Building heavy hitter summary for query optimization

Constructing a heavy hitter summary for query optimization. The heavy hitter summary is constructed by sampling each of multiple partitions of a dataset using a uniformed sampling rate. For each partition, performing a two-stage heavy hitter estimation process to determine whether an estimated frequ...

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Bibliographic Details
Main Authors Kwon, Yongchul, Le, Wangchao, Friedman, Marc Todd
Format Patent
LanguageEnglish
Published 28.07.2020
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Constructing a heavy hitter summary for query optimization. The heavy hitter summary is constructed by sampling each of multiple partitions of a dataset using a uniformed sampling rate. For each partition, performing a two-stage heavy hitter estimation process to determine whether an estimated frequency of a key of the sampled data units may be included in a partition-level heavy hitter summary. Constructing a partition-level heavy hitter summary for each partition of the dataset based on the keys determined via the two-stage process, and constructing a dataset-level heavy hitter summary based on the partition-level heavy hitter summary. The dataset-level heavy hitter summary may be used to optimize query trees.
Bibliography:Application Number: US201715716202