A COMPARATIVE EXPERIMENTAL STUDY OF INDEX PERFORMANCE IN MONGODB AND POSTGRESQL

Abstract

The continuous growth in data volumes, combined with the increasing complexity of modern enterprise information systems, requires database management systems (DBMS) to guarantee ever faster response times. In this context, query performance optimization has become a major challenge for improving operational efficiency and organizational productivity. Among the most widely used DBMS, PostgreSQL and MongoDB stand out for their ability to manage large amounts of complex data while offering advanced performance optimization mechanisms. These systems notably integrate indexing techniques that significantly accelerate query processing, even in environments characterized by very large data volumes. In the context of this study, we analyze the impact of different indexing techniques applied to queries executed on these two reference DBMS. More specifically, we evaluate the performance of three types of indexes: single index, composite index, and text index. The indexing strategies were applied to different data manipulation operations, including retrieval, update, and deletion. The results obtained highlight the effectiveness of indexing techniques in optimizing query performance and confirm their essential role in the efficient management of large databases within modern information systems.