An entry is added to the system catalog to indicate to the query optimizer that such a remote index exists.
一个条目被添加到系统编目中，以向查询优化器表明存在那样的一个远程索引。
With this feature, the query optimizer can immediately consider the index when determining an access plan.
有了这个特性，查询优化器可以在决定访问计划时立即考虑索引。
Determine whether a field has an index and, if so, whether the query can efficiently use the index.
您要决定字段是否拥有索引，如果是的话，那么查询是否能够有效地使用索引。
Importance of this particular query: the more important the query, the more you might want to tune by index creation.
这种特定查询的重要性：查询越重要，那么您可能就越应该通过创建索引来进行调优。
However, the query parser is not thread safe, so each thread using the index should have its own query parser.
不过怎样，查询刨析器不是线程级安全的，所以每个使用索引的线程都需要有自己的查询刨析器。
Abbreviations must be added to a synonym dictionary to be mapped to the full term for index and query processing.
缩写词必须添加到同义词词典中才能映射到索引和查询处理的完整词汇。
Range producer function to define the search ranges for the spatial index during query execution.
用于定义在查询执行期间空间索引的搜索范围的范围生成器函数。
This query plan shows the use of a functional index.
该查询计划展示了函数索引的使用。
This query plan shows an index scan using our functional index.
该查询计划显示索引扫描使用了函数索引。
To check if a spatial grid index is actually exploited during query time, you should have a look at the access plan.
为检查在查询时是否真正使用了一个空间网格索引，应该查看一下访问计划。
Finally, execute a query that USES the functional index.
最后，执行查询，该查询将使用函数索引。
With the step-by-step analysis on statistics, predicate, and index, the performance of the problem query is improved significantly.
通过对统计数据、谓词和索引的逐步分析，问题查询的性能大幅提升。
The XML index definition is equally or less restrictive than the query predicate (" containment ").
xml索引定义的限制等同于或低于查询谓词的限制(“容纳”)。
The GAE datastore leverages an index for any query issued.
GAE数据存储对发出的任何查询使用索引。
A new feature of the query optimizer supports a new type of index scan, called an index self-join path.
查询优化器的一项新特性支持一种新的索引扫描，即索引自连接路径。
Any query that utilizes the Primary Index column will encounter faster response times.
任何利用主索引列的查询将会遭遇更快速响应时间问题。
The database, on the other hand, is an easy place to make a few simple changes (add an index, change a query slightly) and see tremendous performance improvements.
另一方面，数据库是一个容 易做一些简单改变（添加一个索引，稍稍修改一个查询）并看到巨大性能改善的地方。
Figure 17 shows the index recommendations for the sample query in this article, together with an estimated performance improvement and DASD space requirement.
图17显示了本文中查询样例的索引建议，以及估计的性能提升和DASD空间需求。
Furthermore, if indexes already exist for both columns (one for EMPNO and one for DEPTNO), DB2 can use them both to satisfy this query so creating another index might not be necessary.
而且，如果已经存在关于这两列的索引(一个关于EMPNO，一个关于DEPTNO)，DB 2可以使用它们来满足该查询，因此创建另一个索引也许是没有必要的。
The final query is the query that exploits the extended index.
最后的查询是利用扩展的索引的查询。
GAE requires that all the data columns involved in a query be indexed, and the index can't contain BLOB or text columns.
GAE需要将查询中涉及到的所有数据列编入索引，且该索引不包含BLOB或文本列。
Note: Here is a query for which the optimizer cannot consider using the functional index.
注意：优化器不会对下面这个查询使用函数索引。
The costs used to find a leaf page depends on the size of the index, the filer of the query, and the uniqueness of the index.
用以读取叶子页面的成本取决于索引的大小、查询的筛选器以及索引的惟一性。
Database index is critical to query performance.
数据库索引对于查询性能来说十分关键。
Query Tuner also provides index advice.
QueryTuner还提供索引建议。
Access plan for query accessing a hash index.
访问hash索引的查询的访问计划。
To store, index, and query well-formed XML conforming to specific XML schemas in a uniform way.
以统一的方式存储、索引和查询符合特定XML模式的格式良好的XML。
To store, index, and query XML easily without needing to convert the XML (shred) to relational format.
无需将XML(分解)转换为关系格式，即可轻松存储、索引和查询XML。
As an alternative to shredding, you can simply use a table with a column of type XML to store, index, and query the MIML data.
作为分解的备选方法，可以简单地使用一个包含xml列的表来存储、索引和查询MIML数据。
The IndexSearcher does the actual munging through the index, but it only understands Query objects.
IndexSearcher通过索引munging，但它只理解Query对象。
应用推荐