Handling data skew in parallel hash join computation using two-phase scheduling

A large number of parallel join algorithms has been proposed to maintain load-balancing in the presence of data skew. However, one important type of data skew-join product skew (JPS)-has been little studied. In this paper, a dynamic parallel join algorithm, which employs a two-phase scheduling procedure, is designed to handle the JPS problem. Two sets of scheduling heuristics are studied against various parameters. It is shown that many of the existing algorithms can be regarded as a special case of our algorithm, whose cost is based on the nature of data skew. While it can cope with JPS which other algorithms cannot approach, it can be as efficient as most existing algorithms when JPS does not exist