GraphQL

Efficiently Extract Object References in Shopify Storefront GraphQL API

William Mimura

Published Mar 29, 2024

Updated Apr 1, 2024

4 min read

Efficiently Extract Object References in Shopify Storefront GraphQL API

Introduction

So, this blog post is born out of necessity and a bit of frustration. If you're diving into the world of Shopify's Storefront API, you've probably realized that while it's powerful, extracting data in the object reference from Metadata fields or Metaobjects (in the GraphQL query) can be a bit like searching for a needle in a haystack. This complexity often arises not from the API's lack of capabilities but from the sparse and sometimes unclear documentation on this specific aspect.

That's precisely why I decided to create this post. As a developer, I found myself in a situation where the documentation and community resources were either scarce or not detailed enough for the specific challenges I faced. This guide is the result of my journey - from confusion to clarity.

The Situation

To understand the crux of my challenge, it's essential to recognize that creating metafields and metaobjects is a common practice for those seeking a more customized and controlled experience with Shopify CMS. In my specific case, I wanted to enrich the information available for each product's vendor beyond what Shopify typically allows, which is just a single text box. I aimed to have each vendor display their name and two versions of their logo: a themed logo that aligns with my website's color scheme and an original logo for use on specific pages.

The challenge emerged when I fetched a list of all vendors to display on a page. My GraphQL query for the Storefront API looked like this:

query vendorsMetaObjects($country: CountryCode, $language: LanguageCode)
@inContext(country: $country, language: $language) {
    vendorsCollection: metaobjects(type: "vendor", first: ${MAX_PAGE_BY}) {
        nodes {
            name: field(key: "name") {
                value
            }
            originalLogo: field(key: "original_logo") {
                value
            }
            themedLogo: field(key: "themed_logo") {
                value
            }
        }
    }
}

This was when I hit a roadblock. How do I fetch a field with a more complex type than a simple text or number, like an image? To retrieve the correct data, what specific details must I include in the originalLogo and themedLogo fields?

In my quest for a solution, I turned to every resource I could think of. I combed through the Storefront API documentation, searched endlessly on Stack Overflow, and browsed various tech forums. Despite all these efforts, I couldn’t find the clear, detailed answers I needed. It felt like I was looking for something that should be there but wasn’t.

Solution

Before diving into the solution, it's important to note that this is the method I discovered through trial and error. There might be other approaches, but I want to share the process that worked for me without clear documentation.

My first step was to understand the nature of the data returned by the Storefront API. I inspected the value of a metaobject, which looked something like this:

{
  "name": { "value": "A Vendor Test 1" },
  "originalLogo": { "value": "gid://shopify/MediaImage/some_ID" },
  "themedLogo": { "value": "gid://shopify/MediaImage/some_other_ID" }
}

The key here was the gid, or global unique identifier. What stood out was that it always includes the object type, in this case, MediaImage. This was crucial because it indicated which union to use and what properties to query from this object in the Storefront API documentation.

So, I modified my query to include a reference to this object type, focusing on the originalLogo field as an example:

query vendorsMetaObjects($country: CountryCode, $language: LanguageCode)
@inContext(country: $country, language: $language) {
    vendorsCollection: metaobjects(type: "vendor", first: ${MAX_PAGE_BY}) {
        nodes {
            # ...
            originalLogo: field(key: "original_logo") {
                value
                reference {
                    ... on MediaImage {
                        # Explore MediaImage documentation for extractable fields
                    }
                }
            }
            # ...
        }
    }
}

The next step was to consult the Storefront API documentation for MediaImage at Shopify API Documentation. Here, I discovered the image field within MediaImage, an object containing the url field. With this information, I updated my query:

query vendorsMetaObjects($country: CountryCode, $language: LanguageCode)
@inContext(country: $country, language: $language) {
    vendorsCollection: metaobjects(type: "vendor", first: ${MAX_PAGE_BY}) {
        nodes {
            name: field(key: "name") {
                value
            }
            originalLogo: field(key: "original_logo") {
                reference {
                    ... on MediaImage {
                        image {
                            url
                        }
                    }
                }
            }
            themedLogo: field(key: "themed_logo") {
                reference {
                    ... on MediaImage {
                        image {
                            url
                        }
                    }
                }
            }
        }
    }
}

