GraphQL

Ship Less JavaScript with GraphQL Resolvers

Published Jan 17, 2023

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This article was written over 18 months ago and may contain information that is out of date.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

In the past year or two, I’ve noticed a lot of folks online putting an emphasis on the size and amount of code we are shipping as JavaScript bundles in our web applications. And this makes sense. A lean JavaScript bundle is advantageous for many reasons like accessibility, mobile device/network experience, SEO, and overall speed of your web pages and applications.

There are many ways that we can reduce the size of the JavaScript bundles that we ship without sacrificing using the tools and frameworks that we’re familiar with to build high-quality websites and applications. In this article, I want to highlight some ways that we can use GraphQL and tools like Apollo Server to reduce the amount of JavaScript that gets shipped in our bundles.

This article assumes that you have some experience and familiarity with GraphQL. If you need an introduction to these concepts, check out this fantastic post from our blog: Migrating from REST to GraphQL. Otherwise, let's dig right into some different strategies and examples we can use to speed up our websites! For the sake of our examples going forward, we are going to pretend that we are working on an IMDB-like website for movies.

Defining schemas to get the most out of your API

One of the key benefits of GraphQL is the ability to define a schema that specifies the shape of the data that is available through the API. Defining a well-crafted GraphQL schema provides the ability to ship only the data that is actually needed by the client. In a REST API, it is common to fetch entire entities from a database, even if only a small portion of that data is actually needed by the client. With GraphQL, you have granular control over the data that is returned, allowing you to ship only the data that is required.

Here’s a simple example schema to describe our movie API. It includes a query to fetch a list of films, and another to fetch a list of actors. as well as the shape of those entities, that we can request some or all of the fields from, using our GraphQL API.

type Query {
  films(limit: Int): [Film]
  people(limit: Int): [Person]
}

type Film {
  title: String
  releaseDate: String
  actors: [Person]
}

type Person {
  name: String
  films: [Film]
}

Sub-query resolvers

One particularly powerful feature of GraphQL is the ability to define sub-query resolvers. This allows you to specify relationships between different types of data in your schema, allowing the client to request related data in a single query. This can greatly reduce the number of API calls needed to fetch all of the data required by the client, ultimately leading to the shipment of less JavaScript.

Based on our schema above, we can easily write a query to request a list of actors that played in a particular film.

query {
  films(limit: 5) {
    title
    actors {
      name
    }
  }
}

Our Apollo Server sub-query resolver might look something like this:

const resolvers = {
  Film: {
    actors: (film, _, { dataSources }) => dataSources.peopleAPI.getActorsByFilm(film.id),
  },
}

A quality GraphQL schema will provide more granular control to its clients, and will help prevent over-fetching of data. This is an easy way to reduce bytes sent across the wire, which will lead to improved performance in our applications.

Moving client logic ito resolvers

Resolvers are just functions that we write to fetch and aggregate the data that we defined in our schema. A simple concept that provides a powerful layer between our data sources and the contract we have defined with our GraphQL API. They are called by the GraphQL engine and are passed the arguments defined in our queries and a reference to the calling object.

Let's update our films query to accept some additional arguments:

type Query {
  films(limit: Int, title: String, year: Int, offset: Int): [Film]
}

Using the arguments defined on our films query, our client now has more control to only fetch the data they need. The limit and offset arguments that allow them to paginate the results so a single page will need to fetch and render a smaller amount of items at a time.

Here’s what our resolver might look like using these new arguments to fetch a subset of the data that is available:

const resolvers = {
  Query: {
    films: async (_, { limit, title, year, offset }, { dataSources }) => {
        return dataSources.filmAPI.getFilms({ limit, title, year, offset });
    },
  },
}

Magic Fields

Another way to use resolvers is to create "magic fields" that are single-purpose data transformations. For example, we could create a field on the Person type called latestFilm that returns the actor’s most recent film, rather than returning a list of all of the user's posts and having the client filter through them. This can save on both the amount of data shipped, and the amount of processing required by the client.

type Person {
  name: String
  films: [Film]
	latestFilm: Film
}

Transformational Resolvers

Transformational resolvers allow you to perform transformations on the data before it is returned to the client, reducing the need for complex data manipulation logic on the client side.

