OLTP PERFORMANCE IMPROVEMENT USING FILE-SYSTEMS LAYER COMPRESSION
DOI:
https://doi.org/10.11113/jt.v79.8883Keywords:
OLTP database, performance, file-systems layer compressionAbstract
Database for Online Transaction Processing (OLTP) application is used by almost every corporations that has adopted computerisation to support their operational day to day business. Compression in the storage or file-systems layer has not been widely adopted for OLTP database because of the concern that it might decrease database performance. OLTP compression in the database layer is available commercially but it has a significant licence cost that reduces the cost saving of compression. In this research, transparent file-system compression with LZ4, LZJB and ZLE algorithm have been tested to improve performance of OLTP application. Using Swing-bench as the benchmark tool and Oracle database 12c, The result indicated that on OLTP workload, LZJB was the most optimal compression algorithm with performance improvement up to 49% and consistent reduction of maximum response time and CPU utilisation overhead, while LZ4 was the compression with the highest compression ratio and ZLE was the compression with the lowest CPU utilisation overhead. In terms of compression ratio, LZ4 can deliver the highest compression ratio which is 5.32, followed by LZJB, 4.92; and ZLE, 1.76. Furthermore, it is found that there is indeed a risk of reduced performance and/or an increase of maximum response time.
References
H. Plattner. 2009. A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database. Proceedings of the 35th SIGMOD International Conference on Management of Data - SIGMOD '09. Providence, Rhode Island, USA.
M. A. Bassiouni. 1985. Data Compression in Scientific and Statistical Databases. IEEE Transactions on Software Engineering. SE-11(10): 1047-1058.
C. Diaconu, C. Freedman, E. Ismert, P.-Å. Larson, P. Mittal, R. Stonecipher, N. Verma and M. Zwilling. 2013. Hekaton: SQL Server’s Memory -Optimized OLTP Engine. SIGMOD'13, New York.
A. Cuzzocrea and S. Chakravarthy. 2010. Event-based Lossy Compression for Effective and Efficient OLAP over. Data & Knowledge Engineering. 69(1): 678-708.
N. Mukherjee, S. Chavan, M. Colgan, D. Das, M. Gleeson, S. Hase, A. Holloway, H. Jin, . J. Kamp, K. Kulkarni, T. Lahiri, J. Loaiza, N. Macnaughton, V. Marwah, A. Mullick, A. Witkowski, J. Yan and M. Zait. 2015. Distributed Architecture of Oracle Database in-memory. International Conference on Very Large Data Bases. Kohala Coast, Hawaii.
H. Zhang, G. Chen, B. C. Ooi, K. L. Tan and M. Zhang. 2015. In-Memory Big Data Management and Processing: A Survey. IEEE Transactions on Knowledge and Data Engineering. 27(7): 1920-1948.
G. Ray, J. Haritsa and S. Seshadri. 1995. Database Compression: A Performance Enhancement Tool. International Conference on Management of Data.
G. Graefe and L. D. Shapiro. 1991. Data Compression and Database Performance. ACM/IEEE-CS Symposium on Applied Computing, Kansas City.
T. Westman, D. Kossmann, S. Helmer and G. Moerkotte. 2000. The Implemmentation and Performance of Compressed Databases. ACM SIGMOD Record. 29(3): 55-67.
T. Ravichandran. 2013. Real Time Database Compression Optimization Using Iterative. Computer Science & Information Technology (CS & IT). 3(1): 99-105.
A. Habib, A. S. M. L. Hoque and M. S. Rahman. 2012. High Performance Query Operations on Compressed Database. International Journal of Database Theory and Application. 5(3): 1-14.
C. Lin, J. Wang and Y. Papakonstantinou. 2016. Data Compression for Analytics over Large-scale In-memory Column Databases. arXiv:1606.09315v2: 1-4,
A. Wenas and Suharjito. 2016. Improving Data Warehouse Performance Using Filesystem Technology with GZIP, LZJB and ZLE Compression. Jurnal Informatika dan Sistem Informasi. 2(2): 40-51.
A. Tamrakar and V. Nanda. 2012. A Compression Algorithm for Optimization of Storage Consumption of Non Oracle Database. International Journal of Advanced Research in Computer Science and Electronics Engineering. 5(1): 39-43.
M. Sharma and S. Dora. 2012. Efficient Approach for Compression in Data Warehouse. International Journal of Computer Applications. 53(9): 9-11.
D. Giles. 2015. Swingbench [Online]. Available: http://www.dominicgiles.com/Swingbench.pdf. [Accessed 2015].
IBM. 2014. Oracle Database 11g and 12c on IBM Power Systems built with IBM Power8 processor technology and IBM FlashSystem 840. IBM Oracle International Competency Center.
VMware. 2010. Oracle Databases on vSphere Workload Characterization Study. VMware, Palo Alto.
F. N. Almari, P. Zavarsky, R. Ruhl, D. Lindskog and A. Aljaedi. 2012. Performance Analysis of Oracle Database in Virtual Environments. Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on, Fukuoka.
I. E. Tope, P. Zavarsky, R. Ruhl and D. Lindskog. 2011. Performance Evaluation of Oracle VM Server Virtualization Software 64 Bit Linux Environment. Security Measurements and Metrics (Metrisec). 2011 Third International Workshop on, Banff, AB.
D. Ye, A. Pavuluri, C. Waldspurger, B. Tsang, B. Rychlik and S. Woo. 2008. Prototyping a Hybrid Main Memory Using a Virtual Machine Monitor. Computer Design, 2008. ICCD IEEE International Conference on, Lake Tahoe, CA.
T. L. Saaty. 2008. Decision Making with Analytic Hierarchy Process. Int. J. Services Sciences. 1(1): 83-98.
T. L. Saaty and M. Ozdemir. 2003. Negative Priorities in the Analytic Hierarchy Process. Mathematical and Computer Modelling. 37(9): 1063-1075.
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