Comparative Analysis of Asynchronous and Multithreaded Programming Methods in Python for Big Data Processing

This paper presents a comparative analysis of asynchronous and multithreaded programming methods in Python aimed at optimizing big data processing. The main concepts, architectural features, and practical implementation aspects using Python’s standard libraries (asyncio, threading) and additional tools (concurrent.futures) are examined. An experimental performance analysis on typical data processing tasks is provided, highlighting the advantages and drawbacks of each approach and offering recommendations for optimal application depending on the task type.

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Publication Details

Journal
Bulletin of Science and Practice
Published
2025-05-15
DOI
https://doi.org/10.33619/2414-2948/114/19
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Comparative Analysis of Asynchronous and Multithreaded Programming Methods in Python for Big Data Processing

A. Toktorbaev, Zh. Toktomuratova, S. Karabaev
Bulletin of Science and Practice
Computational Physics and Python Applications
article

Comparative Analysis of Asynchronous and Multithreaded Programming Methods in Python for Big Data Processing

A. Toktorbaev, Zh. Toktomuratova, S. Karabaev
article en

Abstract

This paper presents a comparative analysis of asynchronous and multithreaded programming methods in Python aimed at optimizing big data processing. The main concepts, architectural features, and practical implementation aspects using Python’s standard libraries (asyncio, threading) and additional tools (concurrent.futures) are examined. An experimental performance analysis on typical data processing tasks is provided, highlighting the advantages and drawbacks of each approach and offering recommendations for optimal application depending on the task type.

Bulletin of Science and PracticeVol. 11(5)
Osh State University (KG)
Openalex Percentile: Top 95%
Computational Physics and Python Applications
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