we want to provide the last fetched results to the user but then update the feed immediately upon gathering the latest data. This is similar to what X/Twitter does when you reload the app. You'll see the last value that was in the cache then it'll render the network values when ready. This can cause a jarring user experience though, if the data is changing a lot over time, so I recommend using this methodology sparsely. However, if you wanted to update our existing example, we'd just change our SocialFeed component to use this policy, and that'll keep our client and server better in sync while still enabling 10s polling. no-cache This policy is very similar to the network-only policy, except it bypasses the local cache entirely. In our previous example, we wrote engagement as a sub-selector on a Post and stored fields there. These metrics can change in real time pretty drastically. Chat features, reactions, viewership numbers, etc., are all types of data that may change in real time. The no-cache policy is good when this type of data is active, such as during a live stream or within the first few hours of a post going out. You may typically want to use the cache-and-network policy eventually but during that active period, you'll probably want to use no-cache so your consumers can trust your data. I'd probably recommend changing your server to split these queries and run different policies for the operations for performance reasons. I haven't mentioned this yet, but you can make the fetch policy on a query dynamic, meaning you combine these different policies' pending states. This could look like the following: ` We pass whether the event is live to the component that then leverages that info to determine if we should cache or not when fetching the chat. That being said, we should consider using subscription operations for this type of feature as well, but that's an exercise for another blog post. standby This is the most uncommon fetch policy, but has a lot of use. This option runs like a cache-first query when it executes. However, by default, this query does not run and is treated like a "skip" until it is manually triggered by a refetch or updateQueries caller. You can achieve similar results by leveraging the useLazyQuery operator, but this maintains the same behavior as other useQuery operators so you'll have more consistency among your components. This method is primarily used for operations pending other queries to finish or when you want to trigger the caller on a mutation. Think about a dashboard with many filters that need to be applied before your query executes. The standby fetch policy can wait until the user hits the Apply or Submit button to execute the operation then calls a await client.refetchQueries({ include: ["DashboardQuery"] }), which will then allow your component to pull in the parameters for your operation and execute it. Again, you could achieve this with useLazyQuery so it's really up to you and your team how you want to approach this problem. To avoid learning 2 ways, though, I recommend picking just one path. Conclusion Apollo Client's fetch policies are a versatile and helpful tool for managing your application data and keeping it in sync with your GraphQL server. In general, you should use the defaults provided by the library, but think about the user experience you want to provide. This will help you determine which policy best meets your needs. Leveraging tools like fragments will enable you to manage your application and use composable patterns more effectively. With the rise of React Server Components and other similar patterns, you'll need to be wary of how that impacts your Apollo Client strategy. However, if you're on a legacy application that leverages traditional SSR patterns, Apollo allows you to pre-render queries on the server and their related cache. When you combine these technologies, you'll find that your apps perform great, and your users will be delighted....

May 17, 2024

7 mins

GraphQL

How to Handle Uploaded Images and Avoid Image Distortion

When you are working with images in your application, you might run into issues where the image's aspect ratio is different from the container's specified width and height. This could lead to images looking stretched and distorted. In this article, we will take a look at how to solve this problem by using the object-fit CSS property. A Look Into the Issue Using the "Let's Chat With" App Let's Chat With is an open source application that facilitates networking between attendees for virtual and in-person conferences. When users sign up for the app, they can join a conference and create a new profile with their name, image, and bio. When the team at This Dot Labs was testing the application, they noticed that some of the profile images were coming out distorted. The original uploaded source image did not have an aspect ratio of 1:1. A 1:1 aspect ratio refers to an image's width and height being the same. Since the image was not a square, it was not fitting well within the dimensions below. ` In order to fix this problem, the team decided to use the object-fit CSS property. What is the object-fit CSS property? The object-fit property is used to determine how an image or video should resize in order to fit inside its container. There are 5 main values you can use with the object-fit property. - object-fit: contain; - resizes the content to fit inside the container without cropping it - object-fit: cover; - ensures the all of the content covers the container and will crop if necessary - object-fit: fill; - fills the container with the content by stretching it and ignoring the aspect ratio. This could lead to image distortion. - object-fit: none; - does not resize the content which could lead to the content spilling out of the container - object-fit: scale-down; - scales larger content down to fit inside the container When the object-fit: cover; property was applied to the profile image in Let's Chat With, the image was no longer distorted. ` When Should You Consider Using the object-fit Property? There will be times where you will not be able to upload different sized images to fit different containers. You might be in a situation like Let's Chat With, where the user is uploading images to your application. In that case, you will need to apply a solution to ensure that the content appropriately resizes within the container without becoming distorted. Conclusion In this article, we learned about how to fix distorted uploaded images using the object-fit property. We examined the bug inside the Let's Chat With application and how that bug was solved using object-fit: cover;. We also talked about when you should consider using the object-fit property. If you want to check out the Let's Chat with app, you can signup here. If you are interested in contributing to the app, you can check out the GitHub repository....

