Pantheon: Exascale File System Search for Scientific Computing

Abstract

Modern scientific computing generates petabytes of data in billions of files that must be managed. These files are often organized, by name, in a hierarchical directory tree common to most file systems. As the scale of data has increased, this has proven to be a poor method of file organization. Recent tools have allowed for users to navigate files based on file metadata attributes to provide more meaningful organization. In order to search this metadata, it is often stored on separate metadata servers. This solution has drawbacks though due to the multi-tiered architecture of many large scale storage solutions. As data is moved between various tiers of storage and/or modified, the overhead incurred for maintaining consistency between these tiers and the metadata server becomes very large. As scientific systems continue to push towards exascale, this problem will become more pronounced. A simpler option is to bypass the overhead of the metadata server and use the metadata storage inherent to the file system. This approach currently has few tools to perform operations at a large scale though. This paper introduces the prototype for Pantheon, a file system search tool designed to use the metadata storage within the file system itself, bypassing the overhead from metadata servers. Pantheon is also designed with the scientific community’s push towards exascale computing in mind. Pantheon combines hierarchical partitioning, query optimization, and indexing to perform efficient metadata searches over large scale file systems.