在上一篇博文中，散仙已经讲了Hadoop的单机伪分布的部署，本篇，散仙就说下，如何eclipse中调试hadoop2.2.0,如果你使用的还是hadoop1.x的版本，那么，也没事，散仙在以前的博客里，也写过eclipse调试1.x的hadoop程序，两者最大的不同之处在于使用的eclipse插件不同，hadoop2.x与hadoop1.x的API，不太一致，所以插件也不一样，我们只需要使用分别对应的插件即可.
下面开始进入正题:
序号名称描述1eclipse Juno Service Release 4.2的本2操作系统Windows73hadoop的eclipse插件hadoop-eclipse-plugin-2.2.0.jar4hadoop的集群环境虚拟机Linux的Centos6.5单机伪分布式5调试程序Hellow World
遇到的几个问题如下：
class="java" name="code">java.io.IOException: Could not locate executable null\bin\winutils.exe in the Hadoop binaries.
解决办法:
在org.apache.hadoop.util.Shell类的checkHadoopHome()方法的返回值里写固定的
本机hadoop的路径，散仙在这里更改如下：
private static String checkHadoopHome() {
// first check the Dflag hadoop.home.dir with JVM scope
//System.setProperty("hadoop.home.dir", "...");
String home = System.getProperty("hadoop.home.dir");
// fall back to the system/user-global env variable
if (home == null) {
home = System.getenv("HADOOP_HOME");
}
try {
// couldn't find either setting for hadoop's home directory
if (home == null) {
throw new IOException("HADOOP_HOME or hadoop.home.dir are not set.");
}
if (home.startsWith("\"") && home.endsWith("\"")) {
home = home.substring(1, home.length()-1);
}
// check that the home setting is actually a directory that exists
File homedir = new File(home);
if (!homedir.isAbsolute() || !homedir.exists() || !homedir.isDirectory()) {
throw new IOException("Hadoop home directory " + homedir
+ " does not exist, is not a directory, or is not an absolute path.");
}
home = homedir.getCanonicalPath();
} catch (IOException ioe) {
if (LOG.isDebugEnabled()) {
LOG.debug("Failed to detect a valid hadoop home directory", ioe);
}
home = null;
}
//固定本机的hadoop地址
home="D:\\hadoop-2.2.0";
return home;
}
第二个异常，Could not locate executable D:\Hadoop\tar\hadoop-2.2.0\hadoop-2.2.0\bin\winutils.exe in the Hadoop binaries. 找不到win上的执行程序，可以去https://github.com/srccodes/hadoop-common-2.2.0-bin下载bin包，覆盖本机的hadoop跟目录下的bin包即可
第三个异常：
Exception in thread "main" java.lang.IllegalArgumentException: Wrong FS: hdfs://192.168.130.54:19000/user/hmail/output/part-00000, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:310)
at org.apache.hadoop.fs.RawLocalFileSystem.pathToFile(RawLocalFileSystem.java:47)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:357)
at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:245)
at org.apache.hadoop.fs.ChecksumFileSystem$ChecksumFSInputChecker.<init>(ChecksumFileSystem.java:125)
at org.apache.hadoop.fs.ChecksumFileSystem.open(ChecksumFileSystem.java:283)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:356)
at com.netease.hadoop.HDFSCatWithAPI.main(HDFSCatWithAPI.java:23)
出现这个异常，一般是HDFS的路径写的有问题，解决办法，拷贝集群上的core-site.xml和hdfs-site.xml文件，放在eclipse的src根目录下即可。
第四个异常：
Exception in thread "main" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
出现这个异常，一般是由于HADOOP_HOME的环境变量配置的有问题，在这里散仙特别说明一下，如果想在Win上的eclipse中成功调试Hadoop2.2，就需要在本机的环境变量上，添加如下的环境变量：
（1）在系统变量中，新建HADOOP_HOME变量，属性值为D:\hadoop-2.2.0.也就是本机对应的hadoop目录
(2)在系统变量的Path里，追加%HADOOP_HOME%/bin即可
以上的问题，是散仙在测试遇到的，经过对症下药，我们的eclipse终于可以成功的调试MR程序了，散仙这里的Hellow World源码如下：
package com.qin.wordcount;
import java.io.IOException;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
/***
*
* Hadoop2.2.0测试
* 放WordCount的例子
*
* @author qindongliang
*
* hadoop技术交流群： 376932160
*
*
* */
public class MyWordCount {
/**
* Mapper
*
* **/
private static class WMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
private IntWritable count=new IntWritable(1);
private Text text=new Text();
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
String values[]=value.toString().split("#");
//System.out.println(values[0]+"========"+values[1]);
count.set(Integer.parseInt(values[1]));
text.set(values[0]);
context.write(text,count);
}
}
/**
* Reducer
*
* **/
private static class WReducer extends Reducer<Text, IntWritable, Text, Text>{
private Text t=new Text();
@Override
