Efficient Data Processing: Assessing the Performance of Different Programming Languages

Abstract

This paper compares the performance of R, Python, and Rust in the context of data processing tasks. A real-world data processing task in the form of an aggregation of benchmark measurement results was implemented in each language, and their execution times were measured. The results indicate that while all languages can perform the tasks effectively, there are significant differences in performance. Even the same code showed significant runtime differences depending on the interpreter used for execution. Rust and Python were the most efficient, with R requiring much longer execution times. Additionally, the paper discusses the potential implications of these findings for data scientists and developers when choosing a language for data processing projects.