Name
Abstract | ||
|---|---|---|
Phase Change Memory PCM is a new non-volatile memory technology that is comparable to traditional DRAM with regard to read latency, and markedly superior with regard to storage density and idle power consumption. Due to these desirable characteristics, PCM is expected to play a significant role in the next generation of computing systems. However, it also has limitations in the form of expensive writes and limited write endurance. Accordingly, recent research has investigated how database engines may be redesigned to suit DBMS deployments on the new technology. In this paper, we address the pragmatic goal of minimally altering current implementations of database operators to make them PCM-conscious, the objective being to facilitate an easy transition to the new technology. Specifically, we target the implementations of the \"workhorse\" database operators: sort, hash join and group-by, and rework them to substantively improve the write performance without compromising on execution times. Concurrently, we provide simple but effective estimators of the writes incurred by the new techniques, and these estimators are leveraged for integration with the query optimizer. Our new techniques are evaluated on TPC-H benchmark queries with regard to the following metrics: number of writes, response times and wear distribution. The experimental results indicate that the PCM-conscious operators collectively reduce the number of writes by a factor of 2 to 3, while concurrently improving the query response times by about 20﾿% to 30﾿%. When combined with the appropriate plan choices, the improvements are even higher. In essence, our algorithms provide both short-term and long-term benefits. These outcomes augur well for database engines that wish to leverage the impending transition to PCM-based computing. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-22849-5_19 | DEXA |
Field | DocType | Citations |
Hash join,Query optimization,Data mining,Rework,Phase-change memory,Computer science,sort,Implementation,Database,Query plan,Hash table | Conference | 1 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vishesh Garg | 1 | 1 | 0.34 |
| Abhimanyu Singh | 2 | 1 | 0.34 |
| Jayant R. Haritsa | 3 | 2004 | 228.38 |