Query Execution Performance Analysis of Column-Oriented Database in Dashboard
Abstract
In making reports or dashboards from operational data, problems often occur in the query process with low speed in responding to an output, causing the server to experience overload. This condition often occurs in companies or higher education organizations in managing academic data. This condition can be improved by optimizing the database server by integrating relational databases with column-oriented databases to speed up query responses and save development costs. Based on the experiments that had been carried out, column-oriented has succeeded in optimizing with a significant difference in query execution time and the server does not crash.
Full Text:
PDFReferences
W. Khan, W. Ahmad, B. Luo, and E. Ahmed, “SQL Database with physical database tuning technique and NoSQL graph database comparisons,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019, no. Itnec, pp. 110–116, doi: 10.1109/ITNEC.2019.8729264.
Hendra and W. Andriyani, “Studi Komparasi Menyimpan Dan Menampilkan Data Histori Antara Database Terstruktur Mariadb Dan Database Tidak Terstruktur Influxdb,” J. Teknol. Technoscientia, vol. 12, no. 2, pp. 168–174, 2020.
W. Puangsaijai and S. Puntheeranurak, “A comparative study of relational database and key-value database for big data applications,” in 2017 International Electrical Engineering Congress (iEECON), 2017, no. March, pp. 1–4, doi: 10.1109/IEECON.2017.8075813.
A. Alharthi, V. Krotov, and M. Bowman, “Addressing barriers to big data,” Bus. Horiz., vol. 60, no. 3, pp. 285–292, May 2017, doi: 10.1016/j.bushor.2017.01.002.
N. Saeed, “Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases,” no. February, 2020.
Steve Ataky Tsham Mpinda, Patrick Andjasubu Bungama, and Luis Gustavo Maschietto, “From Relational Database to Column-Oriented NoSQL Database: Migration Process,” Int. J. Eng. Res., vol. V4, no. 05, May 2015, doi: 10.17577/IJERTV4IS050021.
A. Skidanov, A. J. Papito, and A. Prout, “A column store engine for real-time streaming analytics,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016, pp. 1287–1297, doi: 10.1109/ICDE.2016.7498332.
J. Sompolski, M. Zukowski, and P. Boncz, “Vectorization vs. compilation in query execution,” in Proceedings of the Seventh International Workshop on Data Management on New Hardware - DaMoN ’11, 2011, no. DaMoN, pp. 33–40, doi: 10.1145/1995441.1995446.
S. Meraji, J. Keenleyside, S. Kamath, and B. Blainey, “Towards a Combined Grouping and Aggregation Algorithm for Fast Query Processing in Columnar Databases with GPUs,” in 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, 2015, pp. 594–603, doi: 10.1109/IPDPSW.2015.21.
H. Lang, T. Mühlbauer, F. Funke, P. A. Boncz, T. Neumann, and A. Kemper, “Data Blocks,” in Proceedings of the 2016 International Conference on Management of Data - SIGMOD ’16, 2016, vol. 26-June-20, no. June, pp. 311–326, doi: 10.1145/2882903.2882925.
R. S. Kalan and M. O. Unalir, “Leveraging big data technology for small and medium-sized enterprises (SMEs),” in 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 2016, no. Iccke, pp. 1–6, doi: 10.1109/ICCKE.2016.7802106.
S. Kalid, A. Syed, A. Mohammad, and M. N. Halgamuge, “Big-data NoSQL databases: A comparison and analysis of ‘Big-Table’, ‘DynamoDB’, and ‘Cassandra,’” in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, 2017, pp. 89–93, doi: 10.1109/ICBDA.2017.8078782.
M.-E. Vasile, G. Avolio, and I. Soloviev, “Evaluating InfluxDB and ClickHouse database technologies for improvements of the ATLAS operational monitoring data archiving,” J. Phys. Conf. Ser., vol. 1525, no. 1, p. 012027, Apr. 2020, doi: 10.1088/1742-6596/1525/1/012027.
T. Zheng, Z. Zhang, and X. Cheng, “SAHA: A String Adaptive Hash Table for Analytical Databases,” Appl. Sci., vol. 10, no. 6, p. 1915, Mar. 2020, doi: 10.3390/app10061915.
F. de Moura Rezende dos and M. Holanda, “Performance Analysis of Financial Institution Operations in a NoSQL Columnar Database,” in 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 2020, vol. 2020-June, no. June, pp. 1–6, doi: 10.23919/CISTI49556.2020.9140981.
A. Czerepicki, “Study on effectiveness of using column-oriented databases in the processing of measurement characteristics of an electric vehicle,” Arch. Transp., vol. 51, no. 3, pp. 77–84, Sep. 2019, doi: 10.5604/01.3001.0013.6164.
H. R. Vyawahare, P. P. Karde, and V. M. Thakare, “Hybrid Database Model For Efficient Performance,” Procedia Comput. Sci., vol. 152, no. September, pp. 172–178, 2019, doi: 10.1016/j.procs.2019.05.040.
B. Djamaluddin, P. Prabhakar, B. James, A. Muzakir, and H. AlMayad, “Real-Time Drilling Operation Activity Analysis Data Modelling with Multidimensional Approach and Column-Oriented Storage,” in Day 3 Wed, March 20, 2019, 2019, vol. 2019-March, no. 1, pp. 1–13, doi: 10.2118/194701-MS.
S. Deepak, S. U. Kumar, M. Durgesh, and P. B. K., “Query Processing and Optimization of Parallel Database System in Multi Processor Environments,” in 2012 Sixth Asia Modelling Symposium, 2012, pp. 191–194, doi: 10.1109/AMS.2012.49.
R. Titos-Gil, R. Fernández-Pascual, A. Ros, and M. E. Acacio, “PfTouch: Concurrent page-fault handling for Intel restricted transactional memory,” J. Parallel Distrib. Comput., vol. 145, pp. 111–123, Nov. 2020, doi: 10.1016/j.jpdc.2020.06.009.
H.-J. Kim, E.-J. Ko, Y.-H. Jeon, and K.-H. Lee, “Migration from RDBMS to Column-Oriented NoSQL: Lessons Learned and Open Problems,” in Lecture Notes in Electrical Engineering, vol. 461, 2018, pp. 25–33.
DOI: http://dx.doi.org/10.26798/jiss.v1i2.768
Article Metrics
Abstract view : 578 timesPDF - 452 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Bagas Triaji
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.