Scalable Data Partitioning and Shuffling Algorithms for Distributed Processing: A Review

Abstract

Scalable data splitting and shuffle algorithms have emerged as crucial elements of effective data processing in distributed computing and big data. This article provides an in-depth analysis of the complex terrain of these algorithms, which play a crucial role in ensuring efficient data distribution, load balancing, and resource optimisation in distributed systems. Among the most important discoveries are the varying functions performed by algorithms like hash-based, range-based, and sort-based techniques. The importance of measurements like data transmission overhead, processing time, and network utilisation in illustrating the impact of various algorithms on performance is emphasised. Challenges, such as algorithmic complexity and the never-ending search for efficiency and adaptation, remain despite their evident importance. The ramifications affect a wide variety of parties. Adaptive algorithms, privacy protection, and energy efficiency are all areas where researchers may make strides forward. Insights for optimised data processing operations, including careful algorithm selection and performance adjustment, might benefit practitioners. Leaders are urged to appreciate the algorithms' strategic value in realising data-driven goals and to invest wisely in the systems and personnel needed for effective distributed processing. As a result, organisations are able to extract meaningful insights, make informed real-time decisions, and navigate the ever-changing world of big data to scalable data division and shuffling algorithms.