基于链接贝叶斯网络的属性值相似性选择性估计
Selectivity Estimation with Attribute Value Dependencies using Linked Bayesian Networks
摘要：
关系查询优化器依赖于成本模型来在不同的查询执行计划之间进行选择。已知选择性估计是成本模型的关键输入。在实践中，标准的选择性估计程序容易产生大的误差。这主要是因为它们依赖于所谓的属性值独立性和连接一致性假设。因此，已经提出了多维方法来捕获两个或多个属性之间的依赖关系内和跨关系。然而，这些方法需要大量的计算成本，这使得它们在实践中无法使用。我们提出了一种基于贝叶斯网络的方法，能够捕获交叉关系的属性值的依赖关系，很少的开销。我们的建议是基于这样的假设，即当涉及连接时，属性之间的依赖关系被保留。此外，我们引入了一个参数之间的交易估计精度和计算成本。我们验证了我们的工作，通过比较它与其他相关的方法在一个大的工作量来自JOB和TPC-DS基准。我们的研究结果表明，我们的方法是一个数量级比现有的方法更有效，同时保持高水平的准确性。
英文摘要：
Relational query optimisers rely on cost models to choose between different query execution plans. Selectivity estimates are known to be a crucial input to the cost model. In practice, standard selectivity estimation procedures are prone to large errors. This is mostly because they rely on the so-called attribute value independence and join uniformity assumptions. Therefore, multidimensional methods have been proposed to capture dependencies between two or more attributes both within and across relations. However, these methods require a large computational cost which makes them unusable in practice. We propose a method based on Bayesian networks that is able to capture cross-relation attribute value dependencies with little overhead. Our proposal is based on the assumption that dependencies between attributes are preserved when joins are involved. Furthermore, we introduce a parameter for trading between estimation accuracy and computational cost. We validate our work by comparing it with other relevant methods on a large workload derived from the JOB and TPC-DS benchmarks. Our results show that our method is an order of magnitude more efficient than existing methods, whilst maintaining a high level of accuracy.
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