Research on Load Balancing Strategy Based on MapReduce
MapReduce is an important component in the Hadoop cluster framework,used for parallel operations on large-scale datasets.This paper proposes a two-stage hash partitioning strategy based on sampling to address the load balancing issue in MapReduce,using two-layer sampling technology for data sampling.In the first stage of partitioning,the Hash algorithm is used to initial partition the samples,and the size of each partition is compared with the threshold to determine whether it is an abnormal partition.In the second stage of partitioning,the idea of offset partitioning and fine-grained partitioning is integrated,and the abnormal partitioning is subjected to a second hash partitioning operation.The experimental results show that this strategy effectively solves the load balancing problem in MapReduce,reduces the performance loss caused by data imbalance,and improves resource utilization.
万方数据
维普
