Multi-Tenant Log Search: Designing for Cost-Effectiveness and Performance at Scale

Abstract

This article presents a novel architecture for multi-tenant log search systems that addresses the critical challenges of cost efficiency and performance at scale. As organizations deploy increasingly complex distributed systems, the ability to efficiently query and analyze logs becomes essential for maintaining operational reliability. Traditional log search solutions face significant restrictions when scaled to multi-tenant environments, particularly regarding resource efficiency, performance isolation, and cost sustainability. The architecture proposed here employs a comprehensive approach that balances competing requirements through several interconnected services: dynamic cluster provisioning, intelligent tenant routing, user migration mechanisms, granular resource controls, and tiered entitlement systems. These components work in concert to maintain strict tenant isolation while optimizing resource utilization. The design incorporates sophisticated data segregation at the index level and implements performance optimization techniques, including efficient sharding, query caching, distributed execution, and asynchronous ingestion. Additionally, the architecture leverages multiple cost reduction strategies, including tiered storage implementation, automated lifecycle management, compute optimization, cloud resource utilization, and consumption-based billing models. The resulting system demonstrates substantial improvements in both performance metrics and operational costs compared to existing solutions, providing organizations with a viable path to scalable log search capabilities that remain economically sustainable even as data volumes continue to grow exponentially.