Adaptive and Big Data Scale Parallel Execution in Oracle

被引：20

作者：

Bellamkonda, Srikanth ^{[1
]}

Li, Hua-Gang ^{[1
]}

Jagtap, Unmesh ^{[1
]}

Zhu, Yali ^{[1
]}

Liang, Vince ^{[2
]}

Cruanes, Thierry ^{[2
]}

机构：

[1] Oracle USA, 500 Oracle Pkwy, Redwood Shores, CA 94065 USA

[2] Oracle, Redwood Shores, CA USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2013年 / 6卷 / 11期

关键词：

D O I：

10.14778/2536222.2536235

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper showcases some of the newly introduced parallel execution methods in Oracle RDBMS. These methods provide highly scalable and adaptive evaluation for the most commonly used SQL operations - joins, group-by, rollup/cube, grouping sets, and window functions. The novelty of these techniques is their use of multi-stage parallelization models, accommodation of optimizer mistakes, and the runtime parallelization and data distribution decisions. These parallel plans adapt based on the statistics gathered on the real data at query execution time. We realized enormous performance gains from these adaptive parallelization techniques. The paper also discusses our approach to parallelize queries with operations that are inherently serial. We believe all these techniques will make their way into big data analytics and other massively parallel database systems.

引用

页码：1102 / 1113

页数：12