protected void reduce(Text key, Iterable<IntWritable> value,Context context)
throws IOException, InterruptedException {
int count=0;
for(IntWritable i:value){
count+=i.get();
}
t.set(count+"");
context.write(key,t);
}
}
/**
* 改动一
* (1)shell源码里添加checkHadoopHome的路径
* (2)974行，FileUtils里面
* **/
public static void main(String[] args) throws Exception{
// String path1=System.getenv("HADOOP_HOME");
// System.out.println(path1);
// System.exit(0);
JobConf conf=new JobConf(MyWordCount.class);
//Configuration conf=new Configuration();
//conf.set("mapred.job.tracker","192.168.75.130:9001");
//读取person中的数据字段
// conf.setJar("tt.jar");
//注意这行代码放在最前面，进行初始化，否则会报
/**Job任务**/
Job job=new Job(conf, "testwordcount");
job.setJarByClass(MyWordCount.class);
System.out.println("模式： "+conf.get("mapred.job.tracker"));;
// job.setCombinerClass(PCombine.class);
// job.setNumReduceTasks(3);//设置为3
job.setMapperClass(WMapper.class);
job.setReducerClass(WReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
String path="hdfs://192.168.46.28:9000/qin/output";
FileSystem fs=FileSystem.get(conf);
Path p=new Path(path);
if(fs.exists(p)){
fs.delete(p, true);
System.out.println("输出路径存在，已删除！");
}
FileInputFormat.setInputPaths(job, "hdfs://192.168.46.28:9000/qin/input");
FileOutputFormat.setOutputPath(job,p );
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
控制台，打印日志如下：
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address
模式： local
输出路径存在，已删除！
INFO - Configuration.warnOnceIfDeprecated(840) | session.id is deprecated. Instead, use dfs.metrics.session-id
INFO - JvmMetrics.init(76) | Initializing JVM Metrics with processName=JobTracker, sessionId=
WARN - JobSubmitter.copyAndConfigureFiles(149) | Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
WARN - JobSubmitter.copyAndConfigureFiles(258) | No job jar file set. User classes may not be found. See Job or Job#setJar(String).
INFO - FileInputFormat.listStatus(287) | Total input paths to process : 1
INFO - JobSubmitter.submitJobInternal(394) | number of splits:1
INFO - Configuration.warnOnceIfDeprecated(840) | user.name is deprecated. Instead, use mapreduce.job.user.name
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.job.name is deprecated. Instead, use mapreduce.job.name
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
INFO - JobSubmitter.printTokens(477) | Submitting tokens for job: job_local1181216011_0001
WARN - Configuration.loadProperty(2172) | file:/root/hadoop/tmp/mapred/staging/qindongliang1181216011/.staging/job_local1181216011_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval; Ignoring.
WARN - Configuration.loadProperty(2172) | file:/root/hadoop/tmp/mapred/staging/qindongliang1181216011/.staging/job_local1181216011_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts; Ignoring.
WARN - Configuration.loadProperty(2172) | file:/root/hadoop/tmp/mapred/local/localRunner/qindongliang/job_local1181216011_0001/job_local1181216011_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval; Ignoring.
WARN - Configuration.loadProperty(2172) | file:/root/hadoop/tmp/mapred/local/localRunner/qindongliang/job_local1181216011_0001/job_local1181216011_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts; Ignoring.
INFO - Job.submit(1272) | The url to track the job: http://localhost:8080/
INFO - Job.monitorAndPrintJob(1317) | Running job: job_local1181216011_0001
INFO - LocalJobRunner$Job.createOutputCommitter(323) | OutputCommitter set in config null
INFO - LocalJobRunner$Job.createOutputCommitter(341) | OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
INFO - LocalJobRunner$Job.run(389) | Waiting for map tasks
INFO - LocalJobRunner$Job$MapTaskRunnable.run(216) | Starting task: attempt_local1181216011_0001_m_000000_0
INFO - ProcfsBasedProcessTree.isAvailable(129) | ProcfsBasedProcessTree currently is supported only on Linux.
INFO - Task.initialize(581) | Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@39550640
INFO - MapTask.runNewMapper(732) | Processing split: hdfs://192.168.46.28:9000/qin/input/test.txt:0+38
INFO - MapTask.createSortingCollector(387) | Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
INFO - MapTask$MapOutputBuffer.setEquator(1183) | (EQUATOR) 0 kvi 26214396(104857584)
INFO - MapTask$MapOutputBuffer.init(975) | mapreduce.task.io.sort.mb: 100
INFO - MapTask$MapOutputBuffer.init(976) | soft limit at 83886080
INFO - MapTask$MapOutputBuffer.init(977) | bufstart = 0; bufvoid = 104857600
INFO - MapTask$MapOutputBuffer.init(978) | kvstart = 26214396; length = 6553600
INFO - LocalJobRunner$Job.statusUpdate(513) |
INFO - MapTask$MapOutputBuffer.flush(1440) | Starting flush of map output
INFO - MapTask$MapOutputBuffer.flush(1459) | Spilling map output
INFO - MapTask$MapOutputBuffer.flush(1460) | bufstart = 0; bufend = 44; bufvoid = 104857600
INFO - MapTask$MapOutputBuffer.flush(1462) | kvstart = 26214396(104857584); kvend = 26214384(104857536); length = 13/6553600
INFO - MapTask$MapOutputBuffer.sortAndSpill(1648) | Finished spill 0
INFO - Task.done(995) | Task:attempt_local1181216011_0001_m_000000_0 is done. And is in the process of committing
INFO - LocalJobRunner$Job.statusUpdate(513) | map
INFO - Task.sendDone(1115) | Task 'attempt_local1181216011_0001_m_000000_0' done.
INFO - LocalJobRunner$Job$MapTaskRunnable.run(241) | Finishing task: attempt_local1181216011_0001_m_000000_0
INFO - LocalJobRunner$Job.run(397) | Map task executor complete.
INFO - ProcfsBasedProcessTree.isAvailable(129) | ProcfsBasedProcessTree currently is supported only on Linux.
INFO - Task.initialize(581) | Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@68843e7b
INFO - Merger$MergeQueue.merge(568) | Merging 1 sorted segments
INFO - Merger$MergeQueue.merge(667) | Down to the last merge-pass, with 1 segments left of total size: 45 bytes
INFO - LocalJobRunner$Job.statusUpdate(513) |
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
INFO - Task.done(995) | Task:attempt_local1181216011_0001_r_000000_0 is done. And is in the process of committing
INFO - LocalJobRunner$Job.statusUpdate(513) |
INFO - Task.commit(1156) | Task attempt_local1181216011_0001_r_000000_0 is allowed to commit now
INFO - FileOutputCommitter.commitTask(439) | Saved output of task 'attempt_local1181216011_0001_r_000000_0' to hdfs://192.168.46.28:9000/qin/output/_temporary/0/task_local1181216011_0001_r_000000
INFO - LocalJobRunner$Job.statusUpdate(513) | reduce > reduce
INFO - Task.sendDone(1115) | Task 'attempt_local1181216011_0001_r_000000_0' done.
INFO - Job.monitorAndPrintJob(1338) | Job job_local1181216011_0001 running in uber mode : false
INFO - Job.monitorAndPrintJob(1345) | map 100% reduce 100%
INFO - Job.monitorAndPrintJob(1356) | Job job_local1181216011_0001 completed successfully
INFO - Job.monitorAndPrintJob(1363) | Counters: 32
File System Counters
FILE: Number of bytes read=372
FILE: Number of bytes written=382174
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=76
HDFS: Number of bytes written=27
HDFS: Number of read operations=17
HDFS: Number of large read operations=0
HDFS: Number of write operations=6
Map-Reduce Framework
Map input records=4
Map output records=4
Map output bytes=44
Map output materialized bytes=58
Input split bytes=109
Combine input records=0
Combine output records=0
Reduce input groups=3
Reduce shuffle bytes=0
Reduce input records=4
Reduce output records=3
Spilled Records=8
Shuffled Maps =0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=0
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=532938752
File Input Format Counters
Bytes Read=38
File Output Format Counters
Bytes Written=27
输入的测试数据如下：
中国#1
美国#2
英国#3
中国#2
输出的结果如下：
中国 3
美国 2
英国 3
至此，我们已经成功的在eclipse里远程调试hadoop成功，调试时，注意散仙，在上文提出的几个问题，如果遇到时，按照对应的方法解决即可。
相关文章
