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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Kompiang Gede Sukadharma
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Computer Science & IT

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Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data Kompiang Gede Sukadharma; I Putu Gede Hendra Suputra
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p12

Abstract

Big Data Processing needed a reliable system that not let the data loss. But sometimes we need to make the system down for a while because we need to push the newest changes of the system. The automation will help us achieve that. Continuous Integration and Continuous Deployment help us to reduce the downtime and increase the availability of the system. Thus, the implication will be led to reduce of data loss. This research focusses on the implementation of CI/CD Pipeline on single-point-of-failure service on Microservices Architecture. This research use Load-Testing to measure data loss on certain amount of time. The result on this research show that implementing CI/CD Pipeline on the Microservices that we made, make the down time will be less than 45 Second with 20 Virtual user who send the data.
Pengolahan Big Data Dengan Sharding Database Dan Kappa Architecture Untuk Data Time-Series Kompiang Gede Sukadharma; I Putu Gede Hendra Suputra; Ida Ayu Gde Suwiprabayanti Putra; Luh Arida Ayu Rahning Putri
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the digital era, managing and processing Big Data presents challenges. Historical or time-series data is crucial for decision-making. Distributed database systems, specifically database sharding, efficiently distribute CPU load and memory usage. Kappa Architecture outperforms Lambda Architecture by 220% in terms of speed, though Lambda has slightly higher reliability. This research integrates database sharding with Kappa Architecture using Kaggle’s time-series data. Load testing showed 60.46% performance for data retrieval across distributed databases. Remarkably, the system’s reliability reached 100%, even when one of the services failed, handling 10000 new data entries. Furthermore, with the same configuration, it was found that the utilization of Kappa Architecture and sharding database resulted in a 70.26% better performance compared to the system solely implementing sharding database.
Co-Authors I Putu Gede Hendra Suputra Ida Ayu Gde Suwiprabayanti Putra Luh Arida Ayu Rahning Putri