GraphQL

How to create and use custom GraphQL Scalars

Published Apr 10, 2024

Updated Apr 10, 2024

5 min read

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:

// src/scalars/color.scalar.ts
import { GraphQLScalarType, Kind } from 'graphql';

const validateColor = (value: string): string => {
	const regex = /^#[0-9A-Fa-f]{6}$/;
	if (!regex.test(value)) {
		throw new Error(`Value is not a valid color: ${value}`);
	}
	return value;
};

export const colorScalar = new GraphQLScalarType({
	name: 'Color',
	description: 'Color custom scalar type for representing colors in hexadecimal',
	parseValue(value) {
		if (typeof value === 'string') {
			return validateColor(value);
		}
		throw new Error(`Value is not a valid string: ${value}`);
	},
	serialize(value) {
		if (typeof value === 'string') {
			return validateColor(value);
		}
		throw new Error(`Value is not a valid string: ${value}`);
	},
	parseLiteral(ast) {
		if (ast.kind === Kind.STRING) {
			return validateColor(ast.value);
		}
		return null;
	},
});

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

Add the scalar to Type Definitions: Include the Color scalar in your GraphQL type definitions. This inclusion informs GraphQL about this new scalar type.
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.

// src/schema/index.ts
import { mergeResolvers, mergeTypeDefs } from '@graphql-tools/merge';
import { URLResolver, URLTypeDefinition } from 'graphql-scalars';
import gql from 'graphql-tag';
import { colorScalar } from '../scalars/color.scalar';
import { technologyResolvers, technologyTypeDefs } from './technology';

// Define the new custom scalar type
const colorTypeDef = gql`
	scalar Color
`;

const graphqlScalarTypeDefs = [URLTypeDefinition, colorTypeDef];
const graphqlScalarResolvers = {
	URL: URLResolver,
	Color: colorScalar, // add the new scalar to the resolver
};

export const typeDefs = mergeTypeDefs([...graphqlScalarTypeDefs, technologyTypeDefs]);

export const resolvers = mergeResolvers([{ ...graphqlScalarResolvers }, technologyResolvers]);

Use the scalar: Update your type to use the new custom scalar

// src/chema/technology/technology.typedefs.ts
import gql from 'graphql-tag';

export const technologyTypeDefs = gql`
	type Technology {
		id: ID!
		displayName: String!
		description: String
		url: URL
		primaryColor: Color
	}
... // rest of the schema
`;

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!

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About the author(s)

William Mimura
@mimurawil

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

In today’s blog post, we’ll build an AI Assistant using three different AI models: Whisper and TTS from OpenAI and Llama 3.1 from Meta. While exploring AI, I wanted to try different things and create an AI assistant that works by voice. This curiosity led me to combine OpenAI’s Whisper and TTS models with Meta’s Llama 3.1 to build a voice-activated assistant. Here’s how these models will work together: * First, we’ll send our audio to the Whisper model, which will convert it from speech to text. * Next, we’ll pass that text to the Llama 3.1 model. Llama will understand the text and generate a response. * Finally, we’ll take Llama’s response and send it to the TTS model, turning the text back into speech. We’ll then stream that audio back to the client. Let’s dive in and start building this excellent AI Assistant! Getting started We will use different tools to build our assistant. To build our client side, we will use Next.js. However, you could choose whichever framework you prefer. 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Kicking things off It's time to kick things off by setting up our Next.js app. Let's start with this command: ` After running the command, you’ll see a few prompts to set the app's details. Let's go step by step: * Name your app. * Enable app router. The other steps are optional and entirely up to you. In my case, I also chose to use TypeScript and Tailwind CSS. Now that’s done, let’s go into our project and install the dependencies that we need to run our models: ` Building our client logic Now, our goal is to record our voice, send it to the backend, and then receive a voice response from it. To record our audio, we need to use client-side functions, which means we need to use client components. In our case, we don’t want to transform our whole page to use client capabilities and have the whole tree in the client bundle; instead, we would prefer to use Server components and import our client components to progressively enhance our application. So, let’s create a separate component that will handle the client-side logic. Inside our app folder, let's create a components folder, and here, we will be creating our component: ` Let’s go ahead and initialize our component. I went ahead and added a button with some styles in it: ` And then import it into our Page Server component: ` Now, if we run our app, we should see the following: Awesome! Now, our button doesn’t do anything, but our goal is to record our audio and send it to someplace; for that, let us create a hook that will contain our logic: ` We will use two APIs to record our voice: navigator and MediaRecorder. The navigator API will give us information about the user’s media devices like the user media audio, and the MediaRecorder will help us record the audio from it. This is how they’re going to play out together: ` Let’s explain this code step by step. First, we create two new states. 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We would need a new ref, and this is where our chunks of audio data will be stored. ` In our useEffect we are going to do two main things: store those chunks in our ref, and when it stops, we are going to create a new Blob of type audio/mp3: ` It is time to wire this hook with our AudioRecorder component: ` Let’s go to the other side of the coin, the backend! Setting up our Server side We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: ` Our route is called ‘chat’. In the route.ts file, we’ll set up our handler. 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You can find the complete demo on GitHub: AI Assistant with Whisper TTS and Ollama using Next.js...

