GraphQL Mutations for Data Manipulation

5 posts · Feb 2019 – Aug 2021

What this trail covers

This feature thread tracks the development and implementation of GraphQL mutations for creating, updating, and deleting data within Shopify's systems. Initial efforts focused on understanding GraphQL queries for data fetching. This post introduces the concept of mutations as the mechanism for modifying data, detailing the process of generating mutation resolvers, defining input arguments, and handling the `resolve` method to interact with the database. It covers specific examples for creating, u

All Shopify trails

Aug 2021Understanding GraphQL for Beginners–Part Three - ShopifyThis post details the creation of GraphQL mutations for modifying data in the database. It covers generating mutation queries using `rails g graphql:mutation`, defining input arguments, implementing the `resolve` method to interact with ActiveRecord objects, and testing these mutations in GraphiQL. Specific examples are provided for creating, updating, and deleting `Food` and `Nutrition` ActiveRecord objects.
Jul 2021Understanding GraphQL for Beginners–Part Two - ShopifyThis post details the creation of a GraphQL object (`food`) based on an existing Ruby on Rails model, the creation of a root field (`all_food`) to query all food items, and the addition of a nested field (`nutrition`) to retrieve nutrition facts associated with food. It also covers writing queries with arguments to retrieve specific food items by `id` and `place_of_origin`, and explains the behind-the-scenes execution flow of GraphQL queries.
Jun 2021Rate Limiting GraphQL APIs by Calculating Query Complexity - ShopifyThis post introduces a calculated query cost method for rate limiting Shopify's GraphQL Admin API. It details how query costs are defined for different GraphQL types (objects, scalars, enums, connections, interfaces, unions, mutations) based on the data requested and potential server load. It also explains how query cost information is returned in API responses and the difference between requested and actual query costs, with a focus on measuring the effectiveness of this model through the correlation between query complexity and execution time.
Jan 2021Using GraphQL for High-Performing Mobile Applications - ShopifyThis post details how GraphQL addresses pain points in mobile application development compared to REST APIs, specifically focusing on strong typing, self-documentation, and compile-time error detection. It explains how GraphQL schemas and introspection enable code generation (e.g., using Syrup) for strongly-typed Swift and Kotlin clients. The post also discusses the trade-offs of GraphQL's flexibility, the importance of considering query costs, and provides best practices for using fragments, customizing query responses, using aliases, and employing directives for conditional data fetching. It highlights how these features benefit mobile UX and maintainability.
Feb 2019Unifying Our GraphQL Design Patterns and Best Practices with Tutorials - ShopifyThis post details the unification of Shopify's GraphQL design patterns and best practices by creating an in-depth design tutorial. It addresses the scaling issues encountered due to a lack of formal documentation and guidelines as more developers contributed to GraphQL APIs. The creation of the API Patterns Team is highlighted as a key initiative to focus on horizontal concerns across the company. The tutorial emphasizes a business domain-centric API design philosophy and provides practical applications through a real example.
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