Rethinking Learned Index and LSM-tree Integration

Summary: Identifies mismatches between learned-index assumptions and LSM compaction: SSTable sizing matters less, while fixed error bounds fail under shifting distributions. Wild Turkey co-tunes level-aware compaction and RL-controlled error bounds, improving throughput and reducing stalls and compactions. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: h20f4089d934595af
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,844 | 27.10%
DOI: 10.14778/3836663.3836671

Incoming Non-self Citations Over Time

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

Authors

1. Guangxun Zhao (Dankook University)
2. Yongjie Zhu (Dankook University)
3. Charles Jaranilla (Dankook University)
4. Seehwan Yoo (Dankook University)
5. Jongmoo Choi (Dankook University)

BibTeX Citation

@article{zhao_vldb26,
        title = {{Rethinking Learned Index and LSM-tree Integration}},
        author = {Zhao, Guangxun and Zhu, Yongjie and Jaranilla, Charles and Yoo, Seehwan and Choi, Jongmoo},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {11},
        pages = {3034--3047},
        doi = {10.14778/3836663.3836671},
        url = {https://doi.org/10.14778/3836663.3836671},
        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 19 of 19 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
258	bLSM: A General Purpose Log Structured Merge Tree	2012	SIGMOD	0.00022939599
400	Monkey: Optimal Navigable Key-Value Store	2017	SIGMOD	0.00019129175
430	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018409112
436	Optimizing Space Amplification in RocksDB	2017	CIDR	0.00018319035
463	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017804544
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
779	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014030069
848	Benchmarking Learned Indexes	2021	VLDB	0.00013506188
1,550	Updatable Learned Index with Precise Positions	2021	VLDB	0.00010282449
2,636	Are Updatable Learned Indexes Ready?	2022	VLDB	8.1941043e-05
2,866	APEX: A High-Performance Learned Index on Persistent Memory	2022	VLDB	7.9258875e-05
2,868	Spooky: Granulating LSM-Tree Compactions Correctly	2022	VLDB	7.9241972e-05
3,232	TreeLine: An Update-In-Place Key-Value Store for Modern Storage	2023	VLDB	7.50343e-05
3,697	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	7.0882335e-05
6,124	Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads	2023	SIGMOD	5.8788211e-05
8,532	AirIndex: Versatile Index Tuning Through Data and Storage	2023	SIGMOD	5.3217474e-05
10,002	BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data	2024	SIGMOD	5.0979044e-05

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