GraphQL

Federation, Roadmaps, GraphiQL & more: GraphQL Contributor Day | May 3, 2022

Published May 23, 2022

Updated Feb 13, 2023

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This article was written over 18 months ago and may contain information that is out of date.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

The latest GraphQL Contributor Day Event focused on new updates to the GraphQL ecosystem, including packages like GraphiQL, GraphQL DataLoader, and other GraphQL clients.

Topics covered at this event included GraphQL federation, industry best practices, the latest available features, and roadmaps for tools like DataLoader.

Don't forget about the upcoming GraphQL Conf scheduled for June 7th, 2022!

Here is a complete list of the hosts and guests that attended the online event.

Hosts

Dustin Goodman, Engineering Manager, This Dot Labs, @dustinsgoodman
Tanmai Gopal, Founder, Hasura, @tanmaigo

Guests

Adhithi Ravichandran, Software Consultant at Surya Consulting Inc., @AdhithiRavi
Eitan Joffe, CTO at Inigo Labs, @eitanjoffe
Gabriel Nordeborn, Partner at Arizon, @zth
Jamie Barton, Full Stack Developer, @notrab
Joey Nenni, Staff Engineer at Paypal, @JoeyNenni
Kyle Schrade, Senior Software Engineer at StockX, @notkyleschrade
Mandi Wise, Senior Manager Solutions Architecture at Apollo, @mandiwise
Marc-André Giroux, Senior Software Developer at Netflix, @xuorig
Roy Derks, Developer Relations at StepZen, @gethackteam
Uri Goldshtein, Founder of The Guild, @UriGoldshtein

You can watch the entire GraphQL Contributor Day event on This Dot Media's YouTube Channel.

New GraphQL Updates

GraphiQL Updates

The latest GraphiQL updates include:

A newly proposed design
Support for multiple operation tabs
Support for design customization of GraphiQL itself
A proposal for new official maintainance structure on how to manage the project, the people, and PR approval for the repository

Dataloader Updates

According to Uri Goldshtein, The Guild will maintain DataLoader. New versions already released, and more are planned for the future, including releases that enable Typescript migration.

The roadmap for DataLoader's future releases are in place. If you have any suggestions for improvements, check out the Dataloader Roadmap.

If you want to learn more about DataLoaders, Jamie Barton and This Dot Media have free trainings available. You can check out Jamie's video Batching with DataLoader or This Dot's Intro to GraphQL DataLoaders.

The panelists were interested in the possibility of DataLoaders being brought into GraphQL as an internal implementation. However, no such implementation currently exists, and most of the present work involves trying to bring the repo up-to-date while answering the community’s questions, and providing documentation.

How does DataLoader differ from Apollo's DataSource concept?

Apollo's data sources is a great tool for retrieving data from a given source, such as REST APIs and databases.

Like DataLoaders, data sources attempt cache results to avoid double fetches.

With DataSource, you can abstract some of the data fetching methods, and easily connect to the methods. There isn't anything built into Datasource around batching, but it does support some mechanism for caching and data fetching.

GraphQL Conf 2022

GraphQL Conf 2022 is coming to Houston, Texas on June 7th. The panelists were excited to finally attend an in-person event again, and were also thrilled to know that the event would be in partnership with the Openjs Foundation.

GraphQL Conf is a conference for GraphQL enthusiasts focused on GraphQL tooling and implementations. Visit GraphQL Conf 2022 if you're interested in speaking at, or attending the conference.

GraphQL Server Side Updates

GraphQL Yoga

GraphQL Yoga was created, and is maintained, by Prisma. But, it was shelved for priority reasons, and The Guild subsequently took over its maintenance.

The team at The Guild has rebuilt the 2.0 version from the ground-up, and v2.0 is fully customizable, user-friendly, and supports all platforms.

Try GraphQL Yoga 2.0 yourself!

GraphQL Clients for Java, Go and others

GraphQL clients for other languages like Java, and Golang include:

Java GraphQL Client - DGS Framework is a framework for Java and Kotlin.
Ent framework from Facebook for Golang
Fezzik's Fully generated Golang Graphql Client by Inigo Labs
genqlient: a truly type-safe Go GraphQL client by Khan Academy.

Key Update on Relay

The Relay team has been working on the Client Control Nullability proposal for more than three months, and has also been working to bring Client Side State into Relay.

Are there specifics on how Relay can work with React Native?

