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.
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