摘要
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 MapHeduce 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.
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 MapHeduce 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.
基金
supported by the National Basic Research 973 Program of China under Grant No.2010CB328104
the National Natural Science Foundation of China under Grant Nos.61070161,61202449,61320106007
the National High Technology Research and Development 863 Program of China under Grant No.2013AA013503
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110092130002
the Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No.BM2003201
the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No.93K-9
the Shanghai Key Laboratory of Scalable Computing and Systems of China under Grant No.2010DS680095
