Addressing the portal resolver framework directly.

直接访问门户分解器框架。

In Listing 5, the code using this resolver USES the new prefix.

在清单5中，使用此解析器的代码还使用了新的前缀。

The resolver, in order to be more re-usable, adds the following

为了更加可重用，这个解析器添加了以下内容

Next, the application specifies an appropriate collection resolver.

接下来，应用程序指定一个适当的集合解析器。

A note about using a generic JDBC Resolver versus other approaches.

使用一个通用JDBC解析器与使用其他方法的对比。

Defines a view resolver using the bean name that the user specifies.

使用用户指定的bean名称定义一个视图解析器。

The "server" side of this resolver solution is called an LSID authority.

这一解析器解决方案的“服务器”端名为LSID中心(LSID authority)。

You can register your entity resolver on your parser as shown in Listing 4.

您可以将实体解析器注册在分析器上，如“清单4”中所示。

The wrapped value is now available to the JSF variable resolver, and thus the view.

现在 JSF 变量解析器和视图就可以使用包装的值。

Write your entity resolver so it caches the content of the entity the first time it is read.

编写实体分解器，使它在实体第一次被读取的时候缓存该实体的内容。

Your new action class USES a resolver class that processes the request and builds the Atom feed.

新的action类使用一个resolver类处理请求并构建Atom提要。

Paremus have recently released Nimble, a resolver that can obtain and download OSGi bundles.

近日Paremus发布了Nimble——用于获取并下载osgi bundle的解析器。

In that case a reference to the resolver object is provided that can be passed to other vats.

此时，将提供对解析器对象的引用，并传递给其他vat。

The Spring resolver first delegates to the native resolver supplied with the JSF implementation.

首先，Spring 解析器委托给 JSF 实现附带的本地解析器。

We also use an appropriate resolver to execute the query, much as we did in the previous scenario.

我们也使用了一个适当的解析器来执行查询，就跟我们在前一场景中所做的一样。

Finally King covers scopes, contexts, and resolver methods. With available scope types as follows.

最后，King论及了作用域、上下文和解析器方法。

The native JSF variable resolver first looks for a JSF managed bean that matches the name courses.

本地J SF变量解析器首先查找与名称courses相匹配的jsf托管bean。

The resolver was also a very challenging part, but from the pure complexity inherent to the problem.

从纯粹的内在复杂性来讲，解析器也极具挑战。

To achieve true integration with Spring, you need a better solution than a custom variable resolver.

要获得与Spring的真正集成，需要比定制变量解析器更好的解决方案。

In Listing 7, you can see where we instantiate this resolver after defining some basic named queries.

在清单7中，我们在定义了一些基本的已命名查询后实例化这个解析器。

This resolver, like the rest of the sample code discussed in this article, is available for download.

此解析器跟本文中讨论的其他样例代码一样，可以下载得到。

If the FacesContext isn't available, then the variable resolver can't access any of the Spring beans.

如果FacesContext不可用，则变量解析器就不能访问任何的Spring bean。

The sample's result resolver will then resolve this statement to a named query which we predefined as.

这个样例的结果解析器然后将这个语句解析为一个我们预先定义的已命名查询。

As in our previous scenarios, this example also relies on a resolver to execute the database operation.

跟我们的前一场景一样，本例也依赖于解析器来执行数据库操作。

The trouble is, the Spring variable resolver relies on the FacesContext to locate the Spring container.

其中的问题是，Spring 变量解析器依赖于 FacesContext 定位 Spring 容器。

We also pass a database connection to the resolver, so the resolver can work against any JDBC connection.

我们还传递了一个数据库连接到该解析器，以便它能够针对任何JD BC连接而工作。

This resolver is basically the same as the simpler sample shown earlier of reading XML data through JDBC 4.0.

从根本上说，这个解析器与此前展示的那个使用JDBC 4.0读取XML数据的简单示例相同。

With the same results resolver approach, you can direct an XQuery 1.0 program to write output to an XML database.

使用相同的结果解析器方法，可以指示一个XQuery 1.0程序将输出写入一个XML数据库。

The search parameter provides a list of domains to the resolver to use when resolving an IP address or host name.

search参数为解析程序提供解析ip地址或主机名时使用的域的列表。

Using the more general collection and results resolver approach handles most cases in a natural and consistent way.

使用更常规的集合和结果解析器能够以一种自然统一的方式处理大多数情况。