当计划查询时，查询编译器使用这些特征和约束。
The query compiler uses these characteristics and restrictions when planning the query.
查询计划可能会更改。
集合查询计划中引用的所有表。
查找一个查询计划中的顶级连接节点。
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.
该查询计划显示了全表扫描。
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.
该查询计划显示了一个全表扫描。
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.
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.
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.
该查询计划展示了函数索引的使用。
分析查询访问计划，从而可以切实看到优化器对访问数据作出的选择。
Analyze the query access plan to visualize the choices the optimizer makes in accessing the data.
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.
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.
下推的决定是包装器在计划阶段与查询优化器一起做出的。
The push-down decision is made by the wrapper during the planning phase, while working with the query optimizer.
从版本9.3开始，该实用程序得到了较好的提高；您可以获取详细的查询执行计划，而不必执行该查询。
From version 9.3 on, there is a nice enhancement of this utility; you can get the detailed query execution plan without ever executing the query.
因此，我们在这些连接列上添加了索引，从下列查询执行计划中我们可以看到，查询成本减少了很多。
So we added indexes on those joining columns and from the following query execution plan, we can see the query cost is greatly reduced.
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.
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.
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.
使用EXPLAIN命令查看查询的解释计划。
为确保优化器选择的查询计划能够最好地反映表的当前状态，应定期运行UPDATESTATISTICS。
To ensure that the optimizer selects a query plan that best reflects the current state of your tables, run UPDATE STATISTICS at regular intervals.
使用SQL指令实现查询计划。
DB 2优化器使用这些统计信息为任何给定的查询决定最有效的访问计划，从而提高查询的性能。
The DB2 optimizer USES these statistics to determine the most efficient access plan for any given query, which improves query performance.
在为查询生成计划的过程中，查询优化器与非关系包装器通信。
During plan generation for the query, the query optimizer communicates with the non-relational wrapper.
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.
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.
首先选择一组您想要影响其访问计划的查询。
First choose a set of queries whose access plans you have decided to influence.
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