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.

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)

William Mimura
@mimurawil

Leveraging GraphQL Scalars to Enhance Your Schema

Introduction GraphQL has revolutionized the way developers approach application data and API layers, gaining well-deserved momentum in the tech world. Yet, for all its prowess, there's room for enhancement, especially when it comes to its scalar types. By default, GraphQL offers a limited set of these primitives — Int, Float, String, Boolean, and ID — that underpin every schema. While these types serve most use cases, there are scenarios where they fall short, leading developers to yearn for more specificity in their schemas. Enter graphql-scalars, a library designed to bridge this gap. By supplementing GraphQL with a richer set of scalar types, this tool allows for greater precision and flexibility in data representation. In this post, we'll unpack the potential of enhanced Scalars, delve into the extended capabilities provided by graphql-scalars, and demonstrate its transformative power using an existing starter project. Prepare to redefine the boundaries of what your GraphQL schema can achieve. Benefits of Using Scalars GraphQL hinges on the concept of "types." Scalars, being the foundational units of GraphQL's type system, play a pivotal role. While the default Scalars — Int, Float, String, Boolean, and ID — serve many use cases, there's an evident need for more specialized types in intricate web development scenarios. 1. Precision: Using default Scalars can sometimes lack specificity. Consider representing a date or time in your application with a String; this might lead to ambiguities in format interpretation and potential inconsistencies. 2. Validation: Specialized scalar types introduce inherent validation. Instead of using a String for an email or a URL, for example, distinct types ensure the data meets expected formats at the query level itself. 3. Expressiveness: Advanced Scalars provide clearer intentions. They eliminate ambiguity inherent in generic types, making the schema more transparent and self-explanatory. Acknowledging the limitations of the default Scalars, tools like graphql-scalars have emerged. By broadening the range of available data types, graphql-scalars allows developers to describe their data with greater precision and nuance. Demonstrating Scalars in Action with Our Starter Project To truly grasp the transformative power of enhanced Scalars, seeing them in action is pivotal. For this, we'll leverage a popular starter kit: the Serverless framework with Apollo and Contentful. This kit elegantly blends the efficiency of serverless functions with the power of Apollo's GraphQL and Contentful's content management capabilities. Setting Up the Starter: 1. Initialize the Project: ` 2. When prompted, name your project enhance-with-graphql-scalars. ` 3. For a detailed setup, including integrating with Contentful and deploying your serverless functions, please follow the comprehensive guide provided in the starter kit here. 4. And we add the graphql-scalars package ` Enhancing with graphql-scalars: Dive into the technology.typedefs.ts file, which is the beating heart of our GraphQL type definitions for the project. Initially, these are the definitions we encounter: ` Our enhancement strategy is straightforward: * Convert the url field from a String to the URL scalar type, bolstering field validation to adhere strictly to the URL format. Post-integration of graphql-scalars, and with our adjustments, the revised type definition emerges as: ` To cap it off, we integrate the URL type definition along with its resolvers (sourced from graphql-scalars) in the schema/index.ts file: ` This facelift doesn't just refine our GraphQL schema but infuses it with innate validation, acting as a beacon for consistent and accurate data. Testing in the GraphQL Sandbox Time to witness our changes in action within the GraphQL sandbox. Ensure your local server is humming along nicely. Kick off with verifying the list query: ` Output: ` Success! Each url in our dataset adheres to the pristine URL format. Any deviation would've slapped us with a format error. Now, let's court danger. Attempt to update the url field with a _wonky_ format: ` As anticipated, the API throws up a validation roadblock: ` For the final act, re-run the initial query to reassure ourselves that the original dataset remains untarnished. Conclusion Enhancing your GraphQL schemas with custom scalars not only amplifies the robustness of your data structures but also streamlines validation and transformation processes. By setting foundational standards at the schema level, we ensure error-free, consistent, and meaningful data exchanges right from the start. The graphql-scalars library offers an array of scalars that address common challenges developers face. Beyond the URL scalar we explored, consider diving into other commonly used scalars such as: * DateTime: Represents date and time in the ISO 8601 format. * Email: Validates strings as email addresses. * PositiveInt: Ensures integer values are positive. * NonNegativeFloat: Guarantees float values are non-negative. As a potential next step, consider crafting your own custom scalars tailored to your project's specific requirements. Building a custom scalar not only offers unparalleled flexibility but also provides deeper insights into GraphQL's inner workings and its extensibility. Remember, while GraphQL is inherently powerful, the granular enhancements like scalars truly elevate the data-fetching experience for developers and users alike. Always evaluate your project's needs and lean into enhancements that bring the most value. To richer and more intuitive GraphQL schemas!...

