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: 4.9793485e-05
Overall Rank: 10,912 | 26.64%
DOI: 10.14778/3827998.3828010

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
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036684414
128	An Evaluation of Buffer Management Strategies for Relational Database Systems	1985	VLDB	0.00030411805
151	An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server	1997	VLDB	0.00028672526
212	The LRU-K Page Replacement Algorithm For Database Disk Buffering	1993	SIGMOD	0.00024767296
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021282642
322	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00021041865
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018315867
593	2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm	1994	VLDB	0.00015836342
751	Automatic Physical Database Tuning: A Relaxation-based Approach	2005	SIGMOD	0.0001425375
1,289	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011162479
1,397	Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms	2020	VLDB	0.00010789242
1,815	CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads	2011	VLDB	9.5791737e-05
3,051	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	7.7055931e-05
3,158	CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions	2021	VLDB	7.5811757e-05
3,519	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.2389387e-05
3,645	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	7.1397796e-05
3,671	Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines	2020	VLDB	7.1100217e-05
4,968	Budget-aware Index Tuning with Reinforcement Learning	2022	SIGMOD	6.3348803e-05
5,287	Practical Prefetching via Data Compression (extended abstract)	1993	SIGMOD	6.196062e-05
9,790	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.1260323e-05
9,906	High-Performance DBMSs with io_uring: When and How to Use It	2026	VLDB	5.1103839e-05
11,224	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9793485e-05

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