DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.157-163

Strategi caching aplikasi berbasis in-memory menggunakan Redis server untuk mempercepat akses data relasional

Application caching strategy based on in-memory using Redis server to accelerate relational data access

Department of Electrical Engineering, Universitas Jenderal Soedirman, Indonesia

Received: 24 Jun 2019; Revised: 18 Mar 2020; Accepted: 20 Mar 2020; Available online: 24 Mar 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Utilization of an in-memory database as a cache can overcome relational database latency problems in a web application, especially when using a lot of join queries. This study aims to model the academic relational data into Redis compatible data and analyze the performance of join queries usage to accelerate access to relational data managed by RDBMS. This study used academic data to calculate student GPA that is modeled in the RDBMS and Redis in-memory database (IMDB). The use of Redis as an in-memory database can significantly increase Mysql database system performance up to 3.3 times faster to display student data using join query and to shorten the time needed to display GPA data to 52 microseconds from 61 milliseconds.

Note: This article has supplementary file(s).

Fulltext View|Download |  common.other
Raw Data Simulasi Redis
Subject
Type Other
  Download (31KB)    Indexing metadata
Email colleagues
Keywords: web application; relational data; in-memory database; Redis
Funding: Kementerian Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Aplikasi Data Pelanggan Berbasis Java dan Mobile pada PT. PLN (Persero) Area Semarang Comparison of distance measurement on k-nearest neighbour in textual data classification Performance comparison of RSA and AES to SMS messages compression using Huffman algorithm MWMOTE optimization for imbalanced data using complete linkage Automatic tsunami arrival detection algorithm for sea level observation system More related articles
Most cited articles
Comparative study of four indoor empirical propagation models for campus wireless network Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms Pemanfaatan Augmented Reality untuk Pembelajaran Pengenalan Alat Musik Piano Perancangan Sistem Sensor Pemonitor Lingkungan Berbasis Jaringan Sensor Nirkabel Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi More cited articles
  1. J. Yan, J. Chen, and W. Jiang, "Data caching techniques in web application," in 2014 Enterprise Systems Conference, Shanghai, Chine, Aug. 2014, pp. 289-293. doi: 10.1109/ES.2014.57
  2. S. Bouchenak, A. Cox, S. Dropsho, S. Mittal, and W. Zwaenepoel, "Caching dynamic web content: designing and analysing an aspect-oriented solution," in ACM/IFIP/USENIX 7th International Middleware Conference, Melbourne, Australia, Dec. 2006, pp. 1-21. doi: 10.1007/11925071_1
  3. W. Ali, S. M. Shamsuddin, and A. S. Ismail, "A survey of web caching and prefetching," International Journal of Advances in Soft Computing and its Applications, vol. 3, no. 1, pp. 1-27, 2011
  4. M. Indrawan-santiago, "Database research : are we at a crossroad ? reflection on NoSQL," in 15th International Conference on Network-Based Information Systems, Melbourne, Australia, Sept. 2012, pp. 45-51. doi: 10.1109/NBiS.2012.95
  5. A. E. Lotfy, A. I. Saleh, H. A. El-Ghareeb, and H. A. Ali, "A middle layer solution to support ACID properties for NoSQL databases," Journal of King Saud University - Computer and Information Sciences, vol. 28, no. 1, pp. 133-145, 2016. doi: 10.1016/j.jksuci.2015.05.003
  6. A. Fadli, M. I. Zulfa, and Y. Ramadhani, "Perbandingan unjuk kerja algoritme klasifikasi data mining dalam sistem peringatan dini ketepatan waktu studi mahasiswa," Jurnal Teknologi dan Sistem Komputer, vol. 6, no. October, pp. 158-163, 2018. doi: 10.14710/jtsiskom.6.4.2018.158-163
  7. J. Sinuraya, "Metode pencarian data menggunakan query hash join dan query nested join," Teknovasi, vol. 4, no. 1, pp. 42-50, 2017
  8. M. Luthfi, M. Data, and W. Yahya, "Perbandingan performa reverse proxy caching nginx dan varnish pada web server apache," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 4, pp. 1457-1463, 2018
  9. R. Nishtala et al., "Scaling memcache at facebook," in 10th USENIX conference on Networked Systems Design and Implementation, Chicago, USA, Apr. 2013, pp. 385-398
  10. C. Liu, K. Ouyang, X. Chu, H. Liu, and Y. Leung, "R-Memcached : a reliable in-memory cache for big key-value stores," Tsinghua Science and Technology, vol. 20, no. 6, pp. 560-573, 2015. doi: 10.1109/TST.2015.7349928
  11. 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, Pattaya, Thailand, Mar. 2017, pp. 8-10. doi: 10.1109/IEECON.2017.8075813
  12. D. J. Carlson, Ebook redis in action. Manning Publications, 2013
  13. N. L. Seth Gilbert, "Brewer's conjecture and the feasibility of consistent, available, and partition-tolerant web services," ACM SIGACT News, vol. 33, no. 2, pp. 51-59, 2002. doi: 10.1145/564585.564601
  14. F. Firdausillah, E. Y. Hidayat, and I. N. Dewi, "NoSQL : latar belakang, konsep, dan kritik," in Seminar Nasional Teknologi Informasi dan Komunikasi Terapan, Semarang, Indonesia, Jun. 2012, pp. 432-438
  15. A. T. Kabakus and R. Kara, "A performance evaluation of in-memory databases," Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 4, pp. 520-525, 2017. doi: 10.1016/j.jksuci.2016.06.007
  16. M. Kusuma, "Evaluasi performa web server menggunakan varnish http reserve proxy dan redis database cache," in Seminar Nasional Inovasi dan Aplikasi Teknologi Industri, Malang, Indonesia, Feb. 2016, pp. 260-264
  17. M. Kusuma, W. Widyawan, and R. Ferdiana, "Performance comparison of caching strategy on wordpress multisite," in 3rd International Conference on Science and Technology - Computer, Yogyakarta, Indonesia, Jul 2017, pp. 176-181. doi: 10.1109/ICSTC.2017.8011874
  18. Redislabs, "Redis," 2014. [Online]. Available: https://redis.io/commands. [Accessed: 23-Nov-2018]
  19. A. P. Negrão, C. Roque, P. Ferreira, and L. Veiga, "An adaptive semantics-aware replacement algorithm for web caching," Journal of Internet Services and Applications, vol. 6, no. 4, pp. 1-14, 2015. doi: 10.1186/s13174-015-0018-4
  20. H. Zhang, G. Chen, and C. Ooi, "In-memory big data management and processing : a survey," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 7, pp. 1920-1948, 2015. doi: 10.1109/TKDE.2015.2427795
  21. D. Li, M. Dong, Y. Yuan, J. Chen, K. Ota, and Y. Tang, "SEER-MCache : a prefetchable memory object caching system for iot real-time data processing," IEEE Internet of Things Journal, vol. PP, no. 8, pp. 3648-3660, 2018. doi: 10.1109/JIOT.2018.2868334
  22. Y. Fan, Y. Wang, and M. Ye, "An improved small file storage strategy in ceph file system," in 14th International Conference on Computational Intelligence and Security, 2018, pp. 488-491. doi: 10.1109/CIS2018.2018.00116
  23. F. Pedone and D. L. Epfl, "A primary-backup protocol for in-memory database replication," in IEEE International Symposium on Network Computing and Applications, Cambridge, USA, Jul. 2006, pp. 204-211. doi: 10.1109/NCA.2006.7
  24. F. Pedone, "Sprint : a middleware for high-performance transaction," in ACM SIGOPS Operationg System Review, vol. 41, no. 3, pp. 385-398, 2007. doi: 10.1145/1272998.1273036
  25. K. Dutta and D. Vandermeer, "Caching to reduce mobile app energy consumption," ACM Transactions on the Web, vol. 12, no. 1, pp. 1-30, 2017. doi: 10.1145/3125778

