A weekly newsletter of the best news, articles and projects about GraphQL

The article discusses how Tailcall's system identifies N+1 issues in GraphQL by using a configuration-driven approach rather than handwritten resolvers. It explains the process of static analysis using a graph data structure and a Depth-First Search (DFS) algorithm to detect inefficient query patterns. Optimizations such as memoization and a custom "Chunk" data structure enhance performance, especially for large configurations. These strategies help ensure efficient and accurate identification of potential performance bottlenecks in GraphQL schemas.

Buffer is taking significant steps to prevent breaking changes in its GraphQL APIs, ensuring that their APIs remain stable and reliable for customers. By focusing on backward compatibility, careful schema design, and introducing strict validation tools, Buffer aims to avoid disruptions in their services. This stability is essential to maintain trust and provide a consistent experience for developers integrating with their platform, which ultimately benefits end-users who rely on Buffer's services for their social media management needs.

Trivago has enhanced its testing process for GraphQL services using the Karate framework, which is integrated with Docker and Justfiles. This setup allows for more reliable deployment through automated tests, parallel execution, and continuous integration pipelines. The approach helps ensure that changes don't break the production environment, especially in a microservices architecture, by rigorously testing across multiple environments before full deployment.

This report provides a thorough analysis of public GraphQL APIs, highlighting main vulnerabilities and the risks they pose. By understanding these security challenges, organizations can proactively protect their GraphQL APIs.