如何正确解决MapReduce在读取XML文件时出现的报错问题？

使用MapReduce读取XML文件时，确保正确解析XML格式并处理可能的错误。可以使用Hadoop Streaming或Pig等工具来辅助处理。

MapReduce读取XML文件

问题描述

在使用MapReduce处理XML文件时，常见的问题是Hadoop无法直接解析XML格式，由于XML没有同步标记，并行处理单个XML文件比较棘手，MapReduce默认不支持XML输入格式，需要自定义InputFormat类来处理XML数据。

解决方案

为了在MapReduce中读取和处理XML文件，可以使用Mahout提供的XmlInputFormat类，以下是具体步骤：

1、配置作业：设置xmlinput.start和xmlinput.end参数，指定XML文件中的开始和结束标记。

   Configuration conf = new Configuration();
   conf.set("xmlinput.start", "<property>");
   conf.set("xmlinput.end", "</property>");

2、设置输入格式：将MapReduce作业的输入格式设置为XmlInputFormat。

   Job job = new Job(conf);
   job.setInputFormatClass(XmlInputFormat.class);

3、编写Mapper：在Mapper中，使用Java的XML Streaming API（StAX）解析器提取每个属性的键和值。

   import org.apache.hadoop.conf.Configuration;
   import org.apache.hadoop.fs.Path;
   import org.apache.hadoop.io.*;
   import org.apache.hadoop.mapreduce.*;
   import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
   import org.apache.hadoop.mapreduce.lib.output.*;
   import org.slf4j.*;
   import javax.xml.stream.*;
   import java.io.*;
   public static class Map extends Mapper<LongWritable, Text, Text, Text> {
       @Override
       protected void map(LongWritable key, Text value, Mapper.Context context) throws IOException, InterruptedException {
           String document = value.toString();
           try {
               XMLStreamReader reader = XMLInputFactory.newInstance().createXMLStreamReader(new ByteArrayInputStream(document.getBytes()));
               String propertyName = "";
               String propertyValue = "";
               String currentElement = "";
               while (reader.hasNext()) {
                   int code = reader.next();
                   switch (code) {
                       case START_ELEMENT:
                           currentElement = reader.getLocalName();
                           break;
                       case CHARACTERS:
                           if (currentElement.equalsIgnoreCase("name")) {
                               propertyName += reader.getText();
                           } else if (currentElement.equalsIgnoreCase("value")) {
                               propertyValue += reader.getText();
                           }
                           break;
                   }
               }
               reader.close();
               context.write(propertyName.trim(), propertyValue.trim());
           } catch (Exception e) {
               log.error("Error processing '" + document + "'", e);
           }
       }
   }

4、执行MapReduce作业：运行配置好的MapReduce作业，处理HDFS中的XML文件。

示例代码

完整的MapReduce作业代码如下：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.slf4j.*;
import javax.xml.stream.*;
import java.io.*;
public class XmlInputExample {
    public static class Map extends Mapper<LongWritable, Text, Text, Text> {
        @Override
        protected void map(LongWritable key, Text value, Mapper.Context context) throws IOException, InterruptedException {
            String document = value.toString();
            try {
                XMLStreamReader reader = XMLInputFactory.newInstance().createXMLStreamReader(new ByteArrayInputStream(document.getBytes()));
                String propertyName = "";
                String propertyValue = "";
                String currentElement = "";
                while (reader.hasNext()) {
                    int code = reader.next();
                    switch (code) {
                        case START_ELEMENT:
                            currentElement = reader.getLocalName();
                            break;
                        case CHARACTERS:
                            if (currentElement.equalsIgnoreCase("name")) {
                                propertyName += reader.getText();
                            } else if (currentElement.equalsIgnoreCase("value")) {
                                propertyValue += reader.getText();
                            }
                            break;
                    }
                }
                reader.close();
                context.write(propertyName.trim(), propertyValue.trim());
            } catch (Exception e) {
                log.error("Error processing '" + document + "'", e);
            }
        }
    }
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        conf.set("xmlinput.start", "<property>");
        conf.set("xmlinput.end", "</property>");
        Job job = new Job(conf);
        job.setJarByClass(XmlInputExample.class);
        job.setMapperClass(Map.class);
        job.setNumReduceTasks(0);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        job.setInputFormatClass(XmlInputFormat.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }}