Partition-Based Online Aggregation with Shared Sampling in the Cloud

Abstract

Online aggregation is an attractive sampling-based technology to response aggregation queries by an estimate to the final result, with the confidence interval becoming tighter over time. It has been built into a MapReduce-based cloud system for big data analytics, which allows users to monitor the query progress, and save money by killing the computation early once sufficient accuracy has been obtained. However, there are several limitations that restrict the performance of online aggregation generated from the gap between the current mechanism of MapReduce paradigm and the requirements of online aggregation, such as: 1) the low sampling efficiency due to the lack of consideration of skewed data distribution for online aggregation in MapReduce, and 2) the large redundant I/O cost of online aggregation caused by the independent job execution mechanism of MapReduce. In this paper, we present OLACloud, a MapReduce-based cloud system to well support online aggregation for different data distributions and large-scale concurrent query processing. We propose a content-aware repartition method with a fair-allocation block placement strategy to increase the sampling efficiency and guarantee the storage and computation load balancing simultaneously. We also develop a shared sampling method to share the sampling opportunities among multiple queries to reduce redundant I/O cost. We also implement OLACloud in Hadoop, and conduct an extensive experimental study on the TPC-H benchmark for skewed data distribution. Our results demonstrate the efficiency and effectiveness of OLACloud.