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A distributed join is a fundamental operation for processing massive datasets in parallel. Unfortunately, computing an equi-join over such datasets is very resource-intensive, even when done in parallel. Given this cost, the equi-join operator becomes a natural candidate for optimization using approximation techniques, which allow users to trade accuracy for latency. Finding the right approximation technique for joins, however, is a challenging task. Sampling, in particular, cannot be directly used in joins; naïvely performing a join over a sample of the dataset will not preserve statistical properties of the query result.
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Quoc et al. (2018) studied this question.
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