How Much Can RocksDB Chew? Achieving Near-Zero Write Stalls with Sustainable RocksDB

Summary: S-RocksDB reframes LSM write stalls as a continuous-control problem, using online reinforcement learning to match foreground admission with time-varying compaction capacity. Safe-state guardrails nearly eliminate stalls (69 vs. 64.3M) while bounding P99.99 latency below 0.11 ms. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: hbb7de41abbcfe0eb
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,856 | 27.02%
DOI: 10.14778/3836663.3836668

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Hojin Shin (Dankook University)
2. Yongmin Lee (Dankook University)
3. Seehwan Yoo (Dankook University)
4. Jongmoo Choi (Dankook University)

BibTeX Citation

@article{shin_vldb26,
        title = {{How Much Can RocksDB Chew? Achieving Near-Zero Write Stalls with Sustainable RocksDB}},
        author = {Shin, Hojin and Lee, Yongmin and Yoo, Seehwan and Choi, Jongmoo},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {11},
        pages = {3202--3215},
        doi = {10.14778/3836663.3836668},
        url = {https://doi.org/10.14778/3836663.3836668},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036684414
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021282642
400	Monkey: Optimal Navigable Key-Value Store	2017	SIGMOD	0.00019129175
430	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018409112
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018315867
555	SageDB: A Learned Database System	2019	CIDR	0.00016506678
754	Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging	2018	SIGMOD	0.00014236015
816	SlimDB: A Space-Efficient Key-Value Storage Engine For Semi-Sorted Data	2017	VLDB	0.00013687311
1,302	Apache Hadoop Goes Realtime at Facebook	2011	SIGMOD	0.00011111126
1,422	The Log-Structured Merge-Bush & the Wacky Continuum	2019	SIGMOD	0.00010725538
4,041	Disaggregating RocksDB: A Production Experience	2023	SIGMOD	6.8371576e-05
4,121	On Performance Stability in LSM-based Storage Systems	2020	VLDB	6.7946812e-05
5,248	Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty	2022	VLDB	6.211056e-05
6,124	Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads	2023	SIGMOD	5.8788211e-05
7,241	CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure	2024	SIGMOD	5.5777445e-05
8,221	ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads	2026	VLDB	5.3759492e-05

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