Designing Query Optimizers for Big Data Problems of The Future

Summary: Vertica SQL Query Optimizer built from the ground up for the Vertica Analytic DB; design decisions and tradeoffs highlighted. Argues that the full power of future big-data systems hinges on a custom, system-tuned optimizer rather than generic approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10744
Venue: VLDB
Year: 2013
Pagerank: 5.093636e-05
Overall Rank: 12,259 | 15.90%
DOI: 10.14778/2536222.2536236

Incoming Non-self Citations Over Time

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

Authors

1. Nga Tran (Hewlett Packard Enterprise)
2. Sreenath Bodagala (Hewlett Packard Enterprise)
3. Jaimin Dave (Hewlett Packard Enterprise)

BibTeX Citation

@article{tran_vldb13,
        title = {{Designing Query Optimizers for Big Data Problems of The Future}},
        author = {Tran, Nga and Bodagala, Sreenath and Dave, Jaimin},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {11},
        pages = {1168--1179},
        doi = {10.14778/2536222.2536236},
        url = {https://doi.org/10.14778/2536222.2536236},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
186	The Vertica Analytic Database: C-Store 7 Years Later	2012	VLDB	0.00026182534
2,078	Query Optimization in Microsoft SQL Server PDW	2012	SIGMOD	9.2078209e-05
4,044	Partial Join Order Optimization in the ParAccel Analytic Database	2009	SIGMOD	6.9400535e-05

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