GraphQL

GraphQL Contributor Days -- November 1st, 2019

Oct 28, 2019

2 min read

GraphQL Contributor Days is back and in-person! We’re excited to host a special GraphQL Contributor Days after GraphQL Summit on November 1st, 2019 from 10am - 3pm PST in San Francisco, CA.

In partnership with Hasura, this event welcomes any and all interested in GraphQL to join the conversation!

The purpose of the event is for the GraphQL community to come together and discuss some key topics in the GraphQL ecosystem, and facilitate cross collaboration within the community.

Our featured guests are: Tracy Lee - Founder, This Dot & RxJS Core Team Rajoshi Ghosh - Co-Founder, Hasura Tanmai Gopal - Founder, Hasura Ivan Goncharov - GraphQL consultant, APIs.guru Dan Schafer - Software Engineer, Facebook Sasha Solomon - Sr. Software Engineer, Twitter Eve Porcello - Co-Owner, Moon Highway Alex Banks - Instructor, Moon Highway Tim Griesser - Senior Software Engineer, Cypress Shruti Kapoor - Software Engineer, Paypal Jim Gust - Sr. Technical Product Manager, Expedia Dan Boerner - Distinguished Program Manager, Expedia Group Patrick Arminio - Senior Software Engineer, Verve Zhifan Li - Senior Software Engineer, Intuit Christian Nwamba - Senior Cloud Developer Advocate, Microsoft

& more to be announced!

If you’re interested in attending, sign up for an invitation here.

Have a topic you’d like us to talk about? Leave us a comment below, and let us know!

Don’t forget to follow us on Twitter for updates on all the action @ThisDotMedia!

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.

How to Resolve Nested Queries in Apollo Server

When working with relational data, there will be times when you will need to access information within nested queries. But how would this work within the context of Apollo Server? In this article, we will take a look at a few code examples that explore different solutions on how to resolve nested queries in Apollo Server. I have included all code examples in CodeSandbox if you are interested in trying them out on your own. Prerequisites This article assumes that you have a basic knowledge of GraphQL terminology. Table of Contents - How to resolve nested queries: An approach using resolvers and the filter method - A refactored approach using Data Loaders and Data Sources - What are Data Loaders - How to setup a Data Source - Setting up our schemas and resolvers - Resolving nested queries when microservices are involved - Conclusion How to resolve nested queries: An approach using resolvers and the filter method In this first example, we are going to be working with two data structures called musicBrands and musicAccessories. musicBrands is a collection of entities consisting of id and name. musicAccessories is a collection of entities consisting of the product name, price, id and an associated brandId. You can think of the brandId as a foreign key that connects the two database tables. We also need to set up the schemas for the brands and accessories. ` The next step is to set up a resolver for our Query to return all of the music accessories. ` When we run the following query and start the server, we will see this JSON output: ` ` As you can see, we are getting back the value of null for the brands field. This is because we haven't set up that relationship yet in the resolvers. Inside our resolver, we are going to create another query for the MusicAccessories and have the value for the brands key be a filtered array of results for each brand. ` When we run the query, this will be the final result: ` ` This single query makes it easy to access the data we need on the client side as compared to the REST API approach. If this were a REST API, then we would be dealing with multiple API calls and a Promise.all which could get a little messy. You can find the entire code in this CodeSandbox example. A refactored approach using Data Loaders and Data Sources Even though our first approach does solve the issue of resolving nested queries, we still have an issue fetching the same data repeatedly. Let’s look at this example query: ` If we take a look at the results, we are making additional queries for the brand each time we request the information. This leads to the N+1 problem in our current implementation. We can solve this issue by using Data Loaders and Data Sources. What are Data Loaders Data Loaders are used to batch and cache fetch requests. This allows us to fetch the same data and work with cached results, and reduce the number of API calls we have to make. To learn more about Data Loaders in GraphQL, please read this helpful article. How to setup a Data Source In this example, we will be using the following packages: - apollo-datasource - apollo-server-caching - dataloader We first need to create a BrandAccessoryDataSource class which will simulate the fetching of our data. ` We will then set up a constructor with a custom Dataloader. ` Right below our constructor, we will set up the context and cache. ` We then want to set up the error handling and cache keys for both the accessories and brands. To learn more about how caching works with GraphQL, please read through this article. ` Next, we are going to set up an asynchronous function called get which takes in an id. The goal of this function is to first check if there is anything in the cached results and if so return those cached results. Otherwise, we will set that data to the cache and return it. We will set the ttl(Time to Live in cache) value to 15 seconds. ` Below the get function, we will create another asynchronous function called getByBrand which takes in a brand. This function will have a similar setup to the get function but will filter out the data by brand. ` Setting up our schemas and resolvers The last part of this refactored example includes modifying the resolvers. We first need to add an accessory key to our Query schema. ` Inside the resolver, we will add the accessories key with a value for the function that returns the data source we created earlier. ` We also need to refactor our Brand resolver to include the data source we set up earlier. ` Lastly, we need to modify our ApolloServer object to include the BrandAccessoryDataSource. ` Here is the entire CodeSandbox example. When the server starts up, click on the Query your server button and run the following query: ` Resolving nested queries when microservices are involved Microservices is a type of architecture that will split up your software into smaller independent services. All of these smaller services can interact with a single API data layer. In this case, this data layer would be GraphQL. The client will interact directly with this data layer, and will consume API data from a single entry point. You would similarly resolve your nested queries as before because, at the end of the day, there are just functions. But now, this single API layer will reduce the number of requests made by the client because only the data layer will be called. This simplifies the data fetching experience on the client side. Conclusion In this article, we looked at a few code examples that explored different solutions on how to resolve nested queries in Apollo Server. The first approach involved creating custom resolvers and then using the filter method to filter out music accessories by brand. We then refactored that example to use a custom DataLoader and Data Source to fix the "N+1 problem". Lastly, we briefly touched on how to approach this solution if microservices were involved. If you want to get started with Apollo Server and build your own nested queries and resolvers using these patterns, check out our serverless-apollo-contentful starter kit!...

