International Business Machines Corporation

Ou Jin

Terence Patrick Purcell

Hong Min

Yefim Shuf

You-Chin Gene Fuh

Fen-Ling Lin

Michael A Chang

Patrick Dooling Bossman

Brian Thinh-Vinh Tran

Shuanglin Guo

Brian L Baggett

Chunfeng Pei

Provided are techniques for a Data-Partitioned Secondary Index (DPSI) partition level join. While using a Data-Partitioned Secondary Index (DPSI) to perform a join of an outer table and an inner table, a different task from multiple tasks is assigned to each partition of the inner table. With each task, a join is performed of the outer table and the assigned partition of the inner table using the DPSI to generate results. The results from each different task are merged.

Data-partitioned secondary index (DPSI) partition level join

A database query is partitioned into an initial partition including a plurality of parallel groups, and is executed, via an execution plan, based on the initial partition. A sampling subset of data is identified from the plurality of parallel groups. Substantially in parallel with the executing of the query, the execution plan is executed on the sampling subset of data as a sampling thread. The execution plan is modified based on feedback from the execution of the execution plan on the sampling subset of data.

Method, apparatus, and computer program product for adaptive query parallelism partitioning with look-ahead probing and feedback

Techniques for partitioning a query are provided. The techniques include establishing one or more criterion for partitioning a query, wherein the query comprises one or more tables, materializing a first of the one or more tables, partitioning the first of the one or more tables until the one or more criterion have been satisfied, and partitioning and joining a remainder of the one or more tables of the query.

Method for partitioning a query

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Morgan Hill, CA, United States of America

Ou Jin