TýrFS: Increasing Small Files Access Performance with Dynamic Metadata Replication

Small files are known to pose major performance challenges for file systems. Yet, such workloads are increasingly common in a number of Big Data Analytics workflows or large-scale HPC simulations. These challenges are mainly caused by the common architecture of most state-of-the-art file systems needing one or multiple metadata requests before being able to read from a file. Small input file size causes the overhead of this metadata management to gain relative importance as the size of each file decreases. In this paper we propose a set of techniques leveraging consistent hashing and dynamic metadata replication to significantly reduce this metadata overhead. We implement such techniques inside a new file system named TýrFS, built as a thin layer above the Týr object store. We prove that TýrFS increases small file access performance up to one order of magnitude compared to other state-of-the-art file systems, while only causing a minimal impact on file write throughput.