May 17, 2023

6 mins

GraphQL

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

Improving INP in React and Next.js

Improving INP in React and Next.js In one of my previous articles, I've explained what INP is, how it works, and how it may affect your website. I also promised you to follow up with more concrete advice on how to improve your INP in your favorite framework. This is the follow-up article, where I'll focus on how to improve your INP score in React and Next.js. How to prepare for INP in React and Next.js? The first thing to do is to ensure you're using the latest version of React. The React team has been working on making React more INP-friendly and has already made some improvements in the latest versions. To enhance your INP score, consider fully taking advantage of new features introduced in React 18, such as Concurrent Rendering, Automatic Batching, and Selective Hydration. However, there are also some general areas to focus on, such as SSR and SSG in Next.js, Web Workers, or optimizing your hooks and state management. Concurrent Rendering The Concurrent Mode in React uses an algorithm that breaks rendering down into so-called "fiber nodes" and schedules the renders based on their expiration and priority. This effectively allows the user to interact with the page while the rendering is still in progress. In previous React versions, all updates, such as setState calls were treated as "urgent" and once the re-render had started, there was no way to interrupt it. Concurrent Mode changes this by being able to prioritize the updates and interrupt a non-blocking state update started with startTransition. For a simple explanation of concurrency in React, you can check out Dan Abramov's explanation. As part of the Concurrent Mode, React introduced several lifecycle methods that allow you to prioritize the rendering of certain parts of your UI, such as: - useTransition hook that allows you to update the state without blocking the UI, - useDeferredValue hook that allows you to defer the rendering of certain parts of your UI, - startTransition API that, similarly to the useTransition hook lets you mark a state update as non-blocking. It lacks, however, an indication of whether it's still pending. Automatic Batching Introduced in React 18, Automatic Batching reduces the number of re-renders that happen on state changes even when they happen outside of event handlers, e.g. in a setTimeout or Promise callback. This feature comes out of the box and you don't have to do anything to enable it, and it makes a great argument for upgrading to React 18. Selective Hydration Selective Hydration allows you to take hydration off the main thread by wrapping your components in a Suspense boundary. This way, components can become interactive faster as the browser can do other work on the main thread while the hydration is happening. To fully take advantage of selective hydration, consider the following: - Prioritizing Above-the-Fold Content: Use Suspense boundaries strategically around any parts of your application that may take the server longer to deliver to ensure they don’t block critical content from becoming interactive as soon as possible. - Hydration on Interaction: Implementing hydration upon user interaction for non-critical components can drastically reduce the main thread's workload, enhancing INP. Vercel even has a small case study showing how using selective hydration improved the performance of a Next.js site. Server-Side Rendering (SSR) and Static Site Generation (SSG) in Next.js Not everything has to run client-side. Next.js excels in SSR and SSG capabilities, which can significantly impact INP by delivering content to users faster. Optimizing SSR with techniques like incremental static regeneration (ISR) or leveraging SSG for static pages ensures that users can interact with content faster, improving the perceived performance. Workers Offloading heavy computations to Web Workers can free up the main thread, enhancing the responsiveness of React and Next.js applications. This strategy is especially useful when dealing with third-party scripts. Offloading such scripts in Next.js can be easily done by