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DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees
Summary: DobLIX: a dual-objective learned index for LSM trees that trains models to minimize both index lookup error and downstream on-disk data-access cost, addressing I/O-heavy lookups when index parts live on disk. Includes an RL tuner for online adaptation, yielding 1.19×–2.21× throughput gains in RocksDB while preserving write efficiency.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 14015
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,712 | 25.48%
- DOI
-
10.14778/3749646.3749667
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 826 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016224841 |
| 835 |
Finding Frequent Items in Data Streams |
2008 |
VLDB |
0.00016109621 |
| 857 |
The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds |
2020 |
VLDB |
0.00015882892 |
| 1,311 |
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging |
2018 |
SIGMOD |
0.00012657439 |
| 1,337 |
HoloDetect: Few-Shot Learning for Error Detection |
2019 |
SIGMOD |
0.00012497164 |
| 1,375 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012303141 |
| 1,460 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011887068 |
| 2,115 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5257379e-05 |
| 3,440 |
Approximate Denial Constraints |
2020 |
VLDB |
7.0918817e-05 |
| 4,084 |
APEX: A High-Performance Learned Index on Persistent Memory |
2022 |
VLDB |
6.4622113e-05 |
| 4,128 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4292373e-05 |
| 4,646 |
CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm |
2022 |
VLDB |
6.0250374e-05 |
| 5,074 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7175726e-05 |
| 5,319 |
DILI: A Distribution-Driven Learned Index |
2023 |
VLDB |
5.5713974e-05 |
| 5,581 |
CliffGuard: A Principled Framework for Finding Robust Database Designs |
2015 |
SIGMOD |
5.424205e-05 |
| 5,592 |
PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery |
2023 |
VLDB |
5.4210633e-05 |
| 5,791 |
Dissecting, Designing, and Optimizing LSM-based Data Stores |
2022 |
SIGMOD |
5.3268999e-05 |
| 5,863 |
GRF: A Global Range Filter for LSM-Trees with Shape Encoding |
2024 |
SIGMOD |
5.2979639e-05 |
| 6,398 |
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty |
2022 |
VLDB |
5.0819209e-05 |
| 6,445 |
Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices |
2023 |
SIGMOD |
5.0589805e-05 |
| 7,390 |
Making In-Memory Learned Indexes Efficient on Disk |
2024 |
SIGMOD |
4.7431654e-05 |
| 7,620 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
4.693568e-05 |
| 7,808 |
CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure |
2024 |
SIGMOD |
4.6455813e-05 |
| 7,894 |
LITS: An Optimized Learned Index for Strings |
2024 |
VLDB |
4.6240341e-05 |
| 8,339 |
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice |
2025 |
SIGMOD |
4.5434069e-05 |
| 8,417 |
The Case for Learned In-Memory Joins |
2023 |
VLDB |
4.5194164e-05 |
| 8,627 |
Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines |
2024 |
SIGMOD |
4.4829101e-05 |
| 9,844 |
DumpKV: Learning based lifetime aware garbage collection for key value separation in LSM-tree |
2025 |
VLDB |
4.2721228e-05 |
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