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AXE: A Task Decomposition Approach to Learned LSM Tuning

Summary: AXE decomposes LSM tuning into learned cost modeling plus a tuner trained on unlimited synthetic samples, avoiding deployment-time retraining and handling categorical knobs. It scales across instances and environments, achieving higher performance than Bayesian Optimization 71% of the time with 100× lower overhead. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hcfa25aa2fa690148
Venue
VLDB
Year
2025
Pagerank
4.9793485e-05
Overall Rank
11,430 | 23.16%
DOI
10.14778/3773731.3773735

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Authors

BibTeX Citation

@article{huynh_vldb25,
        title = {{AXE: A Task Decomposition Approach to Learned LSM Tuning}},
        author = {Huynh, Andy and Saha, Anwesha and Chaudhari, Harshal A. and Athanassoulis, Manos},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {13},
        pages = {5582--5595},
        doi = {10.14778/3773731.3773735},
        url = {https://doi.org/10.14778/3773731.3773735},
        year = {2025}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 30 of 30 cited papers.

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

Rank Cited Paper Year Venue Pagerank
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
88 Automated Selection of Materialized Views and Indexes for SQL Databases 2000 VLDB 0.00035351639
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
253 Database Cracking 2007 CIDR 0.00023042111
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
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018315867
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
754 Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging 2018 SIGMOD 0.00014236015
922 AsterixDB: A Scalable, Open Source BDMS 2014 VLDB 0.00013068048
1,147 MyRocks: LSM-Tree Database Storage Engine Serving Facebook's Social Graph 2020 VLDB 0.00011813751
1,605 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010093796
1,889 Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn 2019 CIDR 9.4273689e-05
2,720 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.0966919e-05
2,772 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.035288e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
3,051 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.7055931e-05
3,479 Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine 2022 VLDB 7.2695068e-05
3,978 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8807882e-05
4,538 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.553705e-05
4,569 COLT: Continuous On-Line Database Tuning 2006 SIGMOD 6.5298888e-05
5,248 Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty 2022 VLDB 6.211056e-05
5,879 Compactionary: A Dictionary for LSM Compactions 2022 SIGMOD 5.9615203e-05
6,080 From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems 2019 SIGMOD 5.8924903e-05
6,124 Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads 2023 SIGMOD 5.8788211e-05
6,251 Foundations of Automated Database Tuning 2006 VLDB 5.8354449e-05
7,033 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168499e-05
7,905 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.4291824e-05
9,181 Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space 2024 SIGMOD 5.2118872e-05
9,431 MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud 2024 VLDB 5.1783663e-05
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