When data skew occurs, the execution efficiency of the program will be reduced, especially in the reduce stage of Spark. Therefore, this paper proposes ReducePartition to solve data skew problem at reduce stage of Spark platform. First, the compute node samples the local data according to the ...
In the parallel computing framework of Hadoop/Spark, data skew is a common problem resulting in performance degradation, such as prolonging of the entire execution time and idle resources. What lies behind this issue is partition imbalance, which causes significant differences in the amount of data...
Data skewMapReduce programming modelDistributed file systemsHadoop frameworkApache Pig LatinFor over a decade, MapReduce has become the leading programming model for parallel and massive processing of large volumes of data. This has been driven by the development of many frameworks such...
Data skewMapReduce programming modelDistributed file systemsHadoop frameworkApache Pig LatinFor over a decade, MapReduce has become the leading programming model for parallel and massive processing of large volumes of data. This has been driven by the development of many frameworks such as Spark, ...
effective in systems with high-throughput, low-latency task schedulers and efficient data materialization, so we propose techniques for scaling these components.To demonstrate the efficacy of this technique, we com- pare micro-tasks to other skew handling techniques using the Spark cluster computing ...