Skew‐aware online aggregation over joins through guided sampling

Abstract

Online aggregation is a query processing technique that returns approximate answers with error guarantees (in the form of confidence intervals) continuously during the query execution process.This approach offers users a suitable tradeoff between query efficiency and accuracy. The key issue of online aggregation is how to ensure a random sample collection's efficiency and effectiveness. However, the often-used "blind" sampling method does not adequately consider dataset statistics and other useful information, leading to inefficient sampling and poor sample quality.This becomes a glaring performance issue for skewed data distribution over joins. To alleviate this problem, we utilize dataset statistics to propose a new "guided" sampling approach, which consists of a logic-partition-based weighted Gaussian sampling method tailored for the skewed join key, as well as a two-level sample allocation method that applies to the skewed measured value.Extensive experiments using the TPC-H benchmark for skewed data distribution demonstrate our solution's superior performance.

Advances in cloud computing and big data analytics

“…This becomes a glaring performance issue for skewed data distribution over joins. To alleviate this problem, Wang et al utilize data set statistics to propose a new “guided” sampling approach, which consists of a logic‐partition‐based weighted Gaussian sampling method tailored for the skewed join key, as well as a two‐level sample allocation method that applies to the skewed measured value …”