论文标题
通过在线机器学习进行选择性纠正
Selectivity correction with online machine learning
论文作者
论文摘要
计算机系统充满了启发他们做出决定的启发式规则。这些经验法则的平均效果良好，但忽略有关可用上下文的特定信息，因此是最佳的。系统学习的新兴领域试图通过机器学习算法学习决策规则。在数据库社区中，已经提出了许多最新的建议，以通过批处理机器学习方法提高选择性估计。这样的方法都是所有需要重新培训并且无法处理概念漂移的批处理方法，例如工作负载更改和模式修改。我们将在线机器学习作为另一种方法。在线模型即时学习，不需要存储数据，它们比批处理模型更轻巧，最后可能适应概念漂移。作为一个实验，我们教导模型来改善PostgreSQL成本模型进行的选择性估计。我们的实验表明，简单的在线模型能够与最近提出的深度学习方法竞争。
Computer systems are full of heuristic rules which drive the decisions they make. These rules of thumb are designed to work well on average, but ignore specific information about the available context, and are thus sub-optimal. The emerging field of machine learning for systems attempts to learn decision rules with machine learning algorithms. In the database community, many recent proposals have been made to improve selectivity estimation with batch machine learning methods. Such methods are all batch methods which require retraining and cannot handle concept drift, such as workload changes and schema modifications. We present online machine learning as an alternative approach. Online models learn on the fly and do not require storing data, they are more lightweight than batch models, and finally may adapt to concept drift. As an experiment, we teach models to improve the selectivity estimates made by PostgreSQL's cost model. Our experiments make the case that simple online models are able to compete with a recently proposed deep learning method.
