| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 03022 | |
| Number of page(s) | 14 | |
| Section | Intelligent Systems and Computing in Industry, Robotics, and Smart Infrastructure | |
| DOI | https://doi.org/10.1051/itmconf/20257803022 | |
| Published online | 08 September 2025 | |
Performance Optimization of Decentralized Databases: Challenges, Existing Solutions and Prospects
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China
With the increasing demand for data sovereignty, privacy protection, and tamper-proof systems, decentralized databases have gained growing attention in recent years. This review systematically analyzes the tech stacks of representative decentralized database solutions, including BigchainDB, GUN, OrbitDB, Bluzelle, Fluree, TiesDB, Fabric, and CovenantSQL, and provides an in-depth analysis of the performance bottlenecks and existing optimizations on the consensus, network, storage, data engine, and application interface layer of the decentralized databases. This review specifies the efficacy and limitations of multiple optimizations on different layers and different points, including the network protocol, the consensus mechanisms, decentralized data storing technologies, a high-performance data engine, and an improved interface, according to the relevant studies and existing solutions. As a result, despite significant progress in performance optimization, the decentralized databases still face challenges in cross-chain tuning, standardization of benchmarking, balancing between security and performance, and the gap between theory and deployment. To promote the application of decentralized databases, lightweight consensus, smart protocol designs, and standardized performance measuring methods is in demand in the future.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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