Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

OLP Scheme on Backup Log and Hbase

Authors
Chao Feng, Baoan Li
Corresponding Author
Chao Feng
Available Online November 2016.
DOI
10.2991/aiie-16.2016.73How to use a DOI?
Keywords
component; HBase; persistence; availability; OLP; write operation efficiency
Abstract

As a non-relational database, HBase (Hadoop Data Base) is an open source data storage system based on column cluster and is applied wisely. The HBase will write the log file before the memory caches data, so the cache size and the writing speed of the log file have become two important factors affecting the system performance. Based on backup log, this paper proposes a persistence and availability scheme OLP (Other Log Process), which makes HBase can achieve better performance in different cache sizes. Experiments show that under the premise of ensuring the persistence and availability of data, OLP can obtain stable performance under different cache sizes, and obviously improve the time performance of write operation when the cache does not exceed the default settings.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.73How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Chao Feng
AU  - Baoan Li
PY  - 2016/11
DA  - 2016/11
TI  - OLP Scheme on Backup Log and Hbase
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 322
EP  - 325
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.73
DO  - 10.2991/aiie-16.2016.73
ID  - Feng2016/11
ER  -