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Efficient join processing plays an important role in big data analysis. In this work, we focus on generic theta joins in a massively parallel environment, such as MapReduce and Spark. Theta joins are notoriously slow due to their inherent quadratic complexity, even when their selectivity is low, e.g., 1%. The main performance bottleneck differs between cases, and is due to any of the following factors...
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