PAW: Data Partitioning Meets Workload Variance

In distributed storage systems (e.g., HDFS, Amazon S3, Databricks), partitioning is applied on a dataset in order to enhance performance and availability. Recently, partitioning methods have been designed to optimize the query performance of partitions with respect to the historical query workload. Never-theless, in practice, future query workloads may deviate from the historical query workload, thus deteriorating the performance of existing partitioning methods. To fill this research gap, we model the variance of future query workloads from the historical query workload, then exploit this characteristic to produce partitions that perform well for future query workloads. In addition, we explore the space of irregular shaped partition regions to further optimize the query performance. Experimental results on TPC-H and real datasets show that our proposal is up to 70x more efficient than the state-of-the-art method.