Eclipse作为一个常用的java IDE，其使用程度虽然比不上idea那么强大，但是对于习惯使用eclipse开发的人来说，也不失为一个可以选择的IDE。对于喜欢eclipse开发的人来说，就是想让他更加的智能化，更加的友好，比如开发mapreduce程序，我们可以利用hadoop-eclipse-plugin这个插件让eclipse能够可视化查看hdfs上的文件系统，并且可以创建mapreduce程序而不需要额外添加hadoop的jar包。
这里介绍eclipse4.12.0版本结合hadoop-eclipse-plugin-2.7.0插件来开发mapreduce程序。hadoop-eclipse-plugin插件是一个jar包，可以在网上下载，也可以自己通过源码编译，编译的话，需要ant,jdk,hadoop安装目录，以及hadoop环境变量HADOOP_HOME，具体教程可以看这篇文章。
eclipse版本信息：
hadoop-eclipse-plugin-2.7.0.jar：
1）、将插件放入eclipse安装目录的plugins目录下，然后重启eclipse即可，我们如果在Window-Preferences选项中看到了Hadoop Map/Reduce选项，说明我们已经安装成功了，注意，一定要找对hadoop与eclipse-hadoop-plugin插件版本对应关系，否则可能会出现启动ecipse而报错的问题，或者插件无法正常使用。
2）、插件有了，我们需要指定本机hadoop安装目录。Window->Preferences->Hadoop Map/Reduce->Hadoop install directory:这里指定本机hadoop安装目录，我这里是E:\apache-hadoop\hadoop-2.7.0
3）、插件安装成功了，我们可以在新建project向导的时候看到Map/Reduce Project选项，也可以在Window->show view下查看Map/Reduce Locations选项。
4）、为了能够方便看到hdfs的信息，我们新建一个mapreduce locations。这里需要填写准确的主机名和端口号，如果是hadoop-2.7.0版本，可以直接填写Host：虚拟机IP,Port：9000。一定要注意这里的端口信息。
这里填写完毕，点击保存，然后在面板上选择Project Explorer，我们可以看到多了DFS Locations选项，依次点击展开，我们会看到hdfs上的文件目录以及文件信息：
5）、新建mapreduce工程：File->New->Project：选择Map/Reduce Project。
项目创建完毕，结构如下，会自动加入本机hadoop安装目录下的hadoop相关依赖jar包，不用额外手动导入jar包，非常方便。
6）、编写简单的wordcount程序：
package com.xxx.hadoop.mapred;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
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;
/**
* 最简单的词频统计程序
*/
public class WordCountApp {
public static class WordCountMapper extends Mapper<Object, Text, Text, IntWritable>{
private IntWritable one = new IntWritable(1);
protected void map(Object key, Text value, Context context) throws java.io.IOException ,InterruptedException {
StringTokenizer tokenizer = new StringTokenizer(value.toString());
while(tokenizer.hasMoreTokens()) {
Text word = new Text(tokenizer.nextToken());
context.write(word, one);
}
};
}
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
private IntWritable result = new IntWritable();
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws java.io.IOException ,InterruptedException {
int sum = 0;
for(IntWritable value:values) {
sum += value.get();
}
result.set(sum);
context.write(key, result);
};
}
public static void main(String[] args) throws Exception {
System.setProperty("HADOOP_USER_NAME", "root");
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
String input = args[0];
String output = args[1];
job.setJarByClass(WordCountApp.class);
FileInputFormat.addInputPath(job, new Path(input));
FileOutputFormat.setOutputPath(job, new Path(output));
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true)?0:1);
}
}
7）、运行时，选择Run As->Run Configurations，在参数输入框中，添加如下参数，分别表示输入路径和输出路径。
8）、运行过程遇到的问题。
org.apache.hadoop.security.AccessControlException: Permission denied: user=hadoop, access=WRITE, inode="/user":root:supergroup:drwxr-xr-x
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:307)这个问题是权限问题导致的，解决办法有两种，一种是修改远程hadoop配置文件，hdfs-site.xml增加如下配置：
<property>
<name>dfs.permissions.enabled</name>
<value>false</value>
</property>修改配置，然后重启hadoop，再次运行程序，就不会报权限异常了，但是这种方式适合在测试环境中使用，一般生产环境，不会轻易的让一般用户随意切换到别的用户目录下进行写操作，因此，这种做法是一种折中的办法。真正的解决这类问题，其实很简单，就是让运行这个程序的用户(HADOOP_USER_NAME)为所需的用户即可，我们可以在程序中显式指定，比如我这里是提示需要root用户，那么可以这样来指定：
System.setProperty("HADOOP_USER_NAME", "root");
这句代码写在main函数中的第一行即可。
另外一个小问题，就是如果我们运行了一次程序之后，会生成输出文件夹目录，如果需要再次运行程序，这个目录如果存在，会报错，为了不让程序报错，我们需要在hdfs中删除这个目录，如：hdfs dfs -rmr /user/output
运行正确的日志打印信息：
2019-08-29 23:48:41,567 [main] [INFO ] org.apache.hadoop.conf.Configuration.deprecation session.id is deprecated. Instead, use dfs.metrics.session-id
2019-08-29 23:48:41,569 [main] [INFO ] org.apache.hadoop.metrics.jvm.JvmMetrics Initializing JVM Metrics with processName=JobTracker, sessionId=
2019-08-29 23:48:41,960 [main] [WARN ] org.apache.hadoop.mapreduce.JobResourceUploader Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2019-08-29 23:48:42,014 [main] [WARN ] org.apache.hadoop.mapreduce.JobResourceUploader No job jar file set. User classes may not be found. See Job or Job#setJar(String).
