In this article, an analysis is carried out of the characteristics of fault-tolerant replication in vector search systems, driven by the rapid expansion of generative artificial intelligence capabilities and related methods, including Retrieval-Augmented Generation (RAG). The key challenge in this area is to guarantee both high availability and immutability of information, which is achieved through the implementation of various fault-tolerant replication schemes. The present study is aimed at the systematization and comparative 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.

vector search, vector database, fault tolerance, replication

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