For example, in your film type, you can use an enum to allow the client to request the release date in different formats, such as SHORT or LONG.

enum DateFormat {
  SHORT
  LONG
}

Then, you can use this format in your film query as a variable and pass it to the releaseDate field:

query Film($id: ID!, $format: DateFormat) {
  film(id: $id) {
    id
    releaseData(format: $format)
  }
}

On the server side, you can define a resolver for the releaseData field that takes the format variable as an argument, and performs the appropriate transformation before returning the data to the client.

const resolvers = {
  Film: {
    releaseData: (film, { format }, { dataSources }) => {
        if (format === DateFormat.SHORT) {
            // Perform short format transformation
            return shortFormatDate(film.releaseDate);
        } else if (format === DateFormat.LONG) {
            // Perform long format transformation
            return longFormatDate(film.releaseDate);
        }
    },
  },
}

This way, the client can request the release date in the format they need, and the server can handle the transformation before returning the data. This reduces the amount of code needed on the client side to manipulate the data and improves the performance of the application.

It's worth noting that in some cases, it may make sense to keep some of the data manipulation logic on the client side. The goal is to find the right balance between server and client-side code, and to make sure that the code you ship to the client only contains the logic that is necessary for the user experience.

Wrapping REST APIs to leverage GraphQL features

In some cases, you may not have the ability to create a new GraphQL API from scratch. Fortunately, it is possible to wrap existing REST APIs with GraphQL using tools such as Apollo Server. This allows you to take advantage of the benefits of GraphQL, even if you don't have control over the underlying API(s).

In our movie application, instead of having our own database of films and actors we might have used the IMDB API instead. Apollo Server specifically provides us with abstractions around the data sources that we use to resolve our data from.

Conclusion

In conclusion, GraphQL is a powerful tool that can be used to ship less JavaScript to the client, resulting in improved performance for your web application. By defining well-crafted schemas, and utilizing resolvers effectively, you can reduce the amount of data shipped over the network and the amount of processing required by the client. If you are working with existing REST APIs already, you can use tools like Apollo Server to wrap them and take advantage of the benefits of GraphQL.

Whether you are building a new application or optimizing an existing one, consider using GraphQL to ship less JavaScript, and improve the performance of your app. If you want to learn more about GraphQL, check out graphql.framework.dev, our open-source library filled with curated GraphQL resources!

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)

Dane Grant
JavaScript developer building things on web, native, and server environments. Free time filled with reading, cooking, dog, sports, comedy, and music.
@danecando @danecando

