Win7上eclipse无插件提交Hadoop2.2分布式作业
一直以来，都以为，想在Win上提交hadoop集群的作业，必须得在eclipse上安装hadoop-eclipse-plugin插件才可以提交，但最近与同事交流，发现其实，不一定必须安装hadoop的eclipse插件，才能提交。今天试了一把，发现果然可以不用安装插件也可以正确提交作业到集群上，故在此总结一下。
既然，无须安装hadoop的eclipse插件，就能提交hadoop作业，那为毛，还出现了这个插件呢？ 其实安装插件除了能直接提交作业外，还有一个比较方便的功能，就是能直接在eclipse上对HDFS上的文件，进行删除，上传，新建目录等，这一点是不安装插件做不到的，当然，如果你不需要这些操作，那么就无所谓了，仅仅提交个作业而已。
下面说下，如何在eclipse上使用无插件提交hadoop作业，（在hadoop集群的8088界面上可以看到提交的作业信息是否成功）。
| 序号 | 操作 | 说明 |
| 1 | eclipse IDE | 散仙在这里是4.2版本的eclipse |
| 2 | hadoop2.2的64位完整包 | 散仙在这里放在D盘根目录下 |
| 3 | 修改源码org/apache/hadoop/mapred/YARNRunner.java，改变linux与windows的路径不一致bug | 散仙已经修改好，文末散仙会上传这个修改好的类 |
| 4 | 把linux集群上的配置文件，core-site.xml，hdfs-site.xml,mapred.site.xml和yarn-site.xml文件，放在src根目录下，另外在D盘hadoop的/etc/hadoop目录下，覆盖一下 | 注意一致 |
| 5 | 编写wordcount的MR例子，开始测试 | 入门测试 |
| 6 | 高富帅工程师一名 | 主角 |
| 7 | 配置hadoop的win上的环境变量HADOOP_HOME | 只配置这一个即可 |
上面的操作都完成后，就可以进行测试了,散仙在这里的WordCount源码如下：
package com.mywordcount;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FilenameFilter;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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;
import org.apache.hadoop.util.Tool;
/***
*
* Hadoop2.2.0 无插件提交集群作业
*
* @author qindongliang
*
* hadoop技术交流群： 376932160
*
*
* */
public class MyWordCount2 {
/**
* 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);
}
}
public static void printEnv(Job job) {
Configuration conf = job.getConfiguration();
System.out.println("###########################################");
System.out.println("fs.defaultFS:" + conf.get("fs.defaultFS"));
System.out.println("mapred.job.tracker:"
+ conf.get("mapred.job.tracker"));
System.out.println("mapreduce.framework.name" + ":"
+ conf.get("mapreduce.framework.name"));
System.out.println("yarn.nodemanager.aux-services" + ":"
+ conf.get("yarn.nodemanager.aux-services"));
System.out.println("yarn.resourcemanager.address" + ":"
+ conf.get("yarn.resourcemanager.address"));
System.out.println("yarn.resourcemanager.scheduler.address" + ":"
+ conf.get("yarn.resourcemanager.scheduler.address"));
System.out.println("yarn.resourcemanager.resource-tracker.address"
+ ":"
+ conf.get("yarn.resourcemanager.resource-tracker.address"));
System.out.println("yarn.application.classpath" + ":"
+ conf.get("yarn.application.classpath"));
System.out.println("zkhost:" + conf.get("zkhost"));
System.out.println("namespace:" + conf.get("namespace"));
System.out.println("project:" + conf.get("project"));
System.out.println("collection:" + conf.get("collection"));
System.out.println("shard:" + conf.get("shard"));
System.out.println("###########################################");
}
/**
* 载入hadoop的配置文件
* 兼容hadoop1.x和hadoop2.x
*
* */
public static void getConf(final Configuration conf) throws FileNotFoundException{
String HADOOP_CONF_DIR = System.getenv().get("HADOOP_CONF_DIR");
String HADOOP_HOME = System.getenv().get("HADOOP_HOME");
System.out.println("HADOOP_HOME:" + HADOOP_HOME);
System.out.println("HADOOP_CONF_DIR:" + HADOOP_CONF_DIR);//此处兼容hadoop1.x
//此处兼容hadoop2.x
if (HADOOP_CONF_DIR == null || HADOOP_CONF_DIR.isEmpty()) {
HADOOP_CONF_DIR = HADOOP_HOME + "/etc/hadoop";
}
//得到hadoop的conf目录的路径加载文件
File file = new File(HADOOP_CONF_DIR);
FilenameFilter filter = new FilenameFilter() {
@Override
public boolean accept(File dir, String name) {
return name.endsWith("xml");
}
};
//获取hadoop的仅仅xml结尾的文件列表
String[] list = file.list(filter);
for (String fn : list) {
System.out.println("Loading Configuration: " + HADOOP_CONF_DIR
+ "/" + fn);
//循环加载xml文件
conf.addResource(new FileInputStream(HADOOP_CONF_DIR + "/" + fn));
}
//yarn的classpath路径，如果为空则加载拼接yarn的路径
if (conf.get("yarn.application.classpath", "").isEmpty()) {
StringBuilder sb = new StringBuilder();
sb.append(System.getenv("CLASSPATH")).append(":");
sb.append(HADOOP_HOME).append("/share/hadoop/common/lib/*")
.append(":");
sb.append(HADOOP_HOME).append("/share/hadoop/common/*").append(":");
sb.append(HADOOP_HOME).append("/share/hadoop/hdfs/*").append(":");
sb.append(HADOOP_HOME).append("/share/hadoop/mapreduce/*")
.append(":");
sb.append(HADOOP_HOME).append("/share/hadoop/yarn/*").append(":");
sb.append(HADOOP_HOME).append("/lib/*").append(":");
conf.set("yarn.application.classpath", sb.toString());
}
}
public static void main(String[] args) throws Exception { {
Configuration conf = new Configuration();
conf.set("mapreduce.job.jar", "myjob.jar");//此处代码，一定放在Job任务前面，否则会报类找不到的异常
Job job = Job.getInstance(conf, "345");
getConf(conf);
job.setJarByClass(MyWordCount2.class);
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 = "/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, "/qin/input");
FileOutputFormat.setOutputPath(job, p);
printEnv(job);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
}
项目结构目录，截图如下：
运行信息如下：
