IndexFS: Scaling File System Metadata Performance

Contacts: Kai Ren, Qing Zheng, Garth Gibson

The growing size of modern storage systems is expected to exceed billions of objects, making metadata scalability critical to overall performance. Many existing distributed file systems only focus on providing highly parallel fast access to file data, and lack a scalable metadata service.

We introduce a middleware design called IndexFS that adds support to existing file systems such as PVFS, Lustre, and HDFS for scalable high-performance operations on metadata and small files. IndexFS uses a table-based architecture that incrementally partitions the namespace on a per-directory basis using GIGA+ technique, preserving server and disk locality for small directories. An optimized log-structured layout is used to store metadata and small files efficiently. IndexFS applies two client-based storm-free caching techniques: bulk namespace insertion for creation intensive workloads such as N-N checkpointing; and stateless consistent metadata caching for hot spot mitigation.

The architecture of IndexFS: middleware layered on top of existing cluster file systems.

By combining these techniques, IndexFS can scale to at least 128 metadata servers, because that is the biggest test we have tried so far. Experiments show our out-of-core metadata throughput out-performing existing solutions such as PVFS, Lustre, and HDFS by 50% to two orders of magnitude.

Per-server and aggregated throughput during mdtest with IndexFS layered on top of Lustre
and HDFS on a log scale. HDFS and Lustre have only one metadata server.

Software Release

The code is open sourced under PDL license, which is a BSD-style three clause license.



Garth Gibson


Kai Ren
Qing Zheng
Swapil Patil



We thank Chuck Cranor, Mitch Franzos, Aaron Caldwell, Alfred Torrez and Sage Weil for helping us run and understand experiments on PanFS, PVFS, HDFS, Lustre and Ceph. We thank Brett Kettering, Andy Pavlo and Wolfgang Richter for helpful discussions and comments on this paper. We especially thank Los Alamos National Laboratory for running our software on one of their HPC clusters (Smog), Panasas for providing a storage cluster and LinkedIn for giving us a trace of its HDFS metadata workload. This research was supported in part by the National Science Foundation under awards CNS-1042537 and CNS-1042543 (PRObE, www.nmc-probe.org), the DOE and Los Alamos National Laboratory, under contract number DE-AC52-06NA25396 subcontracts 161465 and 153593 (IRHPIT), the Qatar National Research Foundation, under award 09-1116-1-172, a Symantec research labs graduate fellowship, and Intel as part of the Intel Science and Technology Center for Cloud Computing (ISTC-CC).

We thank the members and companies of the PDL Consortium: Alibaba Group, Amazon, Datrium, Facebook, Google, Hewlett Packard Enterprise, Hitachi Ltd., Intel Corporation, IBM, Micron, Microsoft Research, NetApp, Inc., Oracle Corporation, Salesforce, Samsung Semiconductor Inc., Seagate Technology, and Two Sigma for their interest, insights, feedback, and support.




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Last updated 29 August, 2016