Tuning the Lookahead Distance for PostgreSQL Asynchronous IO

Summary: Adaptive PostgreSQL AIO read streams tune lookahead distance from IO-completion feedback, avoiding manual producer/consumer configuration. Drawing on optimal-flow-control theory and BBR, the method often finds latency-minimizing concurrency across diverse real-world systems. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: hee275877d17be6cf
Venue: VLDB
Year: 2026
Pagerank: 4.9605343e-05
Overall Rank: 10,982 | 26.39%
DOI: 10.14778/3827998.3828010
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Wentao Wu (Microsoft)
2. Jiasheng Hu (University of Toronto)
3. Manoj Syamala (Microsoft)
4. Andres Freund (Microsoft)
5. Vivek Narasayya (Microsoft)

BibTeX Citation

@article{wu_vldb26,
        title = {{Tuning the Lookahead Distance for PostgreSQL Asynchronous IO}},
        author = {Wu, Wentao and Hu, Jiasheng and Syamala, Manoj and Freund, Andres and Narasayya, Vivek},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {12},
        pages = {3982--3995},
        doi = {10.14778/3827998.3828010},
        url = {https://doi.org/10.14778/3827998.3828010},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
74	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00037164008
126	An Evaluation of Buffer Management Strategies for Relational Database Systems	1985	VLDB	0.00030358639
151	An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server	1997	VLDB	0.00028611216
207	The LRU-K Page Replacement Algorithm For Database Disk Buffering	1993	SIGMOD	0.00024963419
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
411	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.0001874043
572	2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm	1994	VLDB	0.00016154673
756	Automatic Physical Database Tuning: A Relaxation-based Approach	2005	SIGMOD	0.00014209974
1,294	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011129451
1,400	Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms	2020	VLDB	0.00010759064
1,823	CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads	2011	VLDB	9.5457638e-05
3,057	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	7.6865211e-05
3,172	CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions	2021	VLDB	7.5556865e-05
3,384	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.3392425e-05
3,655	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	7.1137504e-05
3,679	Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines	2020	VLDB	7.0849238e-05
4,990	Budget-aware Index Tuning with Reinforcement Learning	2022	SIGMOD	6.3120056e-05
5,309	Practical Prefetching via Data Compression (extended abstract)	1993	SIGMOD	6.1726798e-05
9,829	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.1066866e-05
9,948	High-Performance DBMSs with io_uring: When and How to Use It	2026	VLDB	5.0910746e-05
11,288	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9605343e-05

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