Practical Design Considerations for Wide Locally Recoverable Codes (LRCs)


Saurabh Kadekodi, Shashwat Silas, David Clausen, and Arif Merchant, Google


Most of the data in large-scale storage clusters is erasure coded. At exascale, optimizing erasure codes for low storage overhead, efficient reconstruction, and easy deployment is of critical importance. \textit{Locally recoverable codes (LRCs)} have deservedly gained central importance in this field, because they can balance many of these requirements. In our work we study wide LRCs; LRCs with large number of blocks per stripe and low storage overhead. These codes are a natural next step for practitioners to unlock higher storage savings, but they come with their own challenges. Of particular interest is their \textit{reliability}, since wider stripes are prone to more simultaneous failures.

We conduct a practically-minded analysis of several popular and novel LRCs. We find that wide LRC reliability is a subtle phenomenon that is sensitive to several design choices, some of which are overlooked by theoreticians, and others by practitioners. Based on these insights, we construct novel LRCs called \textit{Uniform Cauchy LRCs}, which show excellent performance in simulations, and a 33% improvement in reliability on unavailability events observed by a wide LRC deployed in a Google storage cluster. We also show that these codes are easy to deploy in a manner that improves their robustness to common maintenance events. Along the way, we also give a remarkably simple and novel construction of distance optimal LRCs (other constructions are also known), which may be of interest to theory-minded readers.

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@inproceedings {285772,
author = {Saurabh Kadekodi and Shashwat Silas and David Clausen and Arif Merchant},
title = {Practical Design Considerations for Wide Locally Recoverable Codes ({{{{{LRCs}}}}})},
booktitle = {21st USENIX Conference on File and Storage Technologies (FAST 23)},
year = {2023},
isbn = {978-1-939133-32-8},
address = {Santa Clara, CA},
pages = {1--16},
url = {},
publisher = {USENIX Association},
month = feb,

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