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Spyglass: Fast, Scalable Metadata Search for Large-Scale Storage Systems


March 01, 2009


Andrew W. Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, and Ethan L. Miller.

Spyglass is a search system that provides fast and complex searches over large-scale file metadata by exploiting metadata search properties. 

The scale of today’s storage systems has made it increasingly difficult to find and manage files. To address this, we have developed Spyglass, a file metadata search system that is specially designed for large-scale storage systems. Using an optimized design, guided by an analysis of real-world metadata traces and a user study, Spyglass allows fast, complex searches over file metadata to help users and administrators better understand and manage their files. 

Spyglass achieves fast, scalable performance through the use of several novel metadata search techniques that exploit metadata search properties. Flexible index control is provided by an index partitioning mechanism that leverages namespace locality. Signature files are used to significantly reduce a query’s search space, improving performance and scalability. Snapshot-based metadata collection allows incremental crawling of only modified files. A novel index versioning mechanism provides both fast index updates and “back-in-time” search of metadata. An evaluation of our Spyglass prototype using our real-world, large-scale metadata traces shows search performance that is 1-4 orders of magnitude faster than existing solutions. The Spyglass index can quickly be updated and typically requires less than 0.1% of disk space. Additionally, metadata collection is up to 10× faster than existing approaches. 

In Proceedings of the USENIX Conference on File and Storage Technologies 2009 (FAST '09)


A copy of the paper is attached to this posting. spyglass-leung-fast2009.pdf

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