Study on Spark performance tuning strategy based on Skewed Partitioning and Locality-aware Partitioning
Abstract
Apache Spark is a large-scale data processing engine, widely used in a variety of big data analysis tasks. However, data skew and data locality issues can cause performance degradation in Spark applications. This paper investigates Spark performance tuning strategies based on Skewed Partitioning and Locality-aware Partitioning. Firstly, the infl uence of data skew and data localizability problems in Spark is analyzed, and then a perfo rmance tuning method combining Skewed Partitioning and Locality-aware Partitioning is proposed. Experimental results show that this method can significantly improve the efficiency of Spark jobs when dealing with large data sets, compared with traditional HashPartitioner.
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