RRPlib: A spark library for representing HDFS blocks as a set of random sample data blocks

Analyzing big data is a challenging problem in cluster computing systems especially when the data volume goes beyond the available computing resources. Sampling is the favored solution for such problems. It summarizes or reduces the amount of data, taking into consideration the statistical characteristics of data distribution. However, the traditional method to sample the massive data by drawing record-by-record is a computationally expensive process because a full scan of the whole data is needed to be performed. While if the massive data is partitioned into a set of data blocks with each block is a random sample data block, the processing time for selecting some blocks as a sample (or different samples) is computationally cheaper. The main purpose of the software described in this paper is to represent the HDFS blocks as a set of random sample data blocks which also stored in HDFS. Our empirical results show that the performance of the partitioning operation is acceptable in the real application especially this operation is only performed once, thereby analysis on terabyte data becomes more natural. (C) 2019 Elsevier B.V. All rights reserved.