BUILDING SCALABLE, LOW-LATENCY SEARCH IN DISTRIBUTED SYSTEMS

Abstract

Distributed search systems achieve scalability and low latency through the orchestrated implementation of five fundamental architectural components. At the foundation lies distributed indexing strategies, which optimize data distribution through range-based partitioning and consistent hashing, enabling systems to scale horizontally while maintaining data accessibility. Building upon this foundation, intelligent load balancing techniques harness machine learning and fuzzy logic to dynamically distribute workloads, preventing system bottlenecks and ensuring optimal resource utilization. Latency minimization in geo-distributed deployments forms the third critical component, combining edge caching with strategic network topology design to reduce response times, while advanced query planning mechanisms optimize request processing across the distributed infrastructure. The fourth component, system caching strategies, implements multi-level architectures with intelligent warming and eviction policies, significantly reducing data access times and backend load. These components are continuously refined through the fifth element: comprehensive performance monitoring and optimization, which leverages feature ranking and neural network models to predict and prevent performance degradation. Together, these architectural components create a robust framework that enables distributed search systems to process massive query volumes with consistent sub-millisecond latency, automatically adapt to traffic variations, and maintain high availability across global deployments, all while optimizing resource utilization and operational costs.