Finally, when executing this query, the output for a single object was as follows:

{
  "name": { "value": "A Vendor Test 1" },
  "originalLogo": {
    "reference": {
      "image": {
        "url": "https://cdn.shopify.com/s/files/rest_of_the_url"
      }
    }
  },
  "themedLogo": {
    "reference": {
      "image": {
        "url": "https://cdn.shopify.com/s/files/rest_of_the_url"
      }
    }
  }
}

Through this process, I successfully extracted the necessary data from the object references in the metafields, specifically handling more complex data types like images.

Conclusion

In wrapping up, it's vital to emphasize that while this guide focused on extracting MediaImage data from Shopify's Storefront API, the methodology I've outlined is broadly applicable. The key is understanding the structure of the gid (global unique identifier) and using it to identify the correct object types within your GraphQL queries.

Whether you're dealing with images or any other data type defined in Shopify's Storefront API, this approach can be your compass. Dive into the API documentation, identify the object types relevant to your needs, and adapt your queries accordingly. It's a versatile strategy that can be tailored to suit many requirements.

Remember, the world of APIs and e-commerce is constantly evolving, and staying adaptable and resourceful is crucial. This journey has been a testament to the power of perseverance and creative problem-solving in the face of technical challenges. May your ventures into Shopify's Storefront API be equally rewarding and insightful.

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About the author(s)

William Mimura
@mimurawil

Build Beautiful Storefronts Quickly with Shopify and Next

Introduction Shopify is one of the world’s best e-commerce platforms for building online stores of any size. It’s a hosted platform, which means you don’t have to worry about the technical details of managing a server or dealing with software updates. You can focus on building your business instead. Next.js is a React framework for building static and server-side rendered applications. It’s a great choice for building e-commerce storefronts and enables you to do more customization, and it’s what we’ll be using in this article. Shopify Next.js Starter Kit Recently, we’ve created a starter kit that you can use to build your own Shopify storefront with Next.js. It’s a great way to get started quickly especially since it is powered by the new App Router and React Server Components, and it’s completely free. You can find it on GitHub here: Shopify Next.js Starter Kit We also have a live demo of the starter kit here: Shopify Next.js Starter Kit Demo Getting Started To get started, open your terminal and run the following command: And choose Shopify, NextJS 13.4 and Tailwind CSS Then, choose the project name And everything ready to go, next steps are to go to the directory and install the packages Setup Shopify Account Next, you’ll need to create a Shopify store. There are two ways to get a Shopify account: 1- You can do this by going to Shopify and clicking on the “Start free trial” button and create a Shopify account. 2- Or you can create a Shopify Partner Account for development purposes Once you’ve created your store, you’ll need to create a private app. You can do this by going to the “Apps” section of your Shopify admin and clicking on the “Manage private apps” link. Then click on the “Create a new private app” button. Give your app a name, and then click on the “Save” button. You’ll then be taken to a page where you can see your API credentials. You’ll need to copy these credentials into your .env.local file. You can find the .env.local file at the root of your project. It should look something like this: Modify the design After adding the required secrets, run npm run dev to run the development server locally The project structure is simple and easy. Since we are using the App Router, all of the routes are under /app folder and the shared components are under /components folder. This structure make it easy for you to edit and modify easily on the design Also all of the components have been written in a clean way with Tailwind CSS to make it easy for you to edit it. Deploy Since it’s a Next.js project, its deployment is easier than ever, most of the modern host providers support deploying it with just one click like * Vercel * Netlify * Cloudflare Page * AWS Amplify * Render * Fly.io Just push the project to a remote git repository using GitHub and connect it to the hosting provider of your choice. Conclusion In this article, we’ve shown you how to build a Shopify storefront with Next.js with our new starter kit from Starter.dev. We’ve also shown you how to use the new App Router and React Server Components to build a fast and performant storefront. We hope you’ve enjoyed this article and found it useful. If you have any questions or comments, please feel free to reach out to us on Twitter or GitHub. We’d love to hear from you!...

