Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this paper is adopted in almost all current database systems. A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost. In the cost model, cardinality, the number of tuples through an operator, plays a crucial role. Due to the inaccuracy in cardinality estimation, errors in cost model, and the huge plan space, the optimizer cannot find the optimal execution plan for a complex query in a reasonable time. In this paper, we first deeply study the causes behind the limitations above. Next, we review the techniques used to improve the quality of the three key components in the cost-based optimizer, cardinality estimation, cost model, and plan enumeration. We also provide our insights on the future directions for each of the above aspects.

中文翻译：

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推进DBMS查询优化器的调查：基数估计，成本模型和计划枚举

查询优化器是数据库系统的核心。本文研究的基于成本的优化器已被几乎所有当前的数据库系统采用。基于成本的优化器引入了计划枚举算法来查找（子）计划，然后使用成本模型来获取该计划的成本，然后选择成本最低的计划。在成本模型中，基数（通过运算符的元组数量）起着至关重要的作用。由于基数估计的不准确性，成本模型的错误以及庞大的计划空间，优化器无法在合理的时间内找到针对复杂查询的最佳执行计划。在本文中，我们首先深入研究上述局限性的原因。接下来，我们将介绍用于提高基于成本的优化器，基数估计，成本模型，和计划枚举。我们还将就上述各个方面的未来方向提供见解。