Paper Info
Title
Parallel Database Recovery for Multicore Main-Memory Databases.
Abstract
Main-memory database systems for multicore servers can achieve excellent performance when processing massive volume of OLTP workloads. But crash-resilience mechanism, or namely loggingand-replay, can either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery without introducing any costly logging overhead to the execution of online transactions. We propose TARS, a parallel log-recovery mechanism that is specifically designed for lightweight, coarse-grained command-logging approach. TARS leverages a combination of static and dynamic analyses to parallelize the log replay: at compile time, TARS decomposes stored procedures by carefully analyzing dependencies within and across programs; at recovery time, a re-execution schedule with a high degree of parallelism is attained through lightweight one-pass scans over transaction parameter values. As such, recovery latency is remarkably minimized. We evaluated TARS in a main-memory database prototype running on a 40-core server. Compared to the state-of-the-art mechanisms, TARS yields significantly higher recovery rate without compromising the efficiency of transaction processing.
Year
Venue
Field
2016
arXiv: Databases
Transaction processing,Degree of parallelism,Parallel database,Computer science,Compile time,Online transaction processing,Parallel computing,Server,Database transaction,Multi-core processor,Database
DocType
Volume
Citations 
Journal
abs/1604.03226
2
PageRank 
References 
Authors
0.36
30
4
Name
Order
Citations
PageRank
Yingjun Wu1321.80
Wentian Guo271.77
Chee Yong Chan3643199.24
Kian-Lee Tan46962776.65