OPTIMIZATION OF ACCESS TO STATIC DATA IN DISTRIBUTED SYSTEMS: A KUBERNETES-BASED SOLUTION WITH POSTGRESQL AND DJANGO

Static data is a crucial component in distributed systems, ensuring the seamless operation of various components. However, achieving reliable and high-frequency access to such data poses challenges due to its heterogeneous structure. This paper introduces a Kubernetesorchestrated Static Data Database that integrates advanced technologies and best practices to address these challenges effectively. The proposed system leverages the Django framework and PostgreSQL database, optimized with advanced features such as JSONB indexing and metadata flexibility. These technologies are not only widely adopted in the industry for their proven scalability and efficiency but also incorporate cutting-edge academic innovations, such as JSONB-based metadata management and modular database schemas, to enhance flexibility in diverse use cases. Data payloads are stored in a POSIXcompliant distributed file system to ensure robustness. The service is containerized and deployed using Helm on the Kubernetes platform, with OKD serving as the deployment environment to achieve scalability and operational efficiency. This deployment model reflects industrial standards for cloud-native applications while demonstrating the practical applicability of academic research on container orchestration and resource optimization. Through extensive testing, the system demonstrated significant scalability, reliability, and performance improvements under high-demand scenarios. The testing process was designed to mirror real-world industrial workloads, such as high-frequency data access and concurrent queries while validating academic hypotheses on system behavior under extreme conditions. The results validate its potential to meet the growing needs of modern distributed systems, offering a scalable and future-ready solution firmly grounded in academic research and industrial practice.