Performance Analysis of Efficient Sorting Algorithms in Big Data Processing

In big data processing, the efficiency of sorting operations is crucial to system performance. After all, it is difficult for mainstream sorting algorithms to achieve such high efficiency when processing massive data. This study examines several efficient sorting algorithms in big data environments to speed up processing. First, this article selects rapid sort, merge sort and Tim sort for analysis, decomposing Hadoop implementation into the use of data sharding technology to achieve the parallel processing capability of the algorithm, thereby reducing the computing load of a single node. Multithreading is further used to accelerate data processing, and the execution time and resource consumption of different data sizes are compared in the experiment. The results show that when processing 100 million items, Tim sort has the highest sorting efficiency, taking 4.1 seconds. Choosing appropriate sorting algorithms and optimization strategies can greatly improve the efficiency of big data processing and provide better practical applications.