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Adaptive and Big Data Scale Parallel Execution in Oracle

Summary: Adaptive, multi-stage parallel execution in Oracle scales joins, group-by, rollup/cube, and window functions. Runtime stats guide parallelization and distribution, tolerating optimizer misestimates and boosting analytics on parallel DBs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10548
Venue
VLDB
Year
2013
Pagerank
7.6991391e-05
Overall Rank
3,021 | 78.99%
DOI
-

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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
11 Implementing Data Cubes Efficiently 1996 SIGMOD 0.0011708144
247 On the Computation of Multidimensional Aggregates 1996 VLDB 0.00030927763
351 Sort vs. Hash Revisited: Fast Join Implementation on Modern Multi-Core CPUs 2009 VLDB 0.0002636504
1,869 WinMagic : Subquery Elimination Using Window Aggregation 2003 SIGMOD 0.00010265836
1,915 Handling Data Skew in Parallel Joins in Shared-Nothing Systems 2008 SIGMOD 0.00010104123
2,504 Enhanced Subquery Optimizations in Oracle 2009 VLDB 8.6351917e-05
4,335 Optimization of Analytic Window Functions 2012 VLDB 6.2790346e-05
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