我如何学会停止担心，热爱重新优化
How I Learned to Stop Worrying and Love Re-optimization
摘要：
基于成本的查询优化器仍然是分析工作负载的数据库管理系统中最重要的组件之一。尽管现代优化器在通常情况下选择接近最佳性能的计划，但少数查询的速度比它们可能的速度慢一个数量级。在本文中，我们调查为什么仍然是这种情况下，尽管几十年的改进成本模型，计划枚举和基数估计。我们证明了为什么我们认为重新优化机制可能是提高端到端查询性能的最具成本效益的方法。我们发现，即使是一个简单的重新优化方案可以改善许多性能不佳的查询的延迟。我们证明，重新优化将Join Order Benchmark中前20个运行时间最长的查询的端到端延迟提高了27%，实现了完美基数估计的大部分好处。
英文摘要：
Cost-based query optimizers remain one of the most important components of database management systems for analytic workloads. Though modern optimizers select plans close to optimal performance in the common case, a small number of queries are an order of magnitude slower than they could be. In this paper we investigate why this is still the case, despite decades of improvements to cost models, plan enumeration, and cardinality estimation. We demonstrate why we believe that a re-optimization mechanism is likely the most cost-effective way to improve end-to-end query performance. We find that even a simple re-optimization scheme can improve the latency of many poorly performing queries. We demonstrate that re-optimization improves the end-to-end latency of the top 20 longest running queries in the Join Order Benchmark by 27%, realizing most of the benefit of perfect cardinality estimation.
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