Paper Title
ANALYZING ELASTICSEARCH PERFORMANCE ACROSS VARIED LOG SIZES: A COMPREHENSIVE STUDY ON INDEXING TIMES

Abstract
Abstract - This research paper thoroughly analyzes Elasticsearch performance across all log sizes (100 to 500 MB), emphasizing data indexing efficiency. In an era dominated by vast digital data, efficient log analysis is very critical for actionable insights. By scrutinizing Elasticsearch's indexing times and key parameters, this study reveals factors influencing its efficiency in handling diverse log sizes. Furthermore, the paper explores the evolution of search technologies, highlighting Elasticsearch's pivotal role in modern data indexing. As digital ecosystems expand, the scalability challenge in log analysis becomes very apparent making Elasticsearch crucial. Beyond empirical analysis, the paper provides insights into industry implications, guiding optimization efforts and also offering software developers nuanced performance insights. It concludes by identifying future research directions, contributing to our understanding of Elasticsearch's role in log analysis amid evolving digital landscapes. Keywords - Elasticsearch, log analysis, indexing efficiency, digital data, search technologies, scalability, empirical analysis, industry implications, optimization, software development, performance insights, future research directions, data indexing, digital landscapes.