Relay is separated into three different packages:

The compiler, which scans the entire project and compiles your queries.
The Relay runtime, which is the actual store and cache.
The React Relay, which ties everything together.

There is also binding for Apollo, which is maintained by the community, and a full port of Relay to Swift, so it is possible to use it with other clients.

Relay's core is fully usable, and can even be used on the server.

GraphQL CodeGen

TypedDocumentNode is one of GraphQL Codegen's newly highlighted features. This allows users to take GraphQL strings, and receive typed results with CodeGen using TypeScript.

You can also write fragments inside of your components, and get the typed results of that query inside of the same components. This reduces the number of types imported from a single file, which decreases bundle size through treeshaking.

Finally, even with a flag, you can enable fragment masking, which ensures that you are just using the data that is inside of the TypeScript component.

Conclusion

This was an incredible conversation with a great group of guests, and I would highly suggest you watch the video to hear more about best practices and code federation.

You can watch the full GraphQL Contributor Day event on This Dot Media's YouTube Channel.

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)

Hassan Sani
@inidaname @inidaname

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

Class and Enum Typings for Handling Data with GraphQL

Intro Today we’ll talk about class and enum typings for handling more complex data with GraphQL. The typings will be used on class objects to make them easier to read and understand. This blog will build on the concepts of another blog found here, in which I discussed wrapping and enhancing data, which talks about how to create a GraphQL Rest API wrapper, and how to enhance your data. This article assumes you have a basic understanding of classes, typing, and enums. Here is the code repo if you want to review it while reading, or just skip ahead. With that said, we will look at the class structure we’re going to be using, the enums, and the type lookup for our customField enum with our mapper. Class setup This class we’re going to set up will be strictly for typing. We’re going to make a RestaurantGuest class as this data will be restaurant themed. So in our restaurant.ts file, we will name it RestaurantGuest, and include a few different items. *Basic starting class example* ` After setting that up, we will add a type that will reference the above class. *Type example* ` This type will be used later when we do the type lookup in conjunction with our mapper function. With the above finished, we can now move on to handling our enums. Handling enums We’ll now create our Enum to make dealing with our complex data easier. Since the above 3249258 represents FoodReservation, we will create an enum for it. Now you might be wondering why 3249258 represents FoodReservation. Unfortunately, this is an example of how data can be returned to us from a call. It could be due to the field id established in a CMS such as Contentful, or from another source that we don’t control. This isn’t readable, so we’re creating an Enum for the value. *Enum example* ` This will be used later during our type look-up. Type lookup Now we can start combining the enum from above with our class type, and a look-up type that we’re about to create. So let's make another type called RestaurantGuestFieldLookup, which will have an id and value. *Look-up type example* ` Perfect, now we’ll swap out ` to be ` We can now move on to creating and using our mapper. In a separate file called mapper.ts, we will create our restaruantGuestMapper function. ` Tada! Thanks to all the work above, we can easily understand and get back the data we need. Conclusion Today's article covered setting up a typing class, creating an enum, and type lookup with a mapper for more complex data handling with GraphQL. Hopefully, the class structure was straightforward and helpful, along with the enums and type lookup and formatting examples. If you want to learn more about GraphQL, read up on our resources available at graphql.framework.dev....

May 26, 2023

2 mins

GraphQL

Using a Starter Kit on Starter.dev to Kickstart Your React and Angular Projects

As a developer, deciding on the stack to use and the dependencies can be a difficult choice. Once you start, configuration, installation, and making sure you have the correct versions are also chores. Starting with a prebuilt framework for some of these tasks can boost a developer's productivity and save time by eliminating repetitions. In this article, I will walk you through how you can cut 40 hours of setup and boost productivity by focusing on what really matters. We will learn a simple solution for implementing project functionality without worrying about setting up different versions of dependencies and configurations, or repeating the same process for different projects. Introducing Starter Kits starter.dev has a curated list of starter kits for various frameworks and libraries to help boost developer productivity. Why starter kits? Our architects discovered they were repeatedly going through the same process every time they needed to start a new project. This process included initializing, installing dependencies, setting up tests, and everything else that comes with bootstrapping a new project. How to use a Starter Kit Let’s walk through starting apps with Angular, and React with starter kits using the Starter Kits Showcases. Every starter kit is structured with the basic requirements to initialize the projects with key features: - Routing - Forms - State Management - API interactions - REST & GraphQL - Authentication - Test Runners These features help developers understand how to architect and build large scale applications. The default project directories are scaffolded with added directories, configurations, and a demo app to help quickly build or modify the components. Using the Angular Apollo TailwindCSS Starter Kit Every starter kit is easy to initialize so you can start building features for your project. The Angular starter kit sets you up with these tools to quickly get you started: - Angular 13: for build frontend components - Apollo Client: for managing both local and remote data with GraphQL - TailwindCSS: for styling components and elements - Storybook: Component library - Jest: Test runner Initializing the project: To initialize a project with the starter kits, run the following: 1. Run npx @this-dot/create-starter to run the scaffolding tool 2. Select Angular, Apollo, and TailwindCSS kits from the CLI library options 3. Name your project 4. cd into your project directory, install dependencies using the tool of your choice, and voila! You can start building features in your new project immediately. Configurations The project pre-configuration is the recommended Angular GraphQL configuration. The ApolloModule and ApolloClientOptions are imported for the App NgModule. ` ` Project Implementation. Starter kits aim to make your project set up fast, easy and reusable. The initial demo project is a counter example connected to a GraphQL module with a fetch example and routes implemented - all following industry’s best practices. Go ahead to add more components or modify existing components without the need to make any changes to the configuration. Extras The preconfigured Storybook for component management gives developers the opportunity to create organized UI systems making both the building process and documentation more efficient and easier to use. React RxJs and Styled Components The React starter kit tools scaffold to quickly get you started with a React project, and include: - React 17: for building frontend components - RxJs: for managing data - Styled Components: for styling components and elements - React Router: For matching project routes - Storybook: Component library - Jest: Test runner Initializing the project: To initialize a project with the starter kits, run the following: - Run npx @this-dot/create-starter to run the scaffolding tool - Select Create React App, RxJS and Styled Components kits from the CLI library options - Name your project - cd into your project directory, install dependencies using the tool of your choice, and voila! You can start building features in your new project immediately. Configurations The project configuration is the same with an initial create-react-app config boilerplate, but with pre-configurations for Storybook and style component. Conclusion In this article, you were able to boost productivity by cutting the time of setting up and moving straight to implementing components, allowing you to focus on working on what matters. I hope this article was helpful, let us know what other starter kits you will love to see on starter.dev....

Dec 12, 2022

3 mins

AngularReact

D1 SQLite: Writing queries with the D1 Client API

Writing queries with the D1 Client API In the previous post we defined our database schema, got up and running with migrations, and loaded some seed data into our database. In this post we will be working with our new database and seed data. If you want to participate, make sure to follow the steps in the first post. We’ve been taking a minimal approach so far by using only wrangler and sql scripts for our workflow. The D1 Client API has a small surface area. Thanks to the power of SQL, we will have everything we need to construct all types of queries. Before we start writing our queries, let's touch on some important concepts. Prepared statements and parameter binding This is the first section of the docs and it highlights two different ways to write our SQL statements using the client API: prepared and static statements. Best practice is to use prepared statements because they are more performant and prevent SQL injection attacks. So we will write our queries using prepared statements. We need to use parameter binding to build our queries with prepared statements. This is pretty straightforward and there are two variations. By default we add ? ’s to our statement to represent a value to be filled in. The bind method will bind the parameters to each question mark by their index. The first ? is tied to the first parameter in bind, 2nd, etc. I would stick with this most of the time to avoid any confusion. ` I like this second method less as it feels like something I can imagine messing up very innocently. You can add a number directly after a question mark to indicate which number parameter it should be bound to. 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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Federation, Roadmaps, GraphiQL & more: GraphQL Contributor Day | May 3, 2022

New GraphQL Updates

GraphiQL Updates

Dataloader Updates

How does DataLoader differ from Apollo's DataSource concept?

GraphQL Conf 2022

GraphQL Server Side Updates

GraphQL Yoga

GraphQL Clients for Java, Go and others

Key Update on Relay

Are there specifics on how Relay can work with React Native?

GraphQL CodeGen

Conclusion

Hassan Sani

You might also like

Leveraging GraphQL Scalars to Enhance Your Schema

Class and Enum Typings for Handling Data with GraphQL

Using a Starter Kit on Starter.dev to Kickstart Your React and Angular Projects

D1 SQLite: Writing queries with the D1 Client API

Let's innovate together!

You might also like

Leveraging GraphQL Scalars to Enhance Your Schema

Class and Enum Typings for Handling Data with GraphQL

Using a Starter Kit on Starter.dev to Kickstart Your React and Angular Projects

D1 SQLite: Writing queries with the D1 Client API