集合查询计划中引用的所有表。
该查询计划展示了函数索引的使用。
使用SQL指令实现查询计划。
该查询计划显示了全表扫描。
该查询计划显示索引扫描使用了函数索引。
This query plan shows an index scan using our functional index.
设计新的查询评估和查询计划变动的范例。
Devising new paradigms for query evaluation and query plan migration.
静态查询已经经过解析，并生成了查询计划。
Static queries have already been parsed, and had a query plan generated.
一个查询计划是一个操作符序列。
估算成本是优化器用来比较查询计划的成本单元。
The estimated costs are cost units that the optimizer uses to compare query plans.
当执行静态查询时，会使用以前生成的查询计划。
When a static query is executed, the previously computed query plan is used.
因此，在进行查询计划优化时，就需要使用统计视图。
Hence, the need for statistical views arises in query plan optimization.
生成的查询计划基于数据，而不是推断，因此非常高效。
The resulting query plans are based on data, rather than heuristics, and are thus extremely efficient.
当优化器创建查询计划时，它将使用下列系统目录信息。
The optimizer USES the following system catalog information as it creates a query plan.
根据系统的工作负载和查询，这将改善查询计划并改进系统性能。
Depending on the workload and queries on the system, this can improve the query plans and help improve system performance.
在执行实际的连接操作之前，优化器使用统计数据制定一个查询计划。
The optimizer would chalk out a query plan before performing the actual join operation itself by making use of the statistical data.
值ANY允许优化器决定一个给定查询计划的 ATQ的数量。
The value ANY allows the optimizer to determine the number of ATQs for a given query plan.
数据库服务器可以在保存视图后立即为视图建立查询计划。
A database server can establish a query plan for a view as soon as the view is saved.
如这个查询计划所示，不止一个散列表的键可以被下推来过滤事实表的行。
As this query plan shows, keys from more than one hash table build can be pushed down to filter rows of the fact table.
随后，我们可以查看图1，其中展示了这个查询计划的等效符号树表示形式。
We can then examine the equivalent symbolic tree representation of this query plan in Figure 1.
当我们查看一下查询计划的时候，我们看到有50- 100k的读操作。
When I look at the query plans for these, I'm seeing 50-100k reads.
setexplainon 指示IDS生成显示查询计划的文件。
set explain on directs IDS to generate a file that shows the query plan.
为了从这个复杂的sql语句中赚取性能，DB 2选择一个非常精妙的查询计划。
In order to realize performance gains from this complicated SQL statement DB2 selects a very sophisticated query plan.
从概念上来说，关系数据行(元组)按照自下向上的方式流过查询计划中的操作节点。
Conceptually, rows of relational data (tuples) flow through the operation nodes in the query plan from bottom to top.
优化器将选择该查询计划，因为其执行的估算成本是所有被估计划中最低的。
The optimizer chose this query plan because the estimated cost for its execution was the lowest among all the evaluated plans.
优化器完成工作后，编译器将把查询计划转换为可执行的代码段，称为snippet。
After the optimizer has done its work, the compiler converts the query plan into executable code segments called snippets.
以下示例中显示的查询计划信息来自这个查询的 sqexplain.out文件。
The query plan information shown in the following examples is taken from the sqexplain.out file for this query.
此命令更新了系统目录中的统计数据，优化程序使用这些数据来确定成本最低的查询计划。
This command updates the statistics in the system catalogs that the optimizer USES to determine the lowest-cost query plan.
为了确保优化器选择最佳的查询计划，使内部统计数据保持为最新并且最准确的十分重要。
To ensure that the optimizer selects the best query plan, it is important to keep internal statistics current and accurate.
理想情况下，查询计划是执行给定查询的最佳计划—也就是说，它会确定抓取数据的最佳方式。
A query plan ideally is an optimal plan to execute the given query - that is, it determines the most efficient way to fetch the data.
接下来将深入讨论如何创建和使用函数索引，以及如何生成和检验查询计划，并提供具体的示例。
The in-depth discussion and examples that follow explore creating and using functional indexes and how to generate and examine query plans.
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