Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
WinMagic: subquery elimination using window aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
Cost-based query transformation in Oracle
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Handling data skew in parallel joins in shared-nothing systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Enhanced subquery optimizations in Oracle
Proceedings of the VLDB Endowment
Sort vs. Hash revisited: fast join implementation on modern multi-core CPUs
Proceedings of the VLDB Endowment
Scalable aggregation on multicore processors
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Optimization of analytic window functions
Proceedings of the VLDB Endowment
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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.