INCREASING THE PRODUCTIVITY OF DECENTRALIZED DATABASES THROUGH OPTIMIZATION OF DATA FRAGMENTATION MECHANISMS IN BLOCKCHAIN NETWORKS

DOI: 10.31673/2409-7292.2025.027836

Abstract

The article presents a comprehensive methodology for optimizing the performance of decentralized databases based on
blockchain technology by implementing specialized data fragmentation mechanisms. The current issues of scalability of
distributed registries and the limitations of existing sharding approaches in the context of highly loaded systems are investigated.
An innovative hierarchical data fragmentation model using dynamic shards and adaptive load redistribution based on the
analysis of data access patterns is proposed. A mathematical model for optimizing the distribution of transactions between
shards is developed, taking into account the minimization of cross-sharding operations and balancing the computational load.
An original data structure based on modified prefix trees with vector labels is implemented for effective query routing in a
fragmented environment. The results of a comprehensive experimental study on a test bench with 64 nodes demonstrate an
increase in overall transaction throughput by 37-42% compared to traditional sharding approaches and a decrease in query
processing latency by 28% while maintaining the level of decentralization and cryptographic stability of the system. A
particularly significant improvement in performance (up to 60%) is observed for cross-sharding operations due to the
implementation of an optimized two-phase protocol with elements of batching and pre-validation. The proposed methodology
allows to effectively overcome the existing limitations of the "blockchain trilemma" by intelligently optimizing data structures
and consensus mechanisms, while maintaining the required level of security and decentralization of the system, which is
confirmed by resistance to a wide range of attacks even when a significant proportion of nodes in individual shards are
compromised. In addition to increasing performance, the developed methodology provides a number of additional advantages,
including: improved adaptability to changes in the nature of the load and data access patterns; reduced resource requirements of
individual network nodes due to effective distribution of computational load; increased resistance to attacks specific to sharding
architectures, such as "shard capture" and attacks aimed at violating the atomicity of cross-sharding transactions. The security
analysis demonstrates that the proposed model maintains a high level of protection even when up to 30% of nodes in the system
are compromised, while traditional sharding approaches demonstrate a critical decrease in stability already at 20-25% of
compromised nodes. The cost-effectiveness of the proposed methodology is confirmed by a 22-31% reduction in energy
consumption compared to existing solutions at the same level of performance, which makes it attractive for implementation in
corporate blockchain systems. The results obtained create the basis for further development of high-performance decentralized
data storage and processing systems capable of operating effectively under high loads while maintaining the key advantages of
blockchain technology in the context of transparency, integrity and data protection.
Keywords: data fragmentation, sharding, scalability, performance, blockchain trilemma, distributed ledgers, consensus
mechanisms, smart contracts.

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