How to Leverage Apollo Client Fetch Policies Like the Pros

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

May 17, 2024

7 mins

GraphQL

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

Introducing the new Serverless, GraphQL, Apollo Server, and Contentful Starter kit The team at This Dot Labs has released a brand new starter kit which includes the Serverless Framework, GraphQL, Apollo Server and Contentful configured and ready to go. This article will walk through how to set up the new kit, the key technologies used, and reasons why you would consider using this kit. Table of Contents - How to get started setting up the kit - Generate the project - Setup Contentful access - Setting up Docker - Starting the local server - How to Create the Technology Model in Contentful - How to seed the database with demo data - How to work with the migration scripts - Technologies included in this starter kit - Why use GraphQL? - Why use Contentful? - Why use Amazon Simple Queue Service (SQS)? - Why use Apollo Server? - Why use the Serverless Framework? - Why use Redis? - Why use the Jest testing framework? - Project structure - How to deploy your application - What can this starter kit be used for? - Conclusion How to get started setting up the kit Generate the project In the command line, you will need to start the starter.dev CLI by running the npx @this-dot/create-starter command. You can then select the Serverless Framework, Apollo Server, and Contentful CMS kit and name your new project. Then you will need to cd into your new project directory and install the dependencies using the tool of your choice (npm, yarn, or pnpm). Next, you will need to Run cp .env.example .env to copy the contents of the .env.example file into the .env file. Setup Contentful access You will first need to create an account on Contentful, if you don't have one already. Once you are logged in, you will need to create a new space. From there, go to Settings -> API keys and click on the Content Management Tokens tab. Next, click on the Generate personal token button and give your token a name. Copy your new Personal Access Token, and add it to the CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN variable. Then, go to Settings -> General settings to get the CONTENTFUL_SPACE_ID. The last step is to add those CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN and CONTENTFUL_SPACE_ID values to your .env file. Setting up Docker You will first need to install Docker Desktop if you don't have it installed already. Once installed, you can start up the Docker container with the npm run infrastructure:up command. Starting the local server While the Docker container is running, open up a new tab in the terminal and run npm run dev to start the development server. Open your browser to http://localhost:3000/dev/graphql to open up Apollo server. How to Create the Technology Model in Contentful To get started with the example model, you will first need to create the model in Contentful. 1. Log into your Contentful account 2. Click on the Content Model tab 3. Click on the Design your Content Modal button if this is your first modal 4. Create a new model called Technology 5. Add three new text fields called displayName, description and url 6. Save your new model How to seed the database with demo data This starter kit comes with a seeding script that pre-populates data for the Technology Content type. In the command line, run npm run db:seed which will add three new data entries into Contentful. If you want to see the results from seeding the database, you can execute a small GraphQL query using Apollo server. First, make sure Docker, and the local server(npm run dev) are running, and then navigate to http://localhost:3000/dev/graphql. Add the following query: ` When you run the query, you should see the following output. ` How to work with the migration scripts Migrations are a way to make changes to your content models and entries. This starter kit comes with a couple of migration scripts that you can study and run to make changes to the demo Technology model. These migration scripts are located in the scripts/migration directory. To get started, you will need to first install the contentful-cli. ` You can then login to Contentful using the contentful-cli. ` You will then need to choose the Contentful space where the Technology model is located. ` If you want to modify the existing demo content type, you can run the second migration script from the starter kit. ` If you want to build out more content models using the CLI, you can study the example code in the /scripts/migrations/01-create-technology-contentType.js file. From there, you can create a new migration file, and run the above contentful space migration command. If you want to learn more about migration in Contentful, then please check out the documentation. Technologies included in this starter kit Why use GraphQL? GraphQL is a query language for your API and it makes it easy to query all of the data you need in a single request. This starter kit uses GraphQL to query the data from our Contentful space. Why use Contentful? Contentful is a headless CMS that makes it easy to create and manage structured data. We have integrated Contentful into this starter kit to make it easy for you to create new entries in the database. Why use Amazon Simple Queue Service (SQS)? Amazon Simple Queue Service (SQS) is a queuing service that allows you to decouple your components and process and store messages in a scalable way. In this starter kit, an SQS message is sent by the APIGatewayProxyHandler using the sendMessage function, which is then stored in a queue called DemoJobQueue. The SQS handler sqs-handler polls this queue, and processes any message received. ` Why use Apollo Server? Apollo Server is a production-ready GraphQL server that works with any GraphQL client, and data source. When you run npm run dev and open the browser to http://localhost:3000/dev/graphql, you will be able to start querying your Contentful data in no time. Why use the Serverless Framework? The Serverless Framework is used to help auto-scale your application by using AWS Lambda functions. In the starter kit, you will find a serverless.yml file, which acts as a configuration for the CLI and allows you to deploy your code to your chosen provider. This starter kit also includes the following plugins: - serverless-offline - allows us to deploy our application locally to speed up development cycles. - serverless-plugin-typescript - allows the use of TypeScript with zero-config. - serverless-dotenv-plugin - preloads function environment variables into the Serverless Framework. Why use Redis? Redis is an open-source in-memory data store that stores data in the server memory. This starter kit uses Redis to cache the data to reduce the API response times and rate limiting. When you make a new request, those new requests will be retrieved from the Redis cache. Why use the Jest testing framework? Jest is a popular testing framework that works well for creating unit tests. You can see some example test files under the src/schema/technology directory. You can use the npm run test command to run all of the tests. Project structure Inside the src directory, you will find the following structure: ` This given structure makes it easy to find all of the code and tests related to that specific component. This structure also follows the single responsibility principle which means that each file has a single purpose. How to deploy your application The Serverless Framework needs access to your cloud provider account so that it can create and manage resources on your behalf. You can follow the guide to get started. Steps to get started: 1. Sign up for an AWS account 2. Create an IAM User and Access Key 3. Export your AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY credentials. ` 4. Deploy your application on AWS Lambda: ` 5. To deploy a single function, run: ` To stop your Serverless application, run: ` For more information on Serverless deployment, check out this article. What can this starter kit be used for? This starter kit is very versatile, and can be used with a front-end application for a variety of situations. Here are some examples: - personal developer blog - small e-commerce application Conclusion In this article, we looked at how we can get started using the Serverless, GraphQL, Apollo Server, and Contentful Starter kit. We also looked at the different technologies used in the kit, and why they were chosen. Lastly, we looked at how to deploy our application using AWS. I hope you enjoy working with our new starter kit!...

