GraphQL vs. REST: Performance and Scalability Analysis for Serverless Applications

Abstract

This thesis presents a comprehensive performance, scalability, and cost comparison of GraphQL and Representational State Transfer (REST) APIs within the context of serverless computing. While REST is the conventional choice for API implementation, its architectural style which is designed for network-based applications, specifically client-server applications, can lead to inefficiencies, such as over-fetching and under-fetching, leading to potential performance and price penalties in pay-per-use serverless environments. This work investigates GraphQL as a flexible and efficient interface alternative for two distinct and representative serverless application use cases: a CPU-bound image processing pipeline and a data-intensive relational database application.For the CPU-bound pipeline, experimental results demonstrate that GraphQL reduces client-perceived Round Trip Time (RTT) by eliminating network latency associated with multiple client-to-server round trips required to orchestrate the workflow with REST. For the data-intensive workload, GraphQL implementations show content-dependent performance compared to REST, with Apollo demonstrating 25-67\% performance improvements over REST on most operations, but worse scalability than REST under very high workloads. Collectively, these findings illustrate that GraphQL provides advantages for serverless applications. The nature of these advantages is context-dependent, from orchestrating tasks in multi-step CPU-bound workflows to data-fetching from a relational database, establishing GraphQL as a compelling architectural alternative for modern cloud-native applications.