Aggregation and duplicate removal are common in SQL queries. However, in the parallel query processing literature, aggregate processing has received surprisingly little attention; furthermore, for each of the traditional parallel aggregation algorithms, there is a range of grouping selectivities where the algorithm performs poorly. In this work, we propose new algorithms that dynamically adapt, at query evaluation time, in response to observed grouping selectivities. Performance analysis via analytical modeling and an implementation on a workstation-cluster shows that the proposed algorithms are able to perform well for all grouping selectivities. Finally, we study the effect of data skew and show that for certain data sets the proposed algorithms can even outperform the best of traditional approaches.

In this paper, we show that shared virtual memory, in a shared-nothing multiprocessor, facilitates the design and implementation of parallel join processing algorithms that perform significantly better in the presence of skew than previously proposed parallel join processing algorithms. We propose two variants of an algorithm for parallel join processing using shared virtual memory, and perform a detailed simulation to investigate their performance. The algorithm is unique in that it employs both the shared virtual memory paradigm and the message-passing paradigm used by current shared-nothing parallel database systems. The implementation of the algorithm requires few modifications to existing shared-nothing parallel database systems.