Improving Flash-Based Disk Cache with Lazy Adaptive Replacement

2016 ◽  
Vol 12 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Sai Huang ◽  
Qingsong Wei ◽  
Dan Feng ◽  
Jianxi Chen ◽  
Cheng Chen
Keyword(s):  
2018 ◽  
Vol 8 (9) ◽  
pp. 1514 ◽  
Author(s):  
Bao Chang ◽  
Hsiu-Fen Tsai ◽  
Yun-Da Lee

This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment.


Author(s):  
JingMin Tu ◽  
Xiangang Luo ◽  
Wenjie Zhao ◽  
Xuejing Xie ◽  
Xincai Wu
Keyword(s):  

1992 ◽  
Vol 41 (6) ◽  
pp. 665-676 ◽  
Author(s):  
D. Thiebaut ◽  
H.S. Stone ◽  
J.L. Wolf

Sign in / Sign up

Export Citation Format

Share Document