我们在上面的代码中使用了一个预编译语句，以使得能够更容易地在查询中填充相关的数据。
We've used a prepared statement in the above to make the process of filling in the data much easier within the query.
版本2在以前提供的查询能力的基础上添加了一些强大的功能来按更复杂的要求进行查询。
Version 2 builds upon the inquiry capabilities previously provided by adding several powerful features dealing with more complex query requirements.
惟一的例外是，具有较少写活动和不可预测的查询工作负载的应用程序，这种应用程序难于定义更明确的索引。
The only exception could be applications with low write activity and unpredictable query workload such that more specific indexes are hard to define.
其缺点是在某些情况下，数据要冗余，查询模型也自然更复杂一些。
The downside is that in some cases data is duplicated and the query model arguably is made more complex.
查询编译器将视图定义展开成主语句块，从而产生一个更复杂的语句。
The query compiler expands view definitions into the main statement block, which might result in a more complex statement.
通过扩展和收缩复杂SQL的一部分，来深入获取更详细的查询部分，例如引用视图和子查询。
Drill into parts of the query in more detail, such as referenced views and sub-queries, by expanding and collapsing sections of a complex SQL.
通过路径表达式可以更简单地表达相同的查询
The same query can be expressed somewhat more concisely as a path expression, as shown in Listing 15.
改进的Galaxy查询语言：支持更复杂的查询。
Improved Galaxy Query Language - supporting more sophisticated queries.
对有问题的查询进行格式化，这样可以更容易阅读和理解查询逻辑。
Format the problem query to make it easier to read and comprehend the logic of the query.
虽然这个查询只是返回标题、名称和URL的列表，但是更复杂的查询可以从匹配项目提取出所有数据。
While this query simply returns a list of titles, names, and URLs, a more complex query could extract all of the data present in the matched items.
在上例中，使用了编号的参数，但是也可以使用指定参数以进行更复杂的查询。
In the above example, we used numbered parameters, but you can also use named parameters for more complicated queries.
更确切地说，使用者只需查询uddi一次，然后就可以安全地缓存代理的地址，并且重复地使用它来调用服务。
Rather, the consumer only needs to query UDDI once and then can safely cache the proxy's address and use it to invoke the service repeatedly.
如果这样一个查询(或更复杂的查询)要重复几百(或上千)次，那么这两种方法之间的差别将变得非常明显。
If such a query (or a more complex one) were to be repeated a few hundred (or thousand) times, the difference between the two approaches would start to become quite drastic.
图4展示了一个更类似的场景——事务中的第二个查询需要访问与第一个查询相同的节点上的数据。
Figure 4 shows a more likely scenario — that the second query in the transaction needs to access data on the same node as the first query.
如果解决方案开发人员需要更复杂的定制查询，那么可以使用RFIDIC中的解决方案查询机制。
If the solution developer needs more complex and customized queries, he can use the solution query mechanism in RFIDIC.
可以组合这些表示维、边界和数据查询的基本查询类型，以建模更复杂的情况。
You can combine these basic query types, which represent the dimension, edge, and data queries described here, to model more complex situations.
更复杂的测试场景(其中可能包含多个未完成任务，每个任务需要使用不同的数据完成)将需要使用查询字符串。
More complex test scenarios, where there might be several outstanding tasks, each needing completion with different data, will require the use of a query string.
如果您想要具有一个扩展的PHP应用程序，那么必须有效地使用数据库，这意味着更智能的查询。
If you want to have a PHP application that scales, you must make efficient use of the database, and that means smarter queries.
当通过在查询中设置更多谓词来提供更详细的搜索条件时，优化器就有机会作出更好的选择。
When you provide more detailed search conditions by having more predicates in a query, the optimizer has a chance to make better choices.
这是用RDF表示查询的常用方法，尽管当查询变得更复杂时，它会显得比较笨拙。
This is a common approach to representing queries in RDF, although it gets unwieldy as queries get more complex.
因为订单在变化，所以在每次请求的时候查询它并检查它的状态会更容易。
Because the order is changing, it's easier to simply look it up and check it's status each request.
再考虑一个更真实的性能场景：生成市场营销报告时，要执行一系列XML查询。
A more realistic performance scenario might involve some of the XML queries that stand behind the marketing reports.
此外，还包括一些更复杂的查询，包括在每家公司中哪些技能会随时间呈增长趋势。
In addition to this one, there are more sophisticated queries possible, including which skills have an increasing trend over time, separated by company.
如果要执行更复杂的查询，或者要连续执行多个查询，那么执行时间的差距就不是次秒级的，而是以秒甚至分钟来计算的。
If more complex queries are executed, or if several queries are executed in sequence, the difference in the execution time is not just sub-seconds but seconds or even minutes.
在这一节中，我将介绍更多SPARQL的特性，以及它们支持的更复杂的查询。
In this section, I'll introduce more of SPARQL's features, and the more complex queries they enable.
要对数据库执行更复杂的查询，必须定义元数据过滤器。
To perform more complex queries against the database, you must define filters against the metadata.
这篇文章描述了关于这个过程更详细的细节，同时还阐述了如何归还到默认的区分大小写的查询中来。
This article describes this process in more detail and also explains how to revert to the default case-sensitive queries.
应该明确的是可更容易地结合前面两个查询来产生所需的信息。
It should be clear that the two preceding queries can be easily combined to produce the necessary information.
操作板会以这种方式显示多个查询，但是报表提供了更充分的灵活性。
Dashboards can show multiple queries in this way, but the report offers greater flexibility.
例如，某一列被访问的频率超过预期；某个表远远大于预期；某些查询比预期执行得更频繁。
For example, a column is being accessed more frequently than anticipated; a table is much larger than anticipated; certain queries are being performed more frequently.
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