Fault-tolerant replication in vector search systems

Аннотация

In this article, an analysis is carried out of the characteristics of fault-tolerant replication in vector OPEN ACCESS search systems, driven by the rapid expansion of SUBMITTED 12 June 2025 ACCEPTED 27 June 2025 generative artificial intelligence capabilities and related PUBLISHED 22 July 2025 methods, including Retrieval-Augmented Generation VOLUME Vol.07 Issue 07 2025 (RAG). The key challenge in this area is to guarantee CITATION both high availability and immutability of information, which is achieved through the implementation of COPYRIGHT various fault-tolerant replication schemes. The present © 2025 Original content from this work may be used under the terms study is aimed at the systematization and comparative of the creative commons attributes 4.0 License. analysis of existing replication models in the context of vector search systems, with attention to the trade-offs between data consistency, service availability, and system response time. The work employs methods of systematic and comparative analysis, as well as a review of academic publications and technical documentation of leading industry solutions. As a result of the conducted analysis, three main classes of replication approaches are identified: leader-follower (primary- backup), consensus-based protocols, and shared- storage architectures. It is shown that the choice of a specific replication scheme is determined by the combination of requirements for throughput, latency, and level of fault tolerance, as well as financial and operational constraints. The conclusions of the study point to the high promise of hybrid solutions that combine elements of different models to achieve an optimal balance between reliability and cost. The material will be useful for system architects of distributed applications, experts in database design, and researchers working on high-load AI systems. Keywords: vector search, vector database, fault tolerance, replication, high availability, distributed systems, nearest neighbor search, data consistency, RAG, MLOps. The American Journal of Management and Economics Innovations 111 https://www.theamericanjournals.com/index.php/tajmei Introduction algorithms are used to manage metadata and a leader- follower architecture is used for replicating index The growing demand for semantic analysis and segments—allows achieving the optimal balance management of heterogeneous unstructured data — between performance and reliability in scalable vector textual corpora, images, and audio recordings — has search systems. become the primary catalyst for the evolution of artificial intelligence technologies. In 2024, the global