A parallel sort-merge-join algorithm which uses a divide-and-conquer approach to address the data skew problem is proposed. The proposed algorithm adds an extra, low-cost scheduling phase to the usual sort, transfer, and join phases. During the scheduling phase, a parallelizable optimization algorithm, using the output of the sort phase, attempts to balance the load across the multiple processors in the subsequent join phase. The algorithm naturally identifies the largest skew elements, and assigns each of them to an optimal number of processors. Assuming a Zipf-like distribution of data skew, the algorithm is demonstrated to achieve very good load balancing for the join phase, and is shown to be very robust relative, among other things, to the degree of data skew and the total number of processors.
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