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Scalable Multi-Query Execution using Reinforcement Learning
Summary: RouLette uses reinforcement learning to drive adaptive, runtime sharing for multi-query execution, avoiding costly pre-optimization. It delivers 1.6–28.3× throughput gains vs a query-at-a-time engine and up to 6.5× vs sharing prototypes on TPC-DS-like workloads.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6080
- Venue
- SIGMOD
- Year
- 2021
- Pagerank
- 4.723898e-05
- Overall Rank
- 7,461 | 48.10%
- DOI
-
10.1145/3448016.3452799
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 80 |
Weaving Relations for Cache Performance |
2001 |
VLDB |
0.00055721729 |
| 115 |
Eddies: Continuously Adaptive Query Processing |
2000 |
SIGMOD |
0.00046221215 |
| 179 |
Efficient and Extensible Algorithms for Multi Query Optimization |
2000 |
SIGMOD |
0.00037672155 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 217 |
Ripple Joins for Online Aggregation |
1999 |
SIGMOD |
0.00033536712 |
| 244 |
Continuously Adaptive Continuous Queries over Streams |
2002 |
SIGMOD |
0.00031066222 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 515 |
QPipe: A Simultaneously Pipelined Relational Query Engine |
2005 |
SIGMOD |
0.00021214633 |
| 659 |
The Making of TPC-DS |
2006 |
VLDB |
0.00018500853 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 940 |
SharedDB: Killing One Thousand Queries With One Stone |
2012 |
VLDB |
0.00015173166 |
| 977 |
Pipelining in Multi-Query Optimization |
2001 |
PODS |
0.0001488881 |
| 1,026 |
Cooperative Scans: Dynamic Bandwidth Sharing in a DBMS |
2007 |
VLDB |
0.00014589172 |
| 1,043 |
Adaptive Ordering of Pipelined Stream Filters |
2004 |
SIGMOD |
0.00014476247 |
| 1,233 |
Maximizing the Output Rate of Multi-Way Join Queries over Streaming Information Sources |
2003 |
VLDB |
0.0001313363 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,299 |
The DataPath System: A Data-Centric Analytic Processing Engine for Large Data Warehouses |
2010 |
SIGMOD |
0.00012751522 |
| 1,429 |
A Scalable, Predictable Join Operator for Highly Concurrent Data Warehouses |
2009 |
VLDB |
0.00012033518 |
| 1,476 |
Efficient Exploitation of Similar Subexpressions for Query Processing |
2007 |
SIGMOD |
0.00011779092 |
| 1,922 |
Selecting Subexpressions to Materialize at Datacenter Scale |
2018 |
VLDB |
0.00010082599 |
| 1,943 |
Procella: Unifying serving and analytical data at YouTube |
2019 |
VLDB |
0.00010012569 |
| 2,925 |
Shared Workload Optimization |
2014 |
VLDB |
7.888494e-05 |
| 3,241 |
TPC-DS, Taking Decision Support Benchmarking to the Next Level |
2002 |
SIGMOD |
7.3305643e-05 |
| 4,267 |
The Case for Precision Sharing |
2004 |
VLDB |
6.3084955e-05 |
| 4,943 |
Lifting the Burden of History from Adaptive Query Processing |
2004 |
VLDB |
5.8170713e-05 |
| 5,293 |
MQJoin: Efficient Shared Execution of Main-Memory Joins |
2016 |
VLDB |
5.5815698e-05 |
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Pagerank |
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SIGMOD |
4.797194e-05 |
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VLDB |
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| 5,371 |
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SIGMOD |
5.5428776e-05 |
| 6,667 |
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VLDB |
4.9688874e-05 |
| 10,565 |
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VLDB |
4.1945683e-05 |
| 5,473 |
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SIGMOD |
5.4885366e-05 |
| 4,961 |
Releasing Cloud Databases from the Chains of Performance Prediction Models |
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CIDR |
5.7984657e-05 |
| 6,040 |
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SIGMOD |
5.2412035e-05 |
| 5,637 |
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VLDB |
5.3979505e-05 |
| 5,671 |
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2022 |
SIGMOD |
5.3803919e-05 |