Approximate Partition Selection for Big-Data Workloads using Summary Statistics

Proc. VLDB Endow., pp. 2606-2619, 2020.

Cited by: 0|Bibtex|Views37|DOI:https://doi.org/10.14778/3407790.3407848
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Abstract:

Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling is inefficient, often requiring reads of many partitions. In this work, we seek to answer queries quickly and approximately by reading a subset of the data p...More

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