Preventing Data Loss using Raft Consensus Algorithm in a Decentralized Database System

Abstract

In today’s digital era, decentralized database management systems have gained significant attention due to their ability to provide scalability, fault tolerance, and improved performance. However, ensuring data integrity, preventing data loss, and maintaining data consistency in such systems remain challenging tasks. This thesis addresses these challenges by proposing a peer-to-peer gossip-based solution that leverages the Raft consensus algorithm and replicated log method. The proposed solution focuses on making each node in the database cluster a witness to transactions, allowing for consensus on the current state of the database. By utilizing gossip-based protocols, transaction information is disseminated among nodes, ensuring that updates reach all relevant participants. The Raft consensus algorithm is employed to achieve agreement on the committed transactions, while the replicated log method synchronizes transaction logs across all nodes. The objectives of this thesis include preventing data loss, maintaining data con sistency, and meeting high transaction and view request targets. With a target transaction rate of 1000 transactions per second and a target view request rate of 10000 requests per second, the solution aims to deliver robust performance and reliability. By combining the peer-to-peer gossip-based approach, Raft consensus algorithm, and replicated log method, the proposed solution offers benefits such as fault tolerance, scalability, and data consistency. The thesis contributes to the field by addressing the limitations of current database systems and proposing an innovative solution that ensures data integrity in de centralized environments. The limitations and complexities of Direct Mail, Anti Entropy, and Rumor Mongering techniques are analyzed, leading to the devel opment of a more effective and efficient solution. The solution’s architecture, mechanisms, and protocols are designed to meet the specified targets and provide a reliable foundation for decentralized database management systems. Through simulations and performance evaluations, the proposed solution demon strates its effectiveness in preventing data loss, maintaining data consistency, and meeting the specified transaction and view request targets. The results highlight the solution’s scalability, fault tolerance, and ability to handle high transaction rates. In conclusion, this thesis presents a peer-to-peer gossip-based solution that lever ages the Raft consensus algorithm and replicated log method to prevent data loss and ensure data consistency in decentralized database management systems. The solution offers a robust and scalable approach, addressing the limitations of exist ing techniques. With its potential applications in various domains, the proposed solution contributes to the advancement of decentralized database management systems, providing a foundation for reliable and high-performance data storage and processing.