Nov 8, 2023

4 mins

GraphQL

Setting Up a Shopify App: Updating Customer Orders with Tracking Info

Today, we are wrapping up our adventure! Last time we learned about retrieving fulfillment IDs, but this time, we encounter the final boss: updating customer orders with tracking information. Now, if we have any new adventurers with us, I recommend heading over here to prep yourself for the encounter ahead. If you just need a recap of our last session, you can head over here. Alternatively, if you just want the code from last time that can be found here. If you want to skip ahead and look at the code it can be found here. With that all said we’re off to battle with updating customer orders with tracking information! Body We’re gonna start by heading over to our app/routes/app.index.jsx file, and grabbing the code found in the loader function. We’ll be moving that to our action function so we can add our post call. We’ll completely replace the existing action function code, and because of that, we need to make a couple of tweaks to the code base. We’re going to remove anything that has a reference to actionData?.product or productId. Now what we need to add the call admin.rest.resources.Fulfillment, which will allow us to update customer orders with tracking information. We’ll be placing it under our fulfillment ID loop. Here is a general example of what that call will look like. ` This is a good start as we now have our fulfillment information and get to add a few things to it. We’ll start off by adding our fulfillment ID and then our fulfillment tracking info. ` Awesome! Now we have given the fulfillment ID and tracking info we need to the fulfillment object, but we need to do one more thing for that to update. Thankfully, it’s a small thing and that’s to save it. ` Now, the above will work wonderfully for a single order, but based on our prior adventurers, we had multiple ids for orders that needed to be completed. So our next step is to loop over our fulfillment object. Though before we do that, here is what the current code should look like: ` Before we go to loop over this, we’re going to add a small change to fulfillmentIds. We’re going to create a new variable and add the company and tracking number information. So above the fulfillment variable, we will add this: ` Perfect! Now for the looping, we’ll just wrap it in a for of loop: ` Now that the loop is set up, we will be able to go through all of the orders and update them with the shipping company and a tracking number. So we’ll run a yarn dev, and navigate to the app page by pressing the p button. We should see our template page, and be able to click on the Generate a product button. Now we’ll navigate to our order page and we should see all open orders set to fulfilled. Conclusion Here we are. At the end of our three part saga, we covered a fair share of things in order to get our customer orders tracking information added, and can now take a long rest to rejuvenate....

Aug 28, 2023

3 mins

Shopify

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

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

In this episode of Leadership Exchange, host Rob Ocel chats with Jerry Reghunadh, Senior Director of Enterprise Architecture at Data Nimbus, about leadership, AI adoption, and data-driven decision-making. Jerry shares his career journey, insights on leveraging tools like ChatGPT and Copilot, and strategies for building effective data pipelines. They explore how companies can avoid "analysis paralysis," adopt AI strategically, and evaluate new technologies to solve real-world problems. Tune in for practical advice on aligning innovation with business goals and staying competitive in a rapidly evolving tech landscape. Chapters 00:00 – Introduction 00:31 – Jerry’s Leadership Journey 02:01 – Discussing AI in Leadership 03:59 – Experimenting with AI Tools 06:24 – Overcoming Analysis Paralysis 09:18 – Importance of Early AI Adoption 13:12 – AI’s Impact on Efficiency 14:30 – Sponsor Message 16:26 – Setting Realistic Goals for AI 19:41 – Data Management and AI 23:09 – Is AI Just a Fad? 27:22 – Testing New Technologies 29:56 – Final Thoughts on AI 30:25 – Connect with Jerry 30:44 – Closing Remarks 31:20 – Outro Follow Jerry on Social Media Linkedin: https://www.linkedin.com/in/jerrymannel/ Sponsored by This Dot: thisdot.co...

Nov 18, 2024

1 min

AIEngineering Leadership

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

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

Leveraging GraphQL Scalars to Enhance Your Schema

Setting Up a Shopify App: Updating Customer Orders with Tracking Info

How to create and use custom GraphQL Scalars

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

Let's innovate together!

You might also like

Leveraging GraphQL Scalars to Enhance Your Schema

Setting Up a Shopify App: Updating Customer Orders with Tracking Info

How to create and use custom GraphQL Scalars

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh