The in-memory key-value store provides a persistence method to ensure data durability. The currently provided methods are either to create a snapshot file of a current dataset or to write the log of the performed command in the log file. However, the snapshot method has a risk of data loss and append only logging method cause a system failure due to an increase in log file size. To prevent excessive AOF file size growth, the in-memory key-value store provides a reconstruction method, but also a performance degradation and excessive memory usage occur. In this paper, we propose a new persistence method for effective memory usage and throughput. The new approach is called Logging Exploiting SnapShot (LESS). LESS is a method that combines the advantages of a snapshot using low memory usage and the benefits of an append only logging method that guarantees data persistence. We implemented LESS on Redis and conducted experiments. A benchmark test demonstrated that the proposed approach reduces the maximum memory usage by 57% and it is 2.7 times faster than the original approach. Overall, the experimental results showed that LESS is effective for Redis.
|Title of host publication||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Apr 1|
|Event||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan|
Duration: 2019 Feb 27 → 2019 Mar 2
|Name||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Conference||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019|
|Period||19/2/27 → 19/3/2|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the SW Starlab support program (IITP-2017-0-00477) supervised by the IITP (Institute for Information & communications Technology Promotion).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems