Hadoop学习记录（7）|Eclipse远程调试Hadoop

1、创建Hadoop项目

2、创建包、类

这里使用hdfs.WordCount为例

3、编写自定Mapper和Reducer程序

MyMapper类

static class MyMapper extends
			Mapper<LongWritable, Text, Text, LongWritable> {
	@Override
	protected void map(LongWritable k1, Text v1, Context context)
			throws IOException, InterruptedException {
		// 对内容进行分词处理存到字符数组内
		StringTokenizer tokenizer = new StringTokenizer(v1.toString());
		// 创建Text k2
		Text k2 = new Text();
		// 遍历写入context中
		while (tokenizer.hasMoreTokens()) {
			k2.set(tokenizer.nextToken());
			context.write(k2, new LongWritable(1));
		}

	}

}</pre></div>

Reducer类
static class MyReducer extends
			Reducer<Text, LongWritable, Text, LongWritable> {
		@Override
		protected void reduce(Text k2, Iterable<LongWritable> v2s,
				Context context) throws IOException, InterruptedException {
			long sum = 0;
			for(LongWritable val : v2s){
				sum += val.get();
			}
			context.write(k2, new LongWritable(sum));
		}
	}


编写main驱动方法

public static void main(String[] args) throws Exception {
	if(args.length != 2){
		System.err.print("Usage:wordcount");
		System.exit(2);
	}
	
	Configuration conf = new Configuration();
	Job job = new Job(conf,WordCount.class.getSimpleName());
	//用eclipse插件运行相当于是jar包运行
	job.setJarByClass(WordCount.class);
	//设置mapper
	job.setMapperClass(MyMapper.class);
	//设置map输出k2的类型
	job.setMapOutputKeyClass(Text.class);
	//设置map输出v2的类型
	job.setMapOutputValueClass(LongWritable.class);
	//设置分区类
	job.setPartitionerClass(HashPartitioner.class);
	//设置作业数量
	job.setNumReduceTasks(1);
	//设置reducer类
	job.setReducerClass(MyReducer.class);
	//设置输出的格式
	job.setOutputFormatClass(TextOutputFormat.class);
	//设置k3的输出类型
	job.setOutputKeyClass(Text.class);
	//设置v3的输出类型
	job.setOutputValueClass(LongWritable.class);
	
	//这里是从外面传入参数
	FileInputFormat.setInputPaths(job, new Path(args[0]));
	FileOutputFormat.setOutputPath(job, new Path(args[1]));
	//提交任务,如果返回false代表有异常,使用system.exit结束java虚拟机,如果没问题返回0正常执行.
	System.exit(job.waitForCompletion(true)?0:1);
	
	
}</pre></div>

4、运行mapreduce程序远程调用hadoop。
先配置访问路径

写hdfs访问路径。

现在使用Run as—Run on hadoop会出现一个错误

14/03/11 15:58:22 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/03/11 15:58:22 ERROR security.UserGroupInformation: PriviledgedActionException as:Sky cause:java.io.IOException: Failed to set permissions of path: 	mphadoop-SkymapredstagingSky1823204560.staging to 0700
Exception in thread "main" java.io.IOException: Failed to set permissions of path: 	mphadoop-SkymapredstagingSky1823204560.staging to 0700
	at org.apache.hadoop.fs.FileUtil.checkReturnValue(FileUtil.java:689)
	at org.apache.hadoop.fs.FileUtil.setPermission(FileUtil.java:662)
	at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:509)
	at org.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:344)
	at org.apache.hadoop.fs.FilterFileSystem.mkdirs(FilterFileSystem.java:189)
	at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:116)
	at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:918)
	at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:912)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:415)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1149)
	at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:912)
	at org.apache.hadoop.mapreduce.Job.submit(Job.java:500)
	at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:530)
	at hdfs.WordCount.main(WordCount.java:58)
这个是windows下的权限问题，在linux上运行时正常的。
解决方法：

打开F:SoftwareHadoophadoop-1.1.2srccoreorgapachehadoopfsFileUtil.java
注释checkReturnValue函数中的内容，保存即可！

再运行时正常输出计算器，并生成了新的目录。输出目录不能存在，由hadoop自动创建完成！

14/03/11 16:08:40 INFO mapred.JobClient:  map 100% reduce 100%
14/03/11 16:08:41 INFO mapred.JobClient: Job complete: job_local_0001
14/03/11 16:08:41 INFO mapred.JobClient: Counters: 19
14/03/11 16:08:41 INFO mapred.JobClient:   File Output Format Counters 
14/03/11 16:08:41 INFO mapred.JobClient:     Bytes Written=2154020
14/03/11 16:08:41 INFO mapred.JobClient:   FileSystemCounters
14/03/11 16:08:41 INFO mapred.JobClient:     FILE_BYTES_READ=631320575
14/03/11 16:08:41 INFO mapred.JobClient:     HDFS_BYTES_READ=141910490
14/03/11 16:08:41 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=774430506
14/03/11 16:08:41 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=2154020
14/03/11 16:08:41 INFO mapred.JobClient:   File Input Format Counters 
14/03/11 16:08:41 INFO mapred.JobClient:     Bytes Read=70955245
14/03/11 16:08:41 INFO mapred.JobClient:   Map-Reduce Framework
14/03/11 16:08:41 INFO mapred.JobClient:     Reduce input groups=59150
14/03/11 16:08:41 INFO mapred.JobClient:     Map output materialized bytes=142981973
14/03/11 16:08:41 INFO mapred.JobClient:     Combine output records=0
14/03/11 16:08:41 INFO mapred.JobClient:     Map input records=255015
14/03/11 16:08:41 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/03/11 16:08:41 INFO mapred.JobClient:     Reduce output records=59150
14/03/11 16:08:41 INFO mapred.JobClient:     Spilled Records=26709860
14/03/11 16:08:41 INFO mapred.JobClient:     Map output bytes=128572984
14/03/11 16:08:41 INFO mapred.JobClient:     Total committed heap usage (bytes)=305004544
14/03/11 16:08:41 INFO mapred.JobClient:     Combine input records=0
14/03/11 16:08:41 INFO mapred.JobClient:     Map output records=7201751
14/03/11 16:08:41 INFO mapred.JobClient:     SPLIT_RAW_BYTES=99
14/03/11 16:08:41 INFO mapred.JobClient:     Reduce input records=7201751