PostgreSQL Performance in Serverless Event Driven Architectures: An AWS-Based Case Study

PostgreSQL has established itself as a powerful and feature-rich open-source relational database management system, increasingly adopted in modern cloud-native applications. As organizations transition to serverless computing for scalability and operational efficiency, integrating traditional stateful systems like PostgreSQL within stateless, event-driven workflows poses unique performance and architectural challenges. This research investigates the performance characteristics of PostgreSQL in such environments by leveraging key AWS services, including AWS Lambda and Amazon S3, to orchestrate serverless data pipelines. The study simulates a range of real-world scenarios, including high-throughput bulk data inserts, concurrent data access, and complex analytical queries, to assess PostgreSQL’s behavior under load in an event-driven setup. Performance metrics such as query latency, throughput, system scalability, and cost-effectiveness are examined in depth. Furthermore, we present a case study modeled on production-scale workloads to highlight common bottlenecks, the impact of cold starts, database connection limits, and strategies to mitigate these issues through architectural and configuration optimizations. The findings contribute actionable insights for developers and architects designing scalable and efficient serverless systems with relational databases.