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Supporting sampling in the presence of joins is an important problem in dataanalysis, but is inherently challenging due to the need to avoid correlationbetween output tuples. Current solutions provide either correlated ornon-correlated samples. Sampling might not always be feasible in thenon-correlated sampling-based approaches -- the sample size or intermediatedata size might be exceedingly large. On the other hand, a correlated samplemay not be representative of the join. This paper presents a \emph{unified}strategy towards join sampling, while considering sample correlation every stepof the way. We provide two key contributions. First, in the case where a\emph{correlated} sample is \emph{acceptable}, we provide techniques, for alljoin types, to sample base relations so that their join is \emph{as random aspossible}. Second, in the case where a correlated sample is \emph{notacceptable}, we provide enhancements to the state-of-the-art algorithms toreduce their execution time and intermediate data size.

Perfect and Maximum Randomness in Stratified Sampling over Joins