ADOC: Automatically Harmonizing Dataflow Between Components in Log-Structured Key-Value Stores for Improved Performance


Jinghuan Yu, City University of Hong Kong; Sam H. Noh, UNIST & Virginia Tech; Young-ri Choi, UNIST; Chun Jason Xue, City University of Hong Kong


Log-Structure Merge-tree (LSM) based Key-Value (KV) systems are widely deployed. A widely acknowledged problem with LSM-KVs is write stalls, which refers to sudden performance drops under heavy write pressure. Prior studies have attributed write stalls to a particular cause such as a resource shortage or a scheduling issue. In this paper, we conduct a systematic study on the causes of write stalls by evaluating RocksDB with a variety of storage devices and show that the conclusions that focus on the individual aspects, though valid, are not generally applicable. Through a thorough review and further experiments with RocksDB, we show that data overflow, which refers to the rapid expansion of one or more components in an LSM-KV system due to a surge in data flow into one of the components, is able to explain the formation of write stalls. We contend that by balancing and harmonizing data flow among components, we will be able to reduce data overflow and thus, write stalls. As evidence, we propose a tuning framework called ADOC (Automatic Data Overflow Control) that automatically adjusts the system configurations, specifically, the number of threads and the batch size, to minimize data overflow in RocksDB. Our extensive experimental evaluations with RocksDB show that ADOC reduces the duration of write stalls by as much as 87.9% and improves performance by as much as 322.8% compared with the auto-tuned RocksDB. Compared to the manually optimized state-of-the-art SILK, ADOC achieves up to 66% higher throughput for the synthetic write-intensive workload that we used, while achieving comparable performance for the real-world YCSB workloads. However, SILK has to use over 20% more DRAM on average.

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@inproceedings {285764,
author = {Jinghuan Yu and Sam H. Noh and Young-ri Choi and Chun Jason Xue},
title = {{ADOC}: Automatically Harmonizing Dataflow Between Components in {Log-Structured} {Key-Value} Stores for Improved Performance},
booktitle = {21st USENIX Conference on File and Storage Technologies (FAST 23)},
year = {2023},
isbn = {978-1-939133-32-8},
address = {Santa Clara, CA},
pages = {65--80},
url = {},
publisher = {USENIX Association},
month = feb,

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