Last update:

  1. Research on optimization of real-time efficient storage algorithm in data information serialization

    Bin Huang, You Tang, Dhananjay Singh. PLOS ONE, 16 (12), 2021. doi: 10.1371/journal.pone.0260697

  2. Improving Cached Data Offloading Optimization Based on Enhanced Hybrid Ant Colony Genetic Algorithm

    Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari, Waleed Ali. IEEE Access, 10 , 2022. doi: 10.1109/ACCESS.2022.3197205

  3. Web Caching Strategy Optimization Based on Ant Colony Optimization and Genetic Algorithm

    Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari, Waleed Ali. 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2021. doi: 10.1109/ISITIA52817.2021.9502260

  4. LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms

    Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari, Waleed Ali. Electronics, 11 (19), 2022. doi: 10.3390/electronics11192978

  5. Performance Comparison of Swarm Intelligence Algorithms for Web Caching Strategy

    Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari. 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2021. doi: 10.1109/COMNETSAT53002.2021.9530778

  6. Infrastruktur High-Available Learning Management System Universitas Menggunakan Least-Connected Load Balancer

    Imam Ramadhan, Lily Wulandari. Jurnal Masyarakat Informatika, 13 (2), 2022. doi: 10.14710/jmasif.13.2.49176

Last update: 2024-12-23 02:37:45

  1. Performance Comparison of Swarm Intelligence Algorithms for Web Caching Strategy

    Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari. 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2021. doi: 10.1109/COMNETSAT53002.2021.9530778

  2. Caching strategy for Web application – a systematic literature review

    Zulfa M.I.. International Journal of Web Information Systems, 16 (5), 2020. doi: 10.1108/IJWIS-06-2020-0032