SQL Server 2008的公用表表达式(CTE)详解

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在《公用表表达式:计算机实验设计与分析第二版》一书中,第7.14节专门探讨了Microsoft SQL Server 2008中的一个重要特性——公用表表达式(CTE,Common Table Expression)。CTE是一种在SQL查询语句中创建的临时结果集,它可以在SELECT、INSERT、UPDATE或DELETE语句中定义并被多次引用,甚至可以嵌套使用,形成递归查询。CTE的主要优势在于其灵活性和复用性,能够简化复杂的查询逻辑,使得代码更为清晰。 在SQL Server 2008中,CTE的引入是为了提高数据库查询的效率和可读性。它们允许在查询执行过程中临时定义和操作数据,而无需直接将结果集存储在表中,这样可以减少资源消耗,并且可以在同一个查询中处理多个步骤的操作。例如,可以使用CTE逐步分解复杂查询,先计算中间结果,再基于这些结果生成最终结果,从而避免了重复数据计算。 此外,CTE还支持视图的功能,允许在定义视图时使用它们,使得视图的创建更为灵活。在设计和分析计算机实验或者进行数据分析时,CTE尤其有用,因为它可以用来模拟实验步骤,组织数据流,或者构建递归查询以处理层次结构数据。 然而,虽然CTE提供了强大的功能,但使用时也需要谨慎,确保查询优化,避免不必要的表扫描。理解并掌握如何恰当地运用CTE,可以显著提升SQL Server 2008中复杂查询的编写和管理能力,有助于数据库管理员和开发人员更高效地处理大规模数据。 公用表表达式是SQL Server 2008数据库管理系统中的一个关键特性,它在提高查询效率、增强代码可读性和管理复杂查询方面发挥着重要作用,是数据库设计和分析过程中不可或缺的一部分。

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

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