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GraphQL Batching for N+1 Problem

2 posts · Apr 2018 – Mar 2026

What this trail covers

Shopify has developed and enhanced its GraphQL execution engine by introducing GraphQL Cardinal, a breadth-first execution engine. This new engine resolves each field once across all objects, rather than once per object, significantly improving performance for high-cardinality list queries. This addresses the hidden costs of depth-first traversal, such as linear scale amplification and field-level overhead, leading to substantial reductions in execution time and memory usage. The migration of a

All Shopify trails
  1. Shopify’s journey to faster breadth-first GraphQL execution (2026) - ShopifyThis post details the development of GraphQL Cardinal, a new breadth-first execution engine for Shopify's GraphQL data layer. It addresses the performance bottlenecks of conventional depth-first traversal in high-cardinality list queries by executing each field once across all objects, rather than once per object. The engine was prototyped and tested, showing up to 15x faster CPU-bound execution and 90% less memory usage compared to depth-first execution, leading to significant end-to-end response time improvements.
  2. Unifying Our GraphQL Design Patterns and Best Practices with Tutorials - ShopifyThis post details the early journey of adopting GraphQL at Shopify, highlighting the challenges of being early adopters without established best practices or community discussions. It describes the informal documentation efforts, the creation of the API Patterns Team to address horizontal concerns, and the development of an in-depth design tutorial to unify GraphQL design patterns and best practices across the organization. The core lesson emphasized is designing APIs to reflect the business domain rather than application implementation.
  3. Solving the N+1 Problem for GraphQL through Batching - ShopifyThis post details the inspiration for and creation of the GraphQL Batch Ruby library, a solution to the N+1 problem in GraphQL. It explains how the library batches requests by returning promises for data, allowing the server to group similar loads and resolve them together, significantly reducing datastore queries. The library is open-sourced to benefit the wider GraphQL community.