使用eclipse来调试hadoop作业是非常简洁方便的，散仙以前也有用eclipse开发过hadoop程序，但是一直没有深入了解eclipse调试的一些模式，有些时候也会出一些莫名奇妙的异常，最常见的就是下面这个
java.lang.RuntimeException: java.lang.ClassNotFoundException: com.qin.sort.TestSort$SMapper at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:857) at org.apache.hadoop.mapreduce.JobContext.getMapperClass(JobContext.java:199) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:718) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:364) at org.apache.hadoop.mapred.Child$4.run(Child.java:255) 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:1190)
这个异常是最莫名其妙的一个，明明自己的MR类里面有这个Mapper的内部类，但是一运行程序，就报这个异常，说找不到这个类，然后就百般查找问题，找来找去，也没找出个所以然。
其实这并不是程序的问题，而是对eclipse的调试模式不够了解的问题，eclipse上运行hadoop总的来说有2种模式，第一种就是Local模式，也叫本地模式，第二种就是我们正式的线上集群模式，当运行本地模式的时候，程序并不会被提交到Hadoop集群上，而是基于单机的模式跑的，但是单机的模式，运行的结果仍在是存储在HDFS上的，只不过没有利用hadoop集群的资源，单机的模式不要提交jar包到hadoop集群上，因此一般我们使用local来测试我们的MR程序是否能够正常运行，
下面我们来看下，基于Local模式跑的一个排序作业：
排序数据：a 784b 12c -11dd 99999
程序源码：
package com.qin.sort;import java.io.IOException;import org.apache.hadoop.conf.Configuration;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.io.WritableComparator;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.output.FileOutputFormat; /** * 测试排序的 * MR作业类 * * QQ技术交流群：324714439 * @author qindongliang * * * **/public class TestSort { /** * Map类 * * **/ private static class SMapper extends Mapper<LongWritable, Text, IntWritable, Text>{ private Text text=new Text();//输出 private static final IntWritable one=new IntWritable(); @Override protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException { String s=value.toString(); //System.out.println("abc: "+s); // if((s.trim().indexOf(" ")!=-1)){ String ss[]=s.split(" "); one.set(Integer.parseInt(ss[1].trim()));// text.set(ss[0].trim()); context.write(one, text); } } /** * Reduce类 * * */ private static class SReduce extends Reducer<IntWritable, Text, Text, IntWritable>{ private Text text=new Text(); @Override protected void reduce(IntWritable arg0, Iterable<Text> arg1,Context context) throws IOException, InterruptedException { for(Text t:arg1){ text.set(t.toString()); context.write(text, arg0); } } } /** * 排序的类 * * **/ private static class SSort extends WritableComparator{ public SSort() { super(IntWritable.class,true);//注册排序组件 } @Override public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3, int arg4, int arg5) { /** * 控制升降序的关键控制-号是降序 * */ return -super.compare(arg0, arg1, arg2, arg3, arg4, arg5);//注意使用负号来完成降序 } @Override public int compare(Object a, Object b) { return -super.compare(a, b);//注意使用负号来完成降序 } } /** * main方法 * */ public static void main(String[] args) throws Exception{ String inputPath="hdfs://192.168.75.130:9000/root/output"; String outputPath="hdfs://192.168.75.130:9000/root/outputsort"; JobConf conf=new JobConf(); //Configuration conf=new Configuration(); //在你的文件地址前自动添加：hdfs://master:9000/ // conf.set("fs.default.name", "hdfs://192.168.75.130:9000"); //指定jobtracker的ip和端口号，master在/etc/hosts中可以配置 // conf.set("mapred.job.tracker","192.168.75.130:9001"); // conf.get("mapred.job.tracker"); System.out.println("模式： "+conf.get("mapred.job.tracker")); // conf.setJar("tt.jar"); FileSystem fs=FileSystem.get(conf); Path pout=new Path(outputPath); if(fs.exists(pout)){ fs.delete(pout, true); System.out.println("存在此路径, 已经删除......"); } Job job=new Job(conf, "sort123"); job.setJarByClass(TestSort.class); job.setOutputKeyClass(IntWritable.class);//告诉map，reduce输出K,V的类型 FileInputFormat.setInputPaths(job, new Path(inputPath)); //输入路径 FileOutputFormat.setOutputPath(job, new Path(outputPath));//输出路径 job.setMapperClass(SMapper.class);//map类 job.setReducerClass(SReduce.class);//reduce类 job.setSortComparatorClass(SSort.class);//排序类// job.setInputFormatClass(org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class);// job.setOutputFormatClass(TextOutputFormat.class); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
