Random Sampling over Joins Revisited
Joins are expensive, especially on large data and/or multiple relations. One promising approach in mitigating their high costs is to just return a simple random sample of the full join results, which is sufficient for many tasks. Indeed, in as early as 1999, Chaudhuri et al. posed the problem of sampling over joins as a fundamental challenge in large database systems. They also pointed out a fundamental barrier for this problem, that the sampling operator cannot be pushed through a join, i.e., sample( R bowtie S )≠ sample( R ) bowtie sample( S ). To overcome this barrier, they used precomputed statistics to guide the sampling process, but only showed how this works for two-relation joins.