Lustre Query: Periodic Offline Metadata Monitoring from MDT Backups

Abstract

Parallel file systems in HPC manage metadata for billions of files across distributed storage servers. The structure of this metadata, how files distribute by size, how users concentrate across servers, which storage policies are actually in use, determines operational decisions about capacity planning, load balancing, and data migration. Despite decades of HPC storage research, these structural properties remain underreported in the published literature for HPC parallel file systems. Runtime I/O behavior has been profiled extensively at the application level. Aggregate monitoring captures quotas and throughput. But the metadata that accumulates on disk, the artifact of all user activity over the life of a system, has received little systematic study. Existing tools can extract inode-level detail in principle, but each imposes barriers that discourage routine analysis: online queries load the metadata server, database replicas require ETL pipelines, and low-level utilities demand scripting that few administrators undertake. Most sites already take periodic MDT backup images for disaster recovery, yet these images sit unused between incidents. We propose using them for periodic metadata analysis. The method parses Lustre-specific on-disk structures and exposes them as SQL-queryable tables, requiring no intermediate files and placing no load on the metadata server. We implemented this method as a DuckDB extension and applied it to a Lustre deployment during its early access period. The analysis surfaces operationally relevant patterns: inode counts differ 4.9:1 across MDTs due to user behavior, 99.9997% of files use the default stripe configuration, the Python package manager uv created 69,785 cross-directory hard links on one MDT, and an automated process generated a 129,000-entry directory in under two minutes. We present these findings alongside the monitoring items they motivate, providing a reusable template for periodic metadata analysis. The tool is available at https://github.com/tsukuba-hpcs/lustre-query.