打印结果如下：
模式： local存在此路径, 已经删除......WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicableWARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.WARN - JobClient.copyAndConfigureFiles(870) | No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1WARN - LoadSnappy.<clinit>(46) | Snappy native library not loadedINFO - JobClient.monitorAndPrintJob(1380) | Running job: job_local1242054158_0001INFO - LocalJobRunner$Job.run(340) | Waiting for map tasksINFO - LocalJobRunner$Job$MapTaskRunnable.run(204) | Starting task: attempt_local1242054158_0001_m_000000_0INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : nullINFO - MapTask.runNewMapper(729) | Processing split: hdfs://192.168.75.130:9000/root/output/sort.txt:0+28INFO - MapTask$MapOutputBuffer.<init>(949) | io.sort.mb = 100INFO - MapTask$MapOutputBuffer.<init>(961) | data buffer = 79691776/99614720INFO - MapTask$MapOutputBuffer.<init>(962) | record buffer = 262144/327680INFO - MapTask$MapOutputBuffer.flush(1289) | Starting flush of map outputINFO - MapTask$MapOutputBuffer.sortAndSpill(1471) | Finished spill 0INFO - Task.done(858) | Task:attempt_local1242054158_0001_m_000000_0 is done. And is in the process of commitingINFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.sendDone(970) | Task 'attempt_local1242054158_0001_m_000000_0' done.INFO - LocalJobRunner$Job$MapTaskRunnable.run(229) | Finishing task: attempt_local1242054158_0001_m_000000_0INFO - LocalJobRunner$Job.run(348) | Map task executor complete.INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : nullINFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Merger$MergeQueue.merge(408) | Merging 1 sorted segmentsINFO - Merger$MergeQueue.merge(491) | Down to the last merge-pass, with 1 segments left of total size: 35 bytesINFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.done(858) | Task:attempt_local1242054158_0001_r_000000_0 is done. And is in the process of commitingINFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.commit(1011) | Task attempt_local1242054158_0001_r_000000_0 is allowed to commit nowINFO - FileOutputCommitter.commitTask(173) | Saved output of task 'attempt_local1242054158_0001_r_000000_0' to hdfs://192.168.75.130:9000/root/outputsortINFO - LocalJobRunner$Job.statusUpdate(466) | reduce > reduceINFO - Task.sendDone(970) | Task 'attempt_local1242054158_0001_r_000000_0' done.INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100%INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_local1242054158_0001INFO - Counters.log(585) | Counters: 19INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=26INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=28INFO - Counters.log(587) | FileSystemCountersINFO - Counters.log(589) | FILE_BYTES_READ=393INFO - Counters.log(589) | HDFS_BYTES_READ=56INFO - Counters.log(589) | FILE_BYTES_WRITTEN=135742INFO - Counters.log(589) | HDFS_BYTES_WRITTEN=26INFO - Counters.log(587) | Map-Reduce FrameworkINFO - Counters.log(589) | Map output materialized bytes=39INFO - Counters.log(589) | Map input records=4INFO - Counters.log(589) | Reduce shuffle bytes=0INFO - Counters.log(589) | Spilled Records=8INFO - Counters.log(589) | Map output bytes=25INFO - Counters.log(589) | Total committed heap usage (bytes)=455475200INFO - Counters.log(589) | Combine input records=0INFO - Counters.log(589) | SPLIT_RAW_BYTES=112INFO - Counters.log(589) | Reduce input records=4INFO - Counters.log(589) | Reduce input groups=4INFO - Counters.log(589) | Combine output records=0INFO - Counters.log(589) | Reduce output records=4INFO - Counters.log(589) | Map output records=4
