This example in Listing 11 parses a large XML document with SAX.
清单11中的例子使用SAX解析大型XML文档。
This example in Listing 10 parses a large XML document with XMLReader.
清单10中的例子使用XMLReader解析大型XML文档。
When you export an entire project, you typically end up with a rather large XML document.
当您导出一个完整项目时，您通常会得到一个相当大的XML文档。
Remember however, that flexibility comes at a price if you load a large XML document into memory all at once.
但是要记住，如果一次将整个大型XML文档载入内存，则取得灵活性的代价相当高昂。
Remember, however, that flexibility comes at a price if you load a large XML document into memory all at once.
但是要记住，如果要把很大的XML文档一次加载到内存中，则取得灵活性的代价相当高昂。
Remember however, that, like the DOM, SimpleXML comes with a price for its ease and flexibility if you load a large XML document into memory.
但是要记住，和DOM一样，SimpleXML的易用性和灵活性的代价也是无法向内存中加载大型XML文档。
XQuery is a high-level, strongly-typed, functional language (free of side-effects) that is ideal to express a query to obtain data both from an XML document and a large XML document repository.
XQuery是一种高端的、强类型的函数语言(没有副作用)，非常适合表达从XML文档或者大型XML存储库(repository)中获取数据的查询。
If an XML document is too large to fit on a single page in a table space, DB2 splits the document into multiple regions which are then stored on multiple pages.
如果XML文档太大，一个表空间中的一个页面容纳不下，那么DB 2就将该文档划分成多个区域，然后将它们分开存储到多个页面上。
Furthermore, native XML support in DB2 provides efficient access to specific portions (XML nodes) of a document; large objects do not.
此外，DB 2中的原生xML支持提供对文档特定部分(xml节点)的高效访问；大对象不可以。
This table has a column that also contains the intact XML document, as a character large object (CLOB).
这个表中的一列还包含原样的XML文档，采用字符大对象(CLOB)形式。
They offer two fundamental database design options — storing each XML document intact as a single large object or "shredding" it into multiple columns often across multiple tables.
它们常常提供两种基本的数据库设计选项——将每个XML文档完整地存储为一个大型对象，或者将它“撕开”，分散存储在多个表的多个列中。
Then it introduced you to SAX and event-based parsing, the approach you must take if your XML document is very large or is an unending stream.
然后介绍了SAX和基于事件的解析，如果XML文档非常大或者是一个无终止的流，必须采用这种方法。
We found a bigger difference for queries that touch a large number of rows, such as a table scan over all 20,000 XML documents with string comparison on every document.
我们发现涉及大量行的查询的性能差异比较大，比如对所有20,000个XML文档进行全表扫描并对每个文档比较字符串。
However, when composing XML documents, it is common that large datasets need to be joined and restructured to match the desired document structure.
但是在编写XML文档时，通常要联接和重构大型数据集，以匹配所需的文档结构。
If you are processing a large XML data tree, keep in mind that SimpleXML loads the full XML document tree into memory before processing.
如果您处理的是一个大型的XML数据树，那么请务必注意SimpleXML在处理前是需要将整个XML文档加载到内存中的。
Hence, validation can entail a large performance cost if a validating processor needs to parse and build an in-memory representation of a schema before validating each XML document.
因此，如果在验证每个XML文档之前，都要构建模式的内存中表示，验证可能造成很大的性能代价。
The parser makes a large number of checks on each aspect of the document, but the parser can only make these checks after it does extensive processing of the documents that comprise the XML Schema.
解析器要对文档的各个方面做大量检查，但是只有对组成XMLSchema的文档进行广泛处理后，解析器才能进行这些检查。
As you can see in Listing 8, XMLUnit, in concert with XPath, provides a handy mechanism for validating precise aspects of an XML document rather than doing a large difference test.
如清单8所示，XMLUnit和XPath一起协作提供了可以准确验证XML文档的一种便捷机制，而不是进行大规模的差异测试。
If your document is large, but you only need part of the document, then the XML pull parser might be a more efficient way to go.
如果您的文档比较大，但您只需要文档的一部分，则XMLPull解析器可能是更为有效的方法。
The large weblog.xml document used for benchmark tests contains about 8,500 elements, each having the same collection of child fields — a typical arrangement for a data-oriented XML document.
用于基准测试的大型文档weblog . xml包含大约8,500个元素，每个元素都含有相同的子字段集合-这是一个面向数据的xml文档的典型布局。
The large weblog.xml document used for benchmark tests contains about 8,500 elements, each having the same collection of child fields — a typical arrangement for a data-oriented XML document.
用于基准测试的大型文档weblog . xml包含大约8,500个元素，每个元素都含有相同的子字段集合-这是一个面向数据的xml文档的典型布局。
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