GraphQL

GraphQL Updates- October 2021

Published Oct 20, 2021

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This article was written over 18 months ago and may contain information that is out of date.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

State of GraphQL

State of GraphQL brings together core contributors, members of the GraphQL Foundation, and community leaders to talk about the future of GraphQL, and answer audience questions.

Hosts

Tracy Lee, CEO, This Dot Labs
Eve Porcello, Co-founder & Instructor, Moon Highway

Panelists

Uri Goldshtein, Founder, The Guild
Carlos Rufo, Engineering Lead, GraphCMS
Joey Nenni, Web Engineering Lead (GraphQL), PayPal
Janessa Garrow, Developer Experience Engineer, Apollo GraphQL
Tanmai Gopal, Founder, Hasura

GraphQL Updates

The first portion of the State of GraphQL covered updates from each panelist.

The GraphQL Foundation

One of the first topics covered by State of GraphQL was The GraphQL Foundation.

The GraphQL Foundation is a neutral foundation founded by global technology and application development companies. The GraphQL Foundation encourages contributions, stewardship, and a shared investment from a broad group in vendor-neutral events, documentation, tools, and support for GraphQL.

GraphQL.org

Uri opened up the conversation by emphasizing how open the foundation is. The GraphQL Foundation has always been open, but it's now easier than ever to become part of the community. The true "meat" of where the GraphQL action is though is in the GraphQL Working Group. There, topics such as changes to the GraphQL spec, ideas for new features, and more are discussed. Also, all the meetings are recorded and even summarized by text!

Apollo Odyssey

Odyssey is Apollo's free interactive learning platform.

Janessa from the Apollo team introduced us to Apollo Odyssey. Odyssey is Apollo's official learning platform. It was created to improve the developer experience of learning GraphQL. While some people may be able to learn effectively from docs, others may find different learning styles, like those offered by Odyssey, a more effective way of learning.

Courses offered range from the basics, like what are queries and resolvers, to the more advanced subjects, such as Apollo Federation.

Hasura

Tanmai from Hasura spoke next about updates done within Hasura. One of the biggest new features is support for cross-database joins over GraphQL. This update allows the developer to retrieve data from any number of data stores with efficient queries. The next thing being worked on now is end-to-end streaming.

PayPal

Joey Nenni of PayPal spoke about PayPal's experience with adopting GraphQL. He mentioned that once PayPal supported developers with tooling and sample apps, GraphQL spread like wildfire. Seeing the potential to do more with GraphQL, especially with sharing components, PayPal looked into Apollo Federation about 9 months ago, and as of about 2 weeks ago, they are now live with millions of requests already going through their gateway.

Joey also spoke about how GraphQL adoption is really about persistence. It's an iterative process. By making small steps, collecting small wins, and repeating that process, it becomes a lot easier to sell GraphQL. Joey coined the term "Guerilla Warfare" when it comes to finding successful implementations for GraphQL solutions.

GraphCMS

GraphCMS gives you instant GraphQL Content APIs to create, enrich, unify, and deliver your content across platforms.

GraphCMS recently hosted GraphQL Conf AND has recently raised $10 million in a Series A funding round. With all of this GraphQL focused momentum, GraphCMS is poising itself as a powerful solution for Headless CMS.

The ultimate content platform that enables you to deliver applications at scale.

Carlos Rufo

Defer and Stream

One important piece of information that Uri brought to the groups' attention was that defer, stream, live queries, and other features in spec proposals, but not in the actual spec, CAN BE USED SAFELY. Uri noticed a patter in production settings: once people have been given the option to use these new features, like defer, they see how valuable those features can be.

If you want to see more information involving these GraphQL directives, you can check out a blog post covering them here.

Apollo Federation

Implement a single graph across multiple services

Apollo Documentation

One very popular subject that is commonly brought up in Apollo conversations is Apollo Federation, and the idea of having a unified Graph connecting many services.

For PayPal, they spoke about the experiences encountered while transitioning some of their shared logic to a federated graph, while maintaining developer experience.

Apollo itself had a very positive experience using Apollo Federation. They ran into the need to unite two services, one written in Kotlin, and one in TypeScript, and have them run under a single gateway. This was a very big win and prevented the need for developers to learn additional languages to support a larger graph.

Tanmai had a balanced perspective on Apollo Federation:

Are we federating how the API comes together and presents itself? Or are we federating the execution itself?

He shared his experiences with analyzing where exactly a federated graph could be a good fit.

