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Kodiak: Leveraging Materialized Views For Very Low-Latency Analytics Over High-Dimensional Web-Scale Data

Summary: Kodiak delivers low-latency, high-dimensional analytics over petabyte-scale ad events by incrementally maintaining thousands of partitioned, replicated materialized views and automatically selecting views per query. Production results: 3+ PB, 8 ms median latency, and far lower resource use than big-data systems. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11424
Venue
VLDB
Year
2016
Pagerank
5.664642e-05
Overall Rank
7,238 | 50.35%
DOI
10.14778/3007263.3007270

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liu_vldb16,
        title = {{Kodiak: Leveraging Materialized Views For Very Low-Latency Analytics Over High-Dimensional Web-Scale Data}},
        author = {Liu, Shaosu and Song, Bin and Gangam, Sriharsha and Lo, Lawrence and Elmeleegy, Khaled},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {13},
        pages = {1269--1280},
        doi = {10.14778/3007263.3007270},
        url = {https://doi.org/10.14778/3007263.3007270},
        year = {2016}
}

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