The exponential growth of unstructured data in cloud storage environments presents significant challenges for search and retrieval operations. Traditional storage systems often struggle with latency, consistency, and scalability when handling metadata indexing for billions of objects. This paper introduces , a novel architectural framework designed to optimize search capabilities within distributed cloud storage. By decoupling metadata from physical storage and implementing a multi-tiered caching mechanism alongside a sharded inverted index, UpStore Search achieves sub-second retrieval times across petabyte-scale datasets. We evaluate the system’s performance against standard distributed search engines, demonstrating a 40% improvement in write throughput and a significant reduction in query latency under high concurrency.
The core of UpStore Search’s retrieval engine is a sharded inverted index. upstore search
: It provides a curated selection of apps that prioritize user data protection. The exponential growth of unstructured data in cloud