For Uri, he spoke about how the most important question is, "Are we actually making the product development faster?" Adopting Apollo Federation may give a different answer depending on the team and work being done.

Conclusion

GraphQL is still growing rapidly, and as shown in this State of GraphQL, there is a growing thriving community surrounding it. Continue the conversation, no matter your experience level, and check out the GraphQL Working Group. While this was a quick summary of the things spoken about during the State of GraphQL, you can catch the whole video on YouTube.

Thank you for enjoying the State of GraphQL!

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)

Matthew Pagan
@mastapegs @mastapegs

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

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

How to deploy a NodeJS application using Fly.io

I was astounded when I learned just how easy it is to deploy static web applications. Services like AWS Amplify, Netlify, GitHub Pages, etc, trivialize this process, allowing developers to focus less on deployment, and more on feature development. I was equally astounded when I learned that it can be *JUST AS EASY* to deploy a web service! There are many options out there: - Render - Heroku - Platform.sh - AWS Elastic Beanstalk This guide is going to be focused on a solution that I have found to be incredibly developer-friendly platform, Fly.io. What is Fly.io? > Deploy App Servers Close to Your Users > > Run your full-stack apps (and databases!) all over the world. No ops required. Fly.io claims to be able to run full-stack apps with no ops required. That's a bold statement. While I've committed to plenty of PR's, I haven't deployed a web service in a *while*. I was feeling the itch to deploy a service to play around with some different languages in the backend, so I was 100% in the market for a developer-friendly way to get this done, with quite literally no ops hopefully required. Fly.io App Deployment Speed-run The last time I personally deployed a brand new web service was months and months ago, on a DigitalOcean droplet. My usual go-to solution for web apps are static web apps, so I'm used to deploying static assets. Fly.io has a particular portion of their documentation which immediately drew my attention: their Speedrun section. Before deciding to write this blog, I figured, I'd at least try out their service, and if I enjoyed it, I'd write about it. Holy moly was I shocked just how *easy* Fly.io made deployment. After about 15 minutes from landing on their documentation, I was able to deploy a web service, complete with GitHub Actions CI/CD. And I didn't even need to touch a server! Here is how you can do the same. I created an incredibly terse ExpressJS app using TypeScript. 1. Install flyctl: The root URL for Fly.io has a one line command you can use to install flyctl, the command line tool which handles deployment for you. For detailed instructions though, check out this page. 2. Sign up / Log in: Sign up or Log in using the appropriate command, flyctl auth signup or flyctl auth login. 3. Run flyctl launch: This will prompt you to name your application, select a region to deploy to, and then deploy your app. This generates a fly.toml config file with the deployment configurations. In those three steps, you too can launch a sample express app. CI/CD Adding CI/CD is as simple as adding a single file to the repo, along with retrieving and adding an API token to the repo. Add the following to .github/workflows/main.yml ` Then run flyctl auth token to generate and print an API token, then add it to the repo as a new secret named FLY_API_TOKEN. That is all you need to continuously deploy your NodeJS application on every commit! Conclusion Using Fly.io honestly felt like cheating. I remember being super proud of myself when I first deployed a full LAMP stack application on a VPS. I also clearly remember the pain of troubleshooting things like SSH, CertBot and LetsEncrypt, Nginx, etc. Fly.io completely trivialized the entire process. I easily see myself using this going forward for all containerized applications since the service even includes the means for auto-scaling your apps and containers!!! If you are a developer and you are not shy of the command-line, I highly recommend checking out Fly.io and spending 15 minutes of your time, just deploying a service. You'll be shocked at just how quick and easy it can be to go from 0 to CI/CD in mere moments....

Dec 7, 2021

4 mins

NodeJS

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

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GraphQL Updates- October 2021

State of GraphQL

Sponsors

Hosts

Panelists

GraphQL Updates

The GraphQL Foundation

Apollo Odyssey

Hasura

PayPal

GraphCMS

Defer and Stream

Apollo Federation

Conclusion

Matthew Pagan

You might also like

How to create and use custom GraphQL Scalars

Class and Enum Typings for Handling Data with GraphQL

How to deploy a NodeJS application using Fly.io

Introduction to Zod for Data Validation

Let's innovate together!

You might also like

How to create and use custom GraphQL Scalars

Class and Enum Typings for Handling Data with GraphQL

How to deploy a NodeJS application using Fly.io

Introduction to Zod for Data Validation