Jul 28, 2023

3 mins

JavaScript

Svelte 5 is Here!

Svelte 5 was finally released after a long time in development. Fortunately, we've been able to test it for some time, and now it has a stable release. Let's dig into its features and why this is such a significant change, even though Svelte 4 code is almost 100% compatible. Svelte syntax everywhere This is one of my favorite additions. Previously, Svelte syntax was limited to a component element. Refactoring code from a component and moving it to a JavaScript file worked differently. Now you can use the .svelte.js or .svelte.ts extension, which allows you to use the same syntax everywhere. It's important to note that it's a way to express that this is not just JS, and what you write may be compiled to something else, just like .svelte files are not just html even though they look very similar. Runes The introduction of runes is one of the most significant changes in Svelte. Many users felt attracted to variables being instantly reactive in previous versions. ` There was, however, a lot of magic underneath and a considerable amount of work from the compiler to make it behave reactively. $state In Svelte 5, a reactive variable has to be explicitly declared using the $state rune, which brings a clear distinction between what is reactive and what isn’t. ` In the previous code, $state is not an actual function being called; it's a hint for the compiler to do something special with this declaration. Rune names always start with a dollar sign ($) and do not need to be imported. However, there’s much more to the changes than just the way we declare reactive variables. Runes bring a lot of new features that make Svelte 5 a great improvement. In Svelte 5, objects or arrays use Proxies to allow for granular reactivity, meaning that individual properties in an object are reactive, even if they are nested objects or arrays. If you modify a property, it will only update that property and will not trigger an update on the whole object. It also supports triggering updates when methods like array.push are called. In previous versions, an assignment was required to trigger an update: ` To have a similar behavior of svelte 4 (no deep reactivity), use the $state.raw() rune. This syntax of .* is common for related features of a rune: $state.raw() will require an assignment to trigger reactivity. ` Because proxies are used, you may need to extract the underlying state instead of the proxy itself. In that case, $state.snapshot() should be used: ` $derived We will use $derived and $derived.by to declare a derived state. The two runes are essentially the same, except one allows us to use a function instead of an expression, allowing for more complex operations to calculate the derived values. ` $effect Effects are useful for running something triggered by a change in state. One of the things you'll note from the $effect rune is that dependencies don't need to be explicit. Those will be picked from reactive values read synchronously in the body of the effect function. ` Something to bear in mind is that these dependencies are picked each time the effect runs. The values read during the last run become the dependencies of the effect. ` Depending on the result of the random method, foo or bar will stop being a dependency of the effect. You should place them outside the condition so they can trigger reruns of the effect. *Variants* of the effect rune are $effect.pre, which runs before a DOM update, and $effect.tracking(), which checks for the context of the effect (true for an effect inside an effect or in the template). $props This rune replaces the export let keywords used in previous versions to define a component's props. To use the $props syntax, we can consider it to return an object with all the properties a component receives. We can use JavaScript syntax to destructure it or use the rest property if we want to retrieve every property not explicitly destructured.. ` $bindable If you want to mutate a prop so that the change flows back to the parent, you can use the $bindable prop to make it work in both directions. A parent component can pass a value to a child component, and the child component is able to modify it, returning the data back to the parent component. ` $inspect The $inspect rune only works in dev mode and will track dependencies deeply. By default, it will call console.log with the provided argument whenever it detects a change. To change the underlying function, use $inspect.with().with): ` $host The host rune gives access to the host element when compiling as a custom element: ` ` Other changes Another important change is that component events are just props in Svelte 5, so you can destructure them using the $props() rune: ` A special children property can be used to project content into a component instead of using slots. Inside the components, use the [@render[(https://svelte.dev/docs/svelte/@render) tag to place them. ` Optional chaining (?.) prevents from attempting to render it if no children are passed in. If you need to render different components in different places (named slots before Svelte 5, you can pass them as any other prop, and use @render with them. Snippets Snippets allow us to declare markup slices and render them conveniently using the render tag (@render). They can take any number of arguments. ` Snippets can also be passed as props to other components. ` ` Conclusion Some exciting changes to the Svelte syntax were introduced while, at the same time, maximum compatibility efforts were made. This was a massive rewrite with numerous improvements in terms of performance, bundle size, and DX. Besides that, a new CLI has been released, making the whole experience of starting a project or adding features delightful. If you haven't tried Svelte before, it's a great time to try it now....

Nov 27, 2024

4 mins

Svelte

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Introducing the New Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Introducing the new Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Table of Contents

How to get started setting up the kit

Generate the project

Setup Contentful access

Setting up Docker

Starting the local server

How to Create the Technology Model in Contentful

How to seed the database with demo data

How to work with the migration scripts

Technologies included in this starter kit

Why use GraphQL?

Why use Contentful?

Why use Amazon Simple Queue Service (SQS)?

Why use Apollo Server?

Why use the Serverless Framework?

Why use Redis?

Why use the Jest testing framework?

Project structure

How to deploy your application

What can this starter kit be used for?

Conclusion

Jessica Wilkins

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