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 ▷◁ S ) , sample(R) ▷◁ 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. This paper revisits this classic problem for both acyclic and cyclic multi-way joins. We build upon the idea of Chaudhuri et al., but extend it in several nontrivial directions. First, we propose a general framework for random sampling over multi-way joins, which includes the algorithm of Chaudhuri et al. as a special case. Second, we explore several ways to instantiate this framework, depending on what prior information is available about the underlying data, and offer different tradeoffs between sample generation latency and throughput. We analyze the properties of different instantiations and evaluate them against the baseline methods; the results clearly demonstrate the superiority of our new techniques.