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D1 SQLite: Writing queries with the D1 Client API

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In this exampl, we reverse the previous binding. ` Reusing prepared statements If we take the first example above and not bind any values we have a statement that can be reused: ` Querying For the purposes of this post we will just build example queries by writing them out directly in our Worker fetch handler. If you are building an app I would recommend building functions or some other abstraction around your queries. select queries Let's write our first query against our data set to get our feet wet. Here’s the initial worker code and a query for all authors: ` We pass our SQL statement into prepare and use the all method to get all the rows. Notice that we are able to pass our types to a generic parameter in all. This allows us to get a fully typed response from our query. We can run our worker with npm run dev and access it at http://localhost:8787 by default. We’ll keep this simple workflow of writing queries and passing them as a json response for inspection in the browser. Opening the page we get our author results. joins Not using an ORM means we have full control over our own destiny. Like anything else though, this has tradeoffs. Let’s look at a query to fetch the list of posts that includes author and tags information. ` Let’s walk through each part of the query and highlight some pros and cons. ` * The query selects all columns from the posts table. * It also selects the name column from the authors table and renames it to author_name. * It aggregates the name column from the tags table into a JSON array. If there are no tags, it returns an empty JSON array. This aggregated result is renamed to tags. ` * The query starts by selecting data from the posts table. * It then joins the authors table to include author information for each post, matching posts to authors using the author_id column in posts and the id column in authors. * Next, it left joins the posts_tags table to include tag associations for each post, ensuring that all posts are included even if they have no tags. * Next, it left joins the tags table to include tag names, matching tags to posts using the tag_id column in posts_tags and the id column in tags. * Finally, group the results by the post id so that all rows with the same post id are combined in a single row SQL provides a lot of power to query our data in interesting ways. JOIN ’s will typically be more performant than performing additional queries.You could just as easily write a simpler version of this query that uses subqueries to fetch post tags and join all the data by hand with JavaScript. This is the nice thing about writing SQL, you’re free to fetch and handle your data how you please. Our results should look similar to this: ` This brings us to our next topic. Marshaling / coercing result data A couple of things we notice about the format of the result data our query provides: Rows are flat. We join the author directly onto the post and prefix its column names with author. ` Using an ORM we might get the data back as a child object: ` Another thing is that our tags data is a JSON string and not a JavaScript array. This means that we will need to parse it ourselves. ` This isn’t the end of the world but it is some more work on our end to coerce the result data into the format that we actually want. This problem is handled in most ORM’s and is their main selling point in my opinion. insert / update / delete Next, let’s write a function that will add a new post to our database. ` There’s a few queries involved in our create post function: * first we create the new post * next we run through the tags and either create or return an existing tag * finally, we add entries to our post_tags join table to associate our new post with the tags assigned We can test our new function by providing post content in query params on our index page and formatting them for our function. ` I gave it a run like this: http://localhost:8787authorId=1&tags=Food%2CReview&title=A+review+of+my+favorite+Italian+restaurant&content=I+got+the+sausage+orchette+and+it+was+amazing.+I+wish+that+instead+of+baby+broccoli+they+used+rapini.+Otherwise+it+was+a+perfect+dish+and+the+vibes+were+great And got a new post with the id 11. UPDATE and DELETE operations are pretty similar to what we’ve seen so far. Most complexity in your queries will be similar to what we’ve seen in the posts query where we want to JOIN or GROUP BY data in various ways. To update the post we can write a query that looks like this: ` COALESCE acts similarly to if we had written a ?? b in JavaScript. If the binded value that we provide is null it will fall back to the default. We can delete our new post with a simple DELETE query: ` Transactions / Batching One thing to note with D1 is that I don’t think the traditional style of SQLite transactions are supported. You can use the db.batch API to achieve similar functionality though. According to the docs: Batched statements are SQL transactions ↗. If a statement in the sequence fails, then an error is returned for that specific statement, and it aborts or rolls back the entire sequence. ` Summary In this post, we've taken a hands-on approach to exploring the D1 Client API, starting with defining our database schema and loading seed data. We then dove into writing queries, covering the basics of prepared statements and parameter binding, before moving on to more complex topics like joins and transactions. We saw how to construct and execute queries to fetch data from our database, including how to handle relationships between tables and marshal result data into a usable format. We also touched on inserting, updating, and deleting data, and how to use transactions to ensure data consistency. By working through these examples, we've gained a solid understanding of how to use the D1 Client API to interact with our database and build robust, data-driven applications....

Dec 23, 2024

6 mins

JavaScript

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How to create and use custom GraphQL Scalars

How to create and use custom GraphQL Scalars

Understanding Custom Scalars

Creating a Custom Scalar

Defining a Custom Scalar in TypeScript:

Example: A 'Color' Scalar in TypeScript

Integrating Custom Scalars into a Schema

Incorporating the 'Color' Scalar

Step-by-Step Integration

Testing the Integration

Conclusion

William Mimura

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