Storing semi-structured data on disk drives

Applications that manage semi-structured data are becoming increasingly commonplace. Current approaches for storing semi-structured data use existing storage machinery; they either map the data to relational databases, or use a combination of flat files and indexes. While employing these existing storage mechanisms provides readily available solutions, there is a need to more closely examine their suitability to this class of data. Particularly, retrofitting existing solutions for semi-structured data can result in a mismatch between the tree structure of the data and the access characteristics of the underlying storage device (disk drive). This study explores various possibilities in the design space of native storage solutions for semi-structured data by exploring alternative approaches that match application data access characteristics to those of the underlying disk drive. For evaluating the effectiveness of the proposed native techniques in relation to the existing solution, we experiment with XML data using the XPathMark benchmark. Extensive evaluation reveals the strengths and weaknesses of the proposed native data layout techniques. While the existing solutions work really well for deep-focused queries into a semi-structured document (those that result in retrieving entire subtrees), the proposed native solutions substantially outperform for the non-deep-focused queries, which we demonstrate are at least as important as the deep-focused. We believe that native data layout techniques offer a unique direction for improving the performance of semi-structured data stores for a variety of important workloads. However, given that the proposed native techniques require circumventing current storage stack abstractions, further investigation is warranted before they can be applied to general-purpose storage systems.