A Lightweight Time Series Database Cluster Architecture Supporting Master-Slave Scheduling

To address the challenges of high availability and elastic scalability in enterprise-level applications due to the lack of mature clustering solutions in mainstream open-source time-series databases, this paper proposes a lightweight time-series database cluster architecture supporting master-slave scheduling. The architecture is tailored for small- to medium-scale scenarios requiring wide-table storage and fast querying of second-level time-series data. It adopts a three-tier decoupled design consisting of clients, master-slave proxy nodes, and time-series database servers. The system employs client-side load balancing strategies combined with proxy-level redundancy and local caching mechanisms to ensure high efficiency and stability of data access. The architecture supports automatic proxy failover and enables server-side sharding and redundant backup, significantly enhancing system reliability and horizontal scalability. This solution provides a lightweight, highly available, and scalable clustering architecture for enterprise-level time-series data management.