Jun 2, 2023

7 mins

GraphQL

Remix's evolution to a Vite plugin

The Remix / React Router news is a beautiful illustration of software evolution. You start with a clear idea of what you’re building, but it’s impossible to anticipate exactly what it will become. If you haven’t heard the news yet, Ryan Florence announced at React Conf 2024 that Remix will be a Vite plugin for React Router. It’s a sort of surprising announcement but it makes a lot of sense if you’ve been following along as Remix the project has evolved. In this post we’ll recap the evolution of the project and then dig into what this change means for the future of Remix in the Server Components era of React. 🗓️ October 2021 > Remix is open sourced In the beginning, Remix cost money. In October 2021 they received some seed funding and the project was open sourced. Remix was an exciting new framework for server-rendering React websites that included API’s for loading data on the server and submitting data to the server with forms. It started a trend in the front-end framework space that a lot of other popular frameworks have modeled after or taken inspiration from. 🗓️ March 2022 - September 2022 > Remixing React Router At some point they realized that the API’s that they had created for Remix were a more natural fit in React Router. In this post Ryan details the plans to move these Remix API’s into React Router. Several months later, React Router 6.4 is released with the action, loader, and other Remix API’s baked in. > "We've always thought of Remix as "just a compiler and server for React Router" After this release, Remix is now mostly as they described - a compiler and server for React Router. 🗓️ October 2023 - February 2024 > Remix gets Vite support If you’re not familiar with Vite, it’s an extremely popular tool for front-end development that handles things like development servers and code bundling/compiling. A lot of the modern front-end frameworks are built on top of it. Remix announced support for Vite as an alternative option to their existing compiler and server. Since Remix is now mostly “just a compiler and server for React Router” - what’s left of Remix now that Vite can handle those things? 🗓️ May 2024 > Remix is React Router The Remix team releases a post detailing the announcement that Ryan made at React Conf. Looking back through the evolution of the project this doesn’t seem like an out of the blue, crazy announcement. Merging Remix API’s into React Router and then replacing the compiler and server with Vite didn’t leave much for Remix to do as a standalone project. 🐐 React Router - the past, present, and future of React React Router is one of the oldest, and most used packages in the React ecosystem. > Since Remix has always been effectively "React Router: The Framework", we wanted to create a bridge for all these React Router projects to be able to upgrade to Remix. With Remix being baked into React Router and now supporting SPA mode, legacy React Router applications have an easier path for migrating to the next generation of React. https://x.com/ryanflorence/status/1791488550162370709 Remix vs React 19 If you’ve taken a look at React 19 at all, it turns out it now handles a lot of the problems that Remix set out to solve in the first place. Things like Server Components, Server Actions, and Form Actions provide the same sort of programming model that Remix popularized. https://x.com/ryanflorence/status/1791484663883829258 Planned Changes for Remix and React Router Just recently, a post was published giving the exact details of what the changes to Remix and React Router will look like. tl;dr For React Router * React Router v6 to v7 will be a non-breaking upgrade * The Vite plugin from Remix is coming to React Router in v7 * The Vite plugin simply makes existing React Router features more convenient to use, but it isn't required to use React Router v7 * v7 will support both React 18 and React 19 For Remix * What would have been Remix v3 is React Router v7 * Remix v2 to React Router v7 will be a non-breaking upgrade * Remix is coming back better than ever in a future release with an incremental adoption strategy enabled by these changes For Both * React Router v7 comes with new features not in Remix or React Router today: RSC, server actions, static pre-rendering, and enhanced Type Safety across the board The show goes on I love how this project has evolved and I tip my hat to the Remix/React Router team. React Router feels like a cozy blanket in an ecosystem that is going through some major changes which feels uncertain and a little scary at times. The Remix / React Router story is still being written and I’m excited to see where it goes. I’m looking forward to seeing the work they are doing to support React Server Components. A lot of people are pretty excited about RSC’s and there is a lot of room for unique and interesting implementations. Long live React Router 🙌....

Jul 10, 2024

4 mins

ReactViteRemix

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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Ship Less JavaScript with GraphQL Resolvers

Defining schemas to get the most out of your API

Sub-query resolvers

Moving client logic ito resolvers

Magic Fields

Transformational Resolvers

Wrapping REST APIs to leverage GraphQL features

Conclusion

Dane Grant

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