DBScholar

Back to papers

CAMAL: Optimizing LSM-trees via Active Learning

Summary: CAMAL tunes LSM-tree parameters via active learning, coupling ML with cost models to optimize reads/writes. Decoupled active learning, online adaptation to dynamic workloads, and extrapolation yield 28% average gains and up to 8x over prior RocksDB. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h242dde7a889f3f87
Venue
SIGMOD
Year
2024
Pagerank
5.4086685e-05
Overall Rank
7,925 | 46.88%
DOI
10.1145/3677138

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yu_sigmod24,
        title = {{CAMAL: Optimizing LSM-trees via Active Learning}},
        author = {Yu, Weiping and Luo, Siqiang and Yu, Zihao and Cong, Gao},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3677138},
        url = {https://dl.acm.org/doi/10.1145/3677138},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 46 of 46 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 0.00046249672
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
224 Self-Driving Database Management Systems 2017 CIDR 0.00023973185
238 LinkBench: a Database Benchmark Based on the Facebook Social Graph 2013 SIGMOD 0.00023594951
261 bLSM: A General Purpose Log Structured Merge Tree 2012 SIGMOD 0.00022862412
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
313 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021288111
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
402 Monkey: Optimal Navigable Key-Value Store 2017 SIGMOD 0.00019070605
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
761 Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging 2018 SIGMOD 0.00014191047
814 SlimDB: A Space-Efficient Key-Value Storage Engine For Semi-Sorted Data 2017 VLDB 0.00013697533
892 SuRF: Practical Range Query Filtering with Fast Succinct Tries 2018 SIGMOD 0.00013207671
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012181459
1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011129451
1,310 Realtime Data Processing at Facebook 2016 SIGMOD 0.00011065568
1,324 X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing 2019 SIGMOD 0.00011019001
1,429 The Log-Structured Merge-Bush & the Wacky Continuum 2019 SIGMOD 0.00010685393
1,572 Compaction management in distributed key-value datastores 2015 VLDB 0.00010174039
1,888 WeTune: Automatic Discovery and Verification of Query Rewrite Rules 2022 SIGMOD 9.4148912e-05
1,900 Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn 2019 CIDR 9.3926585e-05
1,991 Chucky: A Succinct Cuckoo Filter for LSM-Tree 2021 SIGMOD 9.2264312e-05
2,290 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.6813363e-05
2,667 Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores 2020 SIGMOD 8.1324472e-05
2,780 Constructing and Analyzing the LSM Compaction Design Space 2021 VLDB 8.0073289e-05
2,796 Lethe: A Tunable Delete-Aware LSM Engine 2020 SIGMOD 7.9849253e-05
2,882 Spooky: Granulating LSM-Tree Compactions Correctly 2022 VLDB 7.894356e-05
3,012 Accordion: Better Memory Organization for LSM Key-Value Stores 2018 VLDB 7.7414696e-05
3,489 Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine 2022 VLDB 7.2421958e-05
3,575 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1769215e-05
3,679 Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines 2020 VLDB 7.0849238e-05
3,959 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.8804723e-05
4,207 Proteus: A Self-Designing Range Filter 2022 SIGMOD 6.7226406e-05
4,526 Optimization for Active Learning-based Interactive Database Exploration 2019 VLDB 6.5474668e-05
4,796 LogKV: Exploiting Key-Value Stores for Event Log Processing 2013 CIDR 6.3990791e-05
4,879 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.3567479e-05
5,046 Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems 2021 VLDB 6.2884087e-05
5,056 Lightweight Cardinality Estimation in LSM-based Systems 2018 SIGMOD 6.282339e-05
5,065 Dissecting, Designing, and Optimizing LSM-based Data Stores 2022 SIGMOD 6.2789973e-05
5,272 Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty 2022 VLDB 6.1887235e-05
6,137 Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads 2023 SIGMOD 5.8576977e-05
7,715 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4556951e-05
9,215 Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space 2024 SIGMOD 5.1921944e-05
Previous Page 1 / 1 Next

Semantically Similar Papers