Energy and quality aware query processing in wireless sensor database systems

2007 ◽  
Vol 177 (10) ◽  
pp. 2188-2205 ◽  
Author(s):  
Qingchun Ren ◽  
Qilian Liang
Keyword(s):  
Query Processing ◽  
Database Systems ◽  
Wireless Sensor

scholarly journals Smart Intra-query Fault Tolerance for Massive Parallel Processing Databases

2019 ◽  
Vol 5 (1) ◽  
pp. 65-79
Author(s):  
Yunhong Ji ◽  
Yunpeng Chai ◽  
Xuan Zhou ◽  
Lipeng Ren ◽  
Yajie Qin
Keyword(s):  
Cost Effectiveness ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Query Processing ◽  
Success Rate ◽  
Database Systems ◽  
Analytical Processing ◽  
Commodity Clusters ◽  
Massive Parallel Processing ◽  
Query Latency

AbstractIntra-query fault tolerance has increasingly been a concern for online analytical processing, as more and more enterprises migrate data analytical systems from mainframes to commodity computers. Most massive parallel processing (MPP) databases do not support intra-query fault tolerance. They may suffer from prolonged query latency when running on unreliable commodity clusters. While SQL-on-Hadoop systems can utilize the fault tolerance support of low-level frameworks, such as MapReduce and Spark, their cost-effectiveness is not always acceptable. In this paper, we propose a smart intra-query fault tolerance (SIFT) mechanism for MPP databases. SIFT achieves fault tolerance by performing checkpointing, i.e., materializing intermediate results of selected operators. Different from existing approaches, SIFT aims at promoting query success rate within a given time. To achieve its goal, it needs to: (1) minimize query rerunning time after encountering failures and (2) introduce as less checkpointing overhead as possible. To evaluate SIFT in real-world MPP database systems, we implemented it in Greenplum. The experimental results indicate that it can improve success rate of query processing effectively, especially when working with unreliable hardware.

Sign in / Sign up

Export Citation Format

Share Document