Analysis and optimization of database performance in distributed systemsс

Abstract

The paper analyzes and discloses the principles of database optimization in distributed systems. It emphasizes that with a rapidly growing digital user base, high-load applications – those that can process thousands to millions of requests per minute – require fine-tuning to meet growing demand. Various optimization techniques and strategies needed to improve database performance in distributed systems are also discussed. First, the importance of code optimization is analyzed, from writing efficient algorithms to using the right data structures, emphasizing its impact on reducing the computational complexity of applications that are part of distributed systems. It then discusses the relevance of database optimization and explores techniques such as indexing, caching, and query optimization that play a vital role in increasing database response time and therefore application performance. The main generations of query optimizers are discussed in detail: optimization directed by specific lifecycle phases (ODCP) and optimization directed by all phases (ODAP). The phases of the database life cycle and the optimization process are schematically presented, which allows you to visually follow the entire process. The issue of materialized and non-materialized representation is outlined, features are given. The process of overwriting the request in the optimization process has been created. It is noted that in the process of query optimization in parallel processing, the optimal sequential execution plan created at the end of the optimization phase is transformed into a parallel execution plan after the parallelization step