Strategies for Database Performance Optimization in High-Load Systems: A Review of PostgreSQL and Redis Use Cases
Keywords:
databases, high load, system performance, query processing, data storage, distributed solutions, scalingAbstract
The article presents an expanded analysis of performance-optimization strategies for database systems operating under high load, using PostgreSQL and Redis as representative examples. The study is based on comparing architectural principles of storage systems, query-planning models, mechanisms for handling concurrent operations, and the characteristics of in-memory data processing. It examines differences in DBMS behavior as workload profiles change—from read-dominant scenarios to intensive write patterns and mixed workloads. Particular attention is given to how PostgreSQL’s internal mechanisms, including cardinality estimation, plan selection, and concurrency management, respond to data-volume growth and increasing query complexity. A substantial part of the study focuses on the influence of infrastructural decisions—containerization, automatic scaling, load balancing, distributed queues, and fault-tolerance mechanisms—on the real performance of DBMSs under load. The analysis demonstrates that the stability of data-processing pipelines depends on the coherence between database-level optimizations and cloud-environment parameters, enabling the reduction of latency, improved resilience to failures, and effective operation during peak demand. The practical contribution of the work lies in identifying strategies that allow engineers to design databases and supporting infrastructure as a unified optimization system. The article will be useful for database administrators, developers of high-performance services, distributed-systems architects, cloud-platform engineers, and researchers studying data-processing mechanisms in highly loaded computational environments.
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Copyright (c) 2025 Vadym Shevchenko
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