Nov 1, 2023

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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

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Our Journey from Cypress to Playwright for E2E Testing

Our Journey from Cypress to Playwright for E2E Testing Introduction Adapting to unexpected technical challenges often leads to innovation. Our team faced such a moment when we discovered that our primary end-to-end (E2E) testing framework, Cypress, was incompatible with an essential component of our tech stack. This discovery led us to consider alternatives to resolve the compatibility issue and enhance our testing processes. Our choice was Playwright, a modern testing framework acclaimed for its robust features and compatibility with multiple browsers. The shift to Playwright was swift and efficient: all our E2E tests were successfully migrated within a few hours. This rapid transition highlighted our team's ability to adapt and integrate new technologies quickly. In this blog post, we will share the hands-on process we followed during the migration to Playwright, the challenges we encountered along the way, and the solutions we implemented to overcome them. Using a Test Conversion Tool We utilized a third-party test conversion tool to facilitate our migration from Cypress to Playwright, which significantly streamlined the process. Among several options available, we chose Ray's Cypress to Playwright Conversion Tool for its ease of use and effectiveness. This tool allowed us to automatically transpose our existing Cypress tests into the Playwright format. The tool processes each test script by identifying equivalent functions and commands in Playwright, translating our entire suite with minimal input. Below are some before and after images of our test scripts, demonstrating the tool's conversion capabilities. One test script written in Cypress: ` And its result converted in Playwright: ` While the tool performed impressively, converting most of our tests accurately had limitations. Certain custom helper functions and test aliases did not translate directly, and the tool clearly marked these for manual intervention. This feature was beneficial as it allowed us to quickly identify and address the segments that required our attention. Small Fixes and Fine-Tuning The initial output from the conversion tool was highly useful, yet it required some tweaks to align with Playwright's capabilities perfectly. The tool conveniently marked each unconverted function with the identifier FIXME_, making it straightforward to address these specific areas. Here’s how we tackled some of these functions: FIXME_visitWithTimeout: Originally a custom Cypress command designed to visit a URL with a specified timeout (cy.visit(url, { timeout: 30000 })), this was straightforwardly converted to Playwright's page.goto(url) FIXME_getDataCy: Another custom command (cy.get('[data-cy=""]')) was efficiently translated to Playwright’s page.locator('[data-testid=""]') FIXME_scrollTo: This native Cypress command, which scrolls the window to a specific position, proved unnecessary in Playwright. Given Playwright’s automatic detection of elements on the page, we opted to remove this command entirely ` Further refinements were made to the converted code, such as removing some "wait/delay" commands. Playwright’s robust handling of asynchronous events allowed us to streamline our tests by eliminating unnecessary pauses enhancing test execution speed and reliability. Conclusion The transition from Cypress to Playwright in our testing framework was driven by necessity but has significantly improved our testing practices. Utilizing a test conversion tool allowed us to migrate our entire suite of E2E tests efficiently, minimizing manual effort and accelerating the adoption of Playwright. Our experience highlights the importance of flexibility and the willingness to embrace new technologies in the face of unexpected challenges. The marked improvements in test execution speed and reliability, along with Playwright's advanced features, have made this transition a resounding success for our team. As we continue to refine our tests and explore the full capabilities of Playwright, we encourage other teams facing similar challenges to consider this approach. The tools and processes outlined here can serve as a roadmap for those looking to enhance their automated testing solutions....

May 31, 2024

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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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Efficiently Extract Object References in Shopify Storefront GraphQL API

Efficiently Extract Object References in Shopify Storefront GraphQL API

Introduction

The Situation

Solution

Conclusion

William Mimura

You might also like

Build Beautiful Storefronts Quickly with Shopify and Next

How to Leverage Apollo Client Fetch Policies Like the Pros

Our Journey from Cypress to Playwright for E2E Testing

Introduction to Zod for Data Validation

Let's innovate together!

You might also like

Build Beautiful Storefronts Quickly with Shopify and Next

How to Leverage Apollo Client Fetch Policies Like the Pros

Our Journey from Cypress to Playwright for E2E Testing

Introduction to Zod for Data Validation