HADOOP_HOME:D:\hadoop-2.2.0 HADOOP_CONF_DIR:null Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/capacity-scheduler.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/core-site.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/hadoop-policy.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/hdfs-site.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/httpfs-site.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/mapred-site.xml Loading Configuration: D:\hadoop-2.2.0/etc/hadoop/yarn-site.xml 2014-06-25 20:40:08,419 WARN [main] conf.Configuration (Configuration.java:loadProperty(2172)) - java.io.FileInputStream@3ba08dab:an attempt to override final parameter: mapreduce.jobtracker.address; Ignoring. 输出路径存在，已删除！ ########################################### fs.defaultFS:hdfs://h1:9000 2014-06-25 20:40:08,897 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address mapred.job.tracker:h1:8021 mapreduce.framework.name:yarn yarn.nodemanager.aux-services:mapreduce_shuffle yarn.resourcemanager.address:h1:8032 yarn.resourcemanager.scheduler.address:h1:8030 yarn.resourcemanager.resource-tracker.address:h1:8031 yarn.application.classpath:$HADOOP_CONF_DIR,$HADOOP_COMMON_HOME/share/hadoop/common/*,$HADOOP_COMMON_HOME/share/hadoop/common/lib/*,$HADOOP_HDFS_HOME/share/hadoop/hdfs/*,$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*,$HADOOP_YARN_HOME/share/hadoop/yarn/*,$HADOOP_YARN_HOME/share/hadoop/yarn/lib/* zkhost:null namespace:null project:null collection:null shard:null ########################################### 2014-06-25 20:40:08,972 INFO [main] client.RMProxy (RMProxy.java:createRMProxy(56)) - Connecting to ResourceManager at h1/192.168.46.32:8032 2014-06-25 20:40:09,153 WARN [main] mapreduce.JobSubmitter (JobSubmitter.java:copyAndConfigureFiles(149)) - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 2014-06-25 20:40:09,331 INFO [main] input.FileInputFormat (FileInputFormat.java:listStatus(287)) - Total input paths to process : 1 2014-06-25 20:40:09,402 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(394)) - number of splits:1 2014-06-25 20:40:09,412 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - user.name is deprecated. Instead, use mapreduce.job.user.name 2014-06-25 20:40:09,412 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.jar is deprecated. Instead, use mapreduce.job.jar 2014-06-25 20:40:09,413 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class 2014-06-25 20:40:09,413 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class 2014-06-25 20:40:09,413 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class 2014-06-25 20:40:09,414 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.job.name is deprecated. Instead, use mapreduce.job.name 2014-06-25 20:40:09,414 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class 2014-06-25 20:40:09,414 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class 2014-06-25 20:40:09,414 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir 2014-06-25 20:40:09,414 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir 2014-06-25 20:40:09,415 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class 2014-06-25 20:40:09,416 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps 2014-06-25 20:40:09,416 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class 2014-06-25 20:40:09,416 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class 2014-06-25 20:40:09,416 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir 2014-06-25 20:40:09,502 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(477)) - Submitting tokens for job: job_1403723552088_0016 2014-06-25 20:40:09,651 INFO [main] impl.YarnClientImpl (YarnClientImpl.java:submitApplication(174)) - Submitted application application_1403723552088_0016 to ResourceManager at h1/192.168.46.32:8032 2014-06-25 20:40:09,683 INFO [main] mapreduce.Job (Job.java:submit(1272)) - The url to track the job: http://h1:8088/proxy/application_1403723552088_0016/ 2014-06-25 20:40:09,683 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1317)) - Running job: job_1403723552088_0016 2014-06-25 20:40:17,070 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1338)) - Job job_1403723552088_0016 running in uber mode : false 2014-06-25 20:40:17,072 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) - map 0% reduce 0% 2014-06-25 20:40:23,232 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) - map 100% reduce 0% 2014-06-25 20:40:30,273 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) - map 100% reduce 100% 2014-06-25 20:40:30,289 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1356)) - Job job_1403723552088_0016 completed successfully 2014-06-25 20:40:30,403 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1363)) - Counters: 43 File System Counters FILE: Number of bytes read=58 FILE: Number of bytes written=160123 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=136 HDFS: Number of bytes written=27 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=4398 Total time spent by all reduces in occupied slots (ms)=4263 Map-Reduce Framework Map input records=4 Map output records=4 Map output bytes=44 Map output materialized bytes=58 Input split bytes=98 Combine input records=0 Combine output records=0 Reduce input groups=3 Reduce shuffle bytes=58 Reduce input records=4 Reduce output records=3 Spilled Records=8 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=94 CPU time spent (ms)=980 Physical memory (bytes) snapshot=310431744 Virtual memory (bytes) snapshot=1681850368 Total committed heap usage (bytes)=136450048 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=38 File Output Format Counters Bytes Written=27
至此，我们已经可以成功的在无插件的环境里提交hadoop任务了，如果提交过程中，出现权限异常，可以在eclipse的run环境里配置，linux上安装hadoop的用户名即可，截图如下：
注意，一定是安装hadoop的用户，写成其他的用户，可能会导致没有权限访问HDFS上的数据，从而使提交的作业运行失败。