specifying the "worker" strategy on your Script component. Be aware that this feature is not yet stable and does not work with the app directory, though. If you want to take things one step further, you could use Partytown, which helps you offload any resource-intensive scripts to Web Workers. It comes with a React component that you can use to wrap your third-party scripts and offload them to a Web Worker, and it's compatible with Next.js as well. Hooks and State Management State management in React applications can easily get out of hand, leading to unnecessary re-renders and effectively an increased INP. Sometimes, using a state management library like Redux or MobX can help you consolidate your state and reduce the number of re-renders. However, they are not silver bullets and can also introduce performance issues if not used properly. If you are dealing with a lot of re-renders due to prop changes, make sure you are leveraging memoization. As of now, you may need to work with useMemo and useCallback hooks to memoize your values and functions, respectively. The upcoming React 19’s Forget Compiler, however, will apparently memoize everything under the hood, making these hooks obsolete. Using memoization properly can help you reduce the number of re-renders and improve your INP. To investigate your hook dependencies and re-renders, you can leverage React Developer Tools or use this handy helper hook I found on the internet to trace your re-renders: ` Conclusion Improving INP in React and Next.js is not easy and can require much investigation and fine-tuning. Still, it's worth doing to avoid being penalized by Google in its search results and provide a better experience for your users. Adopting React 18's new features, leveraging SSR and SSG in Next.js, utilizing Web Workers, and optimizing hooks and state management can significantly boost your INP score and deliver a faster application to your users. Remember, INP is just one among many performance metrics emphasizing the need for a comprehensive approach to performance optimization...

Apr 19, 2024

5 mins

Web PerformanceReactNextJSJavaScript

Transforming Auth in an AI World with Rod Boothby

In today's digital landscape, the need for secure identity verification has become paramount. With the increasing risks of personal data exposure and the rise of sophisticated cyber threats aided by Artificial Intelligence, it is crucial to adopt robust verification processes to protect individuals and organizations alike. In a recent discussion with Rod Boothby, CEO of ID Partner Systems, the significance of trusted institutions for identity verification was emphasized, particularly the efficiency of bank-based ID verification over traditional methods. One of the key takeaways from the conversation was the importance of continuous authentication. As technology advances, so do the methods employed by cybercriminals. Deepfake technology, for instance, poses a significant threat to identity verification systems. To combat this, tighter security measures and continuous authentication are essential. By constantly verifying and validating user identities, organizations can stay one step ahead of potential fraudsters. Rod Boothby also highlighted the need for a developer-focused approach to identity verification. By providing developers with the tools and resources they need, companies like ID Partner Systems aim to streamline the verification process and enhance security. This approach not only ensures a more efficient experience for users but also allows for the integration of behavioral biometrics, which can further strengthen the verification process. Download this episode here....

Apr 22, 2024

1 min

Engineering Leadership

GraphQL Contributor Days -- November 1st, 2019

You might also like

How to Resolve Nested Queries in Apollo Server

How to create and use custom GraphQL Scalars

Improving INP in React and Next.js

Transforming Auth in an AI World with Rod Boothby

You might also like

How to Resolve Nested Queries in Apollo Server

How to create and use custom GraphQL Scalars

Improving INP in React and Next.js

Transforming Auth in an AI World with Rod Boothby