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LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems

Summary: LSched is a fully learned, workload-aware query scheduler for in-memory analytical DBs, enabling inter- and intra-query scheduling under dynamic workloads. It accounts for operator types and pipelining, beating heuristics and prior RL by 35-50% on TPC-H, SSB, and JOB. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h09c4f883fec04060
Venue
SIGMOD
Year
2022
Pagerank
6.3978116e-05
Overall Rank
4,797 | 67.85%
DOI
10.1145/3514221.3526158
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sabek_sigmod22,
        title = {{LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems}},
        author = {Sabek, Ibrahim and Ukyab, Tenzin Samten and Kraska, Tim},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526158},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526158},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 17 of 17 citing papers.

Rank Citing Paper Year Venue Pagerank
3,328 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.4094448e-05
3,481 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.2463722e-05
4,343 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.6397106e-05
4,915 Can Learned Models Replace Hash Functions? 2023 VLDB 6.3452789e-05
8,419 The Case for Learned In-Memory Joins 2023 VLDB 5.3198816e-05
8,657 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2790784e-05
9,011 Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries 2025 VLDB 5.2238303e-05
10,206 PLATON: Top-down R-tree Packing with Learned Partition Policy 2023 SIGMOD 5.0491152e-05
10,329 Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDB 2024 SIGMOD 5.0241309e-05
10,347 Intra-Query Runtime Elasticity for Cloud-Native Data Analysis 2025 SIGMOD 5.0195882e-05
10,391 DQSim: Does the Task Assignment Slow Down Your Distributed Database System? 2027 SIGMOD 4.9605343e-05
10,738 P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction 2026 SIGMOD 4.9605343e-05
10,806 AQD: Online Adaptive Query Dispatcher for HTAP Databases 2026 VLDB 4.9605343e-05
10,953 QDBO: A Real-time Quantum-augmented Database System Optimizer 2026 VLDB 4.9605343e-05
11,000 Seron: Smart Query Router for Multi-Primary Cloud-Native Databases with Shared Storage 2026 VLDB 4.9605343e-05
11,505 LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases 2025 VLDB 4.9605343e-05
11,766 QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in Spark 2023 SIGMOD 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 23 of 23 cited papers.

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

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046249672
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034050614
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
215 Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age 2014 SIGMOD 0.00024554257
355 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020146245
375 Umbra: A Disk-Based System with In-Memory Performance 2020 CIDR 0.00019660459
427 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018429918
431 HYRISE—A Main Memory Hybrid Storage Engine 2011 VLDB 0.00018411252
438 Optimizing Space Amplification in RocksDB 2017 CIDR 0.00018259155
461 The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds 2020 VLDB 0.00017835351
870 Learning Multi-dimensional Indexes 2020 SIGMOD 0.0001332034
1,132 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00011873466
1,190 Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads 2021 VLDB 0.00011564169
1,445 LISA: A Learned Index Structure for Spatial Data 2020 SIGMOD 0.00010607118
1,884 Effectively Learning Spatial Indices 2020 VLDB 9.422145e-05
2,147 Quickstep: A Data Platform Based on the Scaling-Up Approach 2018 VLDB 8.9595129e-05
3,715 The Case for a Learned Sorting Algorithm 2020 SIGMOD 7.0549546e-05
4,036 Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement 2015 VLDB 6.8259759e-05
4,096 Self-Tuning Query Scheduling for Analytical Workloads 2021 SIGMOD 6.7949494e-05
5,982 Towards instance-optimized data systems 2021 VLDB 5.9096991e-05
7,347 Scalable Multi-Query Execution using Reinforcement Learning 2021 SIGMOD 5.5308106e-05
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