排序结果如下:
dd 99999a 784b 12c -11
单机模式调试通过之后，我们就可以考虑采用hadoop集群的模式来跑，这时候有2种方式，可以来完成这件事，第一是，为了方便将整个项目打成一个jar包，上传到Linux上，然后执行shell命令：
bin/hadoop jar tt.jar com.qin.sort.TestSort
来进行测试，注意，散仙是为了方便，路径是写死在程序里面所以后面不用输入，输入和输出路径，正式的开发，为了灵活性，一般会通过外部传产来指定输入和输出路径。
第二种方式，也比较方便，直接在eclipse中提交到hadoop集群作业中，不过即使是使用eclipse来提交作业，还是需要将整个项目打成一个jar包，只不过这时是eclipse帮我们提交作业的，这样我们就可以Win平台上直接提交运行hadoop作业了，但是主流的还是使用上传jar包的方式。关于把整个项目打成一个jar包，散仙在后面会上传一个ant脚本，直接执行它就可以了，这样就可以把有依赖关系的类打在一起，把一整个项目做为一个整体，在hadoop上，只需要指定jar，指定类的全名称，和输入，输出路径即可。ant的脚本内容如下：
<project name="${component.name}" basedir="." default="jar"> <property environment="env"/> <!-- <property name="hadoop.home" value="${env.HADOOP_HOME}"/> --> <property name="hadoop.home" value="D:/hadoop-1.2.0"/> <!-- 指定jar包的名字 --> <property name="jar.name" value="tt.jar"/> <path id="project.classpath"> <fileset dir="lib"> <include name="*.jar" /> </fileset> <fileset dir="${hadoop.home}"> <include name="**/*.jar" /> </fileset> </path> <target name="clean" > <delete dir="bin" failonerror="false" /> <mkdir dir="bin"/> </target> <target name="build" depends="clean"> <echo message="${ant.project.name}: ${ant.file}"/> <javac destdir="bin" encoding="utf-8" debug="true" includeantruntime="false" debuglevel="lines,vars,source"> <src path="src"/> <exclude name="**/.svn" /> <classpath refid="project.classpath"/> </javac> <copy todir="bin"> <fileset dir="src"> <include name="*config*"/> </fileset> </copy> </target> <target name="jar" depends="build"> <copy todir="bin/lib"> <fileset dir="lib"> <include name="**/*.*"/> </fileset> </copy> <path id="lib-classpath"> <fileset dir="lib" includes="**/*.jar" /> </path> <pathconvert property="my.classpath" pathsep=" " > <mapper> <chainedmapper> <!-- 移除绝对路径 --> <flattenmapper /> <!-- 加上lib前缀 --> <globmapper from="*" to="lib/*" /> </chainedmapper> </mapper> <path refid="lib-classpath" /> </pathconvert> <jar basedir="bin" destfile="${jar.name}" > <include name="**/*"/> <!-- define MANIFEST.MF --> <manifest> <attribute name="Class-Path" value="${my.classpath}" /> </manifest> </jar> </target></project>运行上面的这个ant脚本之后，我们的项目就会被打成一个jar包，截图如下：
jar包有了之后，我们先测试在eclipse上如何把作业提交到hadoop集群上，只要把main方面的代码，稍加改动即可：
/** * main方法 * */ public static void main(String[] args) throws Exception{ String inputPath="hdfs://192.168.75.130:9000/root/output"; String outputPath="hdfs://192.168.75.130:9000/root/outputsort"; JobConf conf=new JobConf(); //Configuration conf=new Configuration();//可以使用这个conf来测试Local模式 //如果在src目录下有，mapred-site.xml文件，就不要此行代码 //注意此行代码也是在非Local模式下才使用 conf.set("mapred.job.tracker","192.168.75.130:9001"); // conf.get("mapred.job.tracker"); System.out.println("模式： "+conf.get("mapred.job.tracker")); // conf.setJar("tt.jar"); 非Local模式下使用 FileSystem fs=FileSystem.get(conf); Path pout=new Path(outputPath); if(fs.exists(pout)){ fs.delete(pout, true); System.out.println("存在此路径, 已经删除......"); } Job job=new Job(conf, "sort123"); job.setJarByClass(TestSort.class); job.setOutputKeyClass(IntWritable.class);//告诉map，reduce输出K,V的类型 FileInputFormat.setInputPaths(job, new Path(inputPath)); //输入路径 FileOutputFormat.setOutputPath(job, new Path(outputPath));//输出路径 job.setMapperClass(SMapper.class);//map类 job.setReducerClass(SReduce.class);//reduce类 job.setSortComparatorClass(SSort.class);//排序类// job.setInputFormatClass(org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class);// job.setOutputFormatClass(TextOutputFormat.class); System.exit(job.waitForCompletion(true) ? 0 : 1); }运行程序，输出，如下：
