Exploiting Interactions among Query Rewrite Rules in the Teradata DBMS

Ghazal, Ahmad; Seid, Dawit Yimam; Crolotte, Alain; Mckenna, Bill

doi:10.1007/978-3-540-85654-2_50

Cited by 5 publications

(1 citation statement)

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“…Query optimization in Teradata is rulebased and cost-based. Rule-based optimizations are normally in the form of query rewrites and are performed by the Query Rewrite subsystem [11]. Examples of query rewrites include projection pushdown, predicate pushdown, join elimination, outer-to-inner join conversion, set operation branch elimination, and view folding.…”

Section: Figure 4: Teradata Shared-nothing Architecturementioning

confidence: 99%

Hybrid row-column partitioning in teradata _®

Al-Kateb

Sinclair

et al. 2016

Proc. VLDB Endow.

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Data partitioning is an indispensable ingredient of database systems due to the performance improvement it can bring to any given mixed workload. Data can be partitioned horizontally or vertically. While some commercial proprietary and open source database systems have one flavor or mixed flavors of these partitioning forms, Teradata Database offers a unique hybrid row-column store solution that seamlessly combines both of these partitioning schemes. The key feature of this hybrid solution is that either row, column, or combined partitions are all stored and handled in the same way internally by the underlying file system storage layer. In this paper, we present the main characteristics and explain the implementation approach of Teradata's row-column store. We also discuss query optimization techniques applicable specifically to partitioned tables. Furthermore, we present a performance study that demonstrates how different partitioning options impact the performance of various queries.

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Section: Figure 4: Teradata Shared-nothing Architecturementioning

confidence: 99%