Scalable File System Indexing
Our current areas of focus are scalable indexing architectures for storage systems, improved file system namespaces, and incorporating concepts from databases and information retrieval, such as ranked search and more intelligent indexes into file systems. We particularly emphasize queries over extended and user-supplied metadata, such as scientific metadata and document metadata.
We are exploring new file system designs where search is first-class functionality rather than an after thought. The current approach of using a search index in addition to the file system's index requires two large and separate index structures to be maintained. This separation forces users and applications to access and update two structures when using their data. Our file system designs take a new approach to internal file system structures, layouts, and logging that are search optimized. Our new design can improve search performance, allow data layouts based on how files are queried, and improve efficiency by reducing the number of index structures that must be maintained. We are in the process of implementing these concepts in the Ceph distributed file system.
In addition, we are doing active research into effective ways of partitioning metadata into indexes. A partitioned metadata index can rule out irrelevant files and quickly focus on files that are more likely to match the search criteria. By integrating partitioning with security criteria, we have been able to design a highly scalable design for scalable metadata search. This allows us to eliminate files that the querier cannot view without ever loading those indexes from storage. We have implemented and tested this system. Our results are available in the proceedings of the 26th IEEE Symposium on Massive Storage Systems and Technologies.
Beyond new mechanisms for indexing and metadata management, we are investigating new indexing policies that are driven by user demand. These policies scale an index's granularity and availability to strike a balance between CPU and storage utilization.
Our previous work in this area includes work done in collaboration with NetApp. Our metadata search index, Spyglass, leverages the characteristics unique to storage systems, such as data distributions and hierarchical namespaces, to design new search and indexing algorithms. Our design has search performance that can outperform basic DBMS-based solutions by up to four orders of magnitude, allows time-traveling queries over versioned metadata, and can efficiently re-crawl very large file systems.
We have also previously done work on a file system query language, QUASAR, that allows users to have powerful semantic access to stored data. QUASAR allows semantic file system views and directories to be created, which provide more meaningful data representations. Inter-file relationships, such as provenance, can be expressed and searched through links. To aid browsing, we are investigating applying faceted search to QUASAR. Faceted search uses rich key-value metadata to allow users to interactively navigate the search space and can allow interfaces to be automatically personalized for each user.