模式： 192.168.75.130:9001存在此路径, 已经删除......WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicableWARN - LoadSnappy.<clinit>(46) | Snappy native library not loadedINFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201403252058_0035INFO - JobClient.monitorAndPrintJob(1393) | map 0% reduce 0%INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 0%INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 33%INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100%INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201403252058_0035INFO - Counters.log(585) | Counters: 29INFO - Counters.log(587) | Job Counters INFO - Counters.log(589) | Launched reduce tasks=1INFO - Counters.log(589) | SLOTS_MILLIS_MAPS=8498INFO - Counters.log(589) | Total time spent by all reduces waiting after reserving slots (ms)=0INFO - Counters.log(589) | Total time spent by all maps waiting after reserving slots (ms)=0INFO - Counters.log(589) | Launched map tasks=1INFO - Counters.log(589) | Data-local map tasks=1INFO - Counters.log(589) | SLOTS_MILLIS_REDUCES=9667INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=26INFO - Counters.log(587) | FileSystemCountersINFO - Counters.log(589) | FILE_BYTES_READ=39INFO - Counters.log(589) | HDFS_BYTES_READ=140INFO - Counters.log(589) | FILE_BYTES_WRITTEN=117654INFO - Counters.log(589) | HDFS_BYTES_WRITTEN=26INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=28INFO - Counters.log(587) | Map-Reduce FrameworkINFO - Counters.log(589) | Map output materialized bytes=39INFO - Counters.log(589) | Map input records=4INFO - Counters.log(589) | Reduce shuffle bytes=39INFO - Counters.log(589) | Spilled Records=8INFO - Counters.log(589) | Map output bytes=25INFO - Counters.log(589) | Total committed heap usage (bytes)=176033792INFO - Counters.log(589) | CPU time spent (ms)=1140INFO - Counters.log(589) | Combine input records=0INFO - Counters.log(589) | SPLIT_RAW_BYTES=112INFO - Counters.log(589) | Reduce input records=4INFO - Counters.log(589) | Reduce input groups=4INFO - Counters.log(589) | Combine output records=0INFO - Counters.log(589) | Physical memory (bytes) snapshot=259264512INFO - Counters.log(589) | Reduce output records=4INFO - Counters.log(589) | Virtual memory (bytes) snapshot=1460555776INFO - Counters.log(589) | Map output records=4
我们可以看出，运行正常，排序的内容如下：
dd 99999a 784b 12c -11
结果和local模式下的一样，还有一个与local模式不同的地方是，我们可以在http://192.168.75.130:50030/jobtracker.jsp的任务页面上找到刚才执行的任务状况，这一点在Local模式下运行程序，是没有的。/size]
[size=large]最后，散仙再来看下，如何将jar包，上传到Linux提交作业到hadoop集群上。刚才，我们已经把jar给打好了，现在只需上传到linux上即可：
然后开始执行shell命令运行程序：
到此，我们已经完美的执行成功，最后一点需要注意的是，在执行排序任务时，如果出现异常：
java.lang.Exception: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.LongWritable, recieved org.apache.hadoop.io.IntWritable at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:354)Caused by: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.LongWritable, recieved org.apache.hadoop.io.IntWritable at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1019) at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:690) at org.apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80) at com.qin.sort.TestSort$SMapper.map(TestSort.java:51) at com.qin.sort.TestSort$SMapper.map(TestSort.java:1) at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:364) at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:223) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:334) at java.util.concurrent.FutureTask.run(FutureTask.java:166) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1110) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:603) at java.lang.Thread.run(Thread.java:722)
这个异常的出现，多半是因为，我们没有指定输出的Key，或者Value，或者指定的类型不一致，导致，我们只需要正确的设置输出的Key或者Value的类型即可.
job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(Text.class);
设置完后，就可以正常测试运行了。