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PIQL: A Performance Insightful Query Language

Summary: PIQL targets web-scale workloads by layering a declarative query language over distributed key-value stores. It expresses common website queries with guaranteed I/O bounds, delivering predictable performance and reducing the need for onerous explicit indexing and imperative lookups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4412
Venue
SIGMOD
Year
2010
Pagerank
6.1017514e-05
Overall Rank
5,747 | 60.58%
DOI
10.1145/1807167.1807320

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{armbrust_sigmod10,
        title = {{PIQL: A Performance Insightful Query Language}},
        author = {Armbrust, Michael and Tu, Stephen and Fox, Armando and Franklin, Michael J. and Patterson, David A. and Lanham, Nick and Trushkowsky, Beth and Trutna, Jesse},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807320},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807320},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
90 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00034951786
11,867 Block as a Value for SQL over NoSQL 2019 VLDB 5.093636e-05
11,990 BEAS: Bounded Evaluation of SQL Queries 2017 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,635 SCADS: Scale-Independent Storage for Social Computing Applications 2009 CIDR 0.00010158805
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