Research on Optimization of Stream Data Processing Algorithm for Multi-Core Processor Based on Mapreduce

With the rapid development of information technology and the popularization of the Internet, streaming data processing has become one of the core tasks of big data analysis. With their parallel computing capabilities, multi-core processors show great potential in efficiently processing massive streams of data. However, the traditional streaming data processing algorithm often faces the problems of uneven resource allocation, unbalanced load and high communication overhead when facing multi-core architecture, which limits the overall performance of the system. Based on MapReduce programming model, this paper discusses the optimization strategy of streaming data processing algorithm in multi-core processor environment. Firstly, this paper analyzes the limitations of the existing MapReduce framework in the stream data processing scenario, including the delay increase caused by batch processing tendency and the lack of adaptability to dynamic data flow. Then, an improved Partitioning Strategy is proposed, which can dynamically adjust task allocation according to data flow characteristics, ensure load balancing among cores, and minimize cross-core communication costs. In order to further improve the processing efficiency, this paper also designs a kernel processor stream data processing algorithm based on MapReduce-partitioning strategy, which can dynamically adjust the task priority and optimize the execution order according to real-time performance monitoring information. The experimental results show that the proposed optimization method not only effectively improves the speed of upstream data processing, but also enhances the stability and reliability of the system, which provides theoretical support and technical reference for future large-scale streaming data analysis.