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<p>Parallel processing is an attractive option for relational database systems. As in any parallel environment however, load balancing is a critical issue which affects overall performance. Load balancing for one common database operation in particular, the join of two relations, can be severely hampered for conventional parallel algorithms, due to a natural phenomenon known as data skew. In a pair of recent papers (J. Wolf et al., 1993; 1993), we described two new join algorithms designed to address the data skew problem. We propose significant improvements to both algorithms, increasing their effectiveness while simultaneously decreasing their execution times. The paper then focuses on the comparative performance of the improved algorithms and their more conventional counterparts. The new algorithms outperform their more conventional counterparts in the presence of just about any skew at all, dramatically so in cases of high skew.</p>
relational databases; relational algebra; resource allocation; parallel algorithms; parallel programming; relational database joins; data skew; parallel processing; relational database systems; parallel environment; load balancing; common database operation; parallel algorithms; join algorithms; comparative performance

P. Yu, J. Wolf, J. Turek and D. Dias, "New Algorithms for Parallelizing Relational Database Joins in the Presence of Data Skew," in IEEE Transactions on Knowledge & Data Engineering, vol. 6, no. , pp. 990-997, 1994.
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