GraphQL vs. REST: Investigating Performance and Scalability for Serverless Data Persistence

Serverless computing simplifies application deployment by removing the need for infrastructure management, with REST APIs being the common interface. Data persistence is essential for serverless applications because serverless functions are stateless and short-lived. To retain information across function executions, relational databases are commonly used to persist data, to ensure continuity, scalability, and reliable management. However, REST can lead to inefficiencies such as data over-fetching and under-fetching, which impact performance. GraphQL is a data query and manipulation language that supports client queries that specify what data is to be retrieved or modified. GraphQL resolvers fetch and transform data as specified by client queries.This paper compares the performance and scalability of GraphQL APIs as a database interface middleware in contrast to REST APIs implemented using serverless functions. We compare a GraphQL API implemented using the managed GraphQL AWS AppSync service, an unmanaged GraphQL API hosted using Apollo Server, and a traditional REST API implemented via the Amazon API Gateway and AWS Lambda. We compare these alternatives using clients implemented with serverless AWS Lambda functions, and also a local machine, an Amazon virtual machine (VM), and a Google VM. Our data APIs accessed a managed Amazon Aurora PostgreSQL cluster populated with the U.S. Centers for Medicare & Medicaid Services (CMS) Open Payments dataset. Our GraphQL implementations show content-dependent performance compared to REST, with Apollo Server demonstrating 25-67% faster average round-trip times vs. REST for most operations, but worse scalability than REST with very high concurrent workloads. Our findings provide practical guidance for developers selecting serverless architectures for GraphQL and REST APIs based on specific application requirements, network conditions, and expected request volumes.