在这里使用哪个 XML 解析器?

问题描述

我正在接收一个 XML 文件作为输入，其大小可以从几 KB 到更多.我正在通过网络获取此文件.我需要根据我的使用提取少量节点，所以大部分文档对我来说毫无用处.我没有记忆偏好，我只需要速度.

I am receving an XML file as an input, whose size can vary from a few KBs to a lot more. I am getting this file over a network. I need to extract a small number of nodes as per my use, so most of the document is pretty useless for me. I have no memory preferences, I just need speed.

考虑到这一切，我得出结论:

Considering all this, I concluded :

1. 这里不使用 DOM(由于 doc 可能很大，没有 CRUD 要求，并且来源是网络)

1. Not using DOM here (due to possible huge size of doc , no CRUD requirement, and source being network)

没有 SAX，因为我只需要获取一小部分数据.

No SAX as I only need to get a small subset of data.

StaX 可能是一种方法，但我不确定它是否是最快的方法.

StaX can be a way to go, but I am not sure if it is the fastest way.

JAXB 是另一种选择——但它使用什么样的解析器?我读到它默认使用 Xerces(这是什么类型 - 推或拉?)，尽管我可以按照这个 链接p>

JAXB came up as another option - but what sort of parser does it use ? I read it uses Xerces by default (which is what type - push or pull ?), although I can configure it for use with Stax or Woodstock as per this link

我读了很多书，仍然对这么多选项感到困惑！任何帮助将不胜感激.

I am reading a lot, still confused with so many options ! Any help would be appreciated.

谢谢！

我想在这里再添加一个问题:在这里使用 JAXB 有什么问题?

Edit : I want to add one more question here : What is wrong in using JAXB here ?

推荐答案

目前最快的解决方案是 StAX 解析器，特别是因为您只需要 XML 文件的特定子集，并且您可以轻松地忽略任何不需要使用的东西StAX，而如果您使用 SAX 解析器，无论如何您都会收到该事件.

Fastest solution is by far a StAX parser, specially as you only need a specific subset of the XML file and you can easily ignore whatever isn't really necessary using StAX, while you would receive the event anyway if you were using a SAX parser.

但它也比使用 SAX 或 DOM 稍微复杂一些.有一天，我不得不为以下 XML 编写 StAX 解析器:

But it's also a little bit more complicated than using SAX or DOM. One of these days I had to write a StAX parser for the following XML:

<?xml version="1.0"?>
<table>
    <row>
        <column>1</column>
        <column>Nome</column>
        <column>Sobrenome</column>
        <column>email@gmail.com</column>
        <column></column>
        <column>2011-06-22 03:02:14.915</column>
        <column>2011-06-22 03:02:25.953</column>
        <column></column>
        <column></column>
    </row>
</table>    

以下是最终解析器代码的样子:

Here's how the final parser code looks like:

public class Parser {

private String[] files ;

public Parser(String ... files) {
    this.files = files;
}

private List<Inscrito> process() {

    List<Inscrito> inscritos = new ArrayList<Inscrito>();


    for ( String file : files ) {

        XMLInputFactory factory = XMLInputFactory.newFactory();

        try {

            String content = StringEscapeUtils.unescapeXml( FileUtils.readFileToString( new File(file) ) );

            XMLStreamReader parser = factory.createXMLStreamReader( new ByteArrayInputStream( content.getBytes() ) );

            String currentTag = null;
            int columnCount = 0;
            Inscrito inscrito = null;           

            while ( parser.hasNext() ) {

                int currentEvent = parser.next();

                switch ( currentEvent ) {
                case XMLStreamReader.START_ELEMENT: 

                    currentTag = parser.getLocalName();

                    if ( "row".equals( currentTag ) ) {
                        columnCount = 0;
                        inscrito = new Inscrito();                      
                    }

                    break;
                case XMLStreamReader.END_ELEMENT:

                    currentTag = parser.getLocalName();

                    if ( "row".equals( currentTag ) ) {
                        inscritos.add( inscrito );
                    }

                    if ( "column".equals( currentTag ) ) {
                        columnCount++;
                    }                   

                    break;
                case XMLStreamReader.CHARACTERS:

                    if ( "column".equals( currentTag ) ) {

                        String text = parser.getText().trim().replaceAll( "
" , " "); 

                        switch( columnCount ) {
                        case 0:
                            inscrito.setId( Integer.valueOf( text ) );
                            break;
                        case 1:                         
                            inscrito.setFirstName( WordUtils.capitalizeFully( text ) );
                            break;
                        case 2:
                            inscrito.setLastName( WordUtils.capitalizeFully( text ) );
                            break;
                        case 3:
                            inscrito.setEmail( text );
                            break;
                        }

                    }

                    break;
                }

            }

            parser.close();

        } catch (Exception e) {
            throw new IllegalStateException(e);
        }           

    }

    Collections.sort(inscritos);

    return inscritos;

}

public Map<String,List<Inscrito>> parse() {

    List<Inscrito> inscritos = this.process();

    Map<String,List<Inscrito>> resultado = new LinkedHashMap<String, List<Inscrito>>();

    for ( Inscrito i : inscritos ) {

        List<Inscrito> lista = resultado.get( i.getInicial() );

        if ( lista == null ) {
            lista = new ArrayList<Inscrito>();
            resultado.put( i.getInicial(), lista );
        }

        lista.add( i );

    }

    return resultado;
}

}

代码本身是葡萄牙语，但你应该很容易理解它是什么，这里是github上的repo.

The code itself is in portuguese but it should be straightforward for you to understand what it is, here's the repo on github.

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