2019-08-29 23:48:42,063 [main] [INFO ] org.apache.hadoop.mapreduce.lib.input.FileInputFormat Total input paths to process : 1
2019-08-29 23:48:42,183 [main] [INFO ] org.apache.hadoop.mapreduce.JobSubmitter number of splits:1
2019-08-29 23:48:42,567 [main] [INFO ] org.apache.hadoop.mapreduce.JobSubmitter Submitting tokens for job: job_local1702410896_0001
2019-08-29 23:48:42,852 [main] [INFO ] org.apache.hadoop.mapreduce.Job The url to track the job: http://localhost:8080/
2019-08-29 23:48:42,854 [main] [INFO ] org.apache.hadoop.mapreduce.Job Running job: job_local1702410896_0001
2019-08-29 23:48:42,862 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner OutputCommitter set in config null
2019-08-29 23:48:42,870 [Thread-3] [INFO ] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter File Output Committer Algorithm version is 1
2019-08-29 23:48:42,873 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
2019-08-29 23:48:42,946 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Waiting for map tasks
2019-08-29 23:48:42,950 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Starting task: attempt_local1702410896_0001_m_000000_0
2019-08-29 23:48:42,992 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter File Output Committer Algorithm version is 1
2019-08-29 23:48:43,003 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.yarn.util.ProcfsBasedProcessTree ProcfsBasedProcessTree currently is supported only on Linux.
2019-08-29 23:48:43,053 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.Task Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@9057f5
2019-08-29 23:48:43,061 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask Processing split: hdfs://192.168.56.202:9000/user/root/wordcount.txt:0+85
2019-08-29 23:48:43,115 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask (EQUATOR) 0 kvi 26214396(104857584)
2019-08-29 23:48:43,116 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask mapreduce.task.io.sort.mb: 100
2019-08-29 23:48:43,116 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask soft limit at 83886080
2019-08-29 23:48:43,116 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask bufstart = 0; bufvoid = 104857600
2019-08-29 23:48:43,116 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask kvstart = 26214396; length = 6553600
2019-08-29 23:48:43,121 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
2019-08-29 23:48:43,463 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.LocalJobRunner
2019-08-29 23:48:43,466 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask Starting flush of map output
2019-08-29 23:48:43,466 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask Spilling map output
2019-08-29 23:48:43,466 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask bufstart = 0; bufend = 145; bufvoid = 104857600
2019-08-29 23:48:43,466 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask kvstart = 26214396(104857584); kvend = 26214340(104857360); length = 57/6553600
2019-08-29 23:48:43,484 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.MapTask Finished spill 0
2019-08-29 23:48:43,491 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.Task Task:attempt_local1702410896_0001_m_000000_0 is done. And is in the process of committing
2019-08-29 23:48:43,504 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.LocalJobRunner map
2019-08-29 23:48:43,505 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.Task Task 'attempt_local1702410896_0001_m_000000_0' done.
2019-08-29 23:48:43,505 [LocalJobRunner Map Task Executor #0] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Finishing task: attempt_local1702410896_0001_m_000000_0
2019-08-29 23:48:43,506 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner map task executor complete.
2019-08-29 23:48:43,509 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Waiting for reduce tasks
2019-08-29 23:48:43,510 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Starting task: attempt_local1702410896_0001_r_000000_0
2019-08-29 23:48:43,520 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter File Output Committer Algorithm version is 1
2019-08-29 23:48:43,520 [pool-6-thread-1] [INFO ] org.apache.hadoop.yarn.util.ProcfsBasedProcessTree ProcfsBasedProcessTree currently is supported only on Linux.
2019-08-29 23:48:43,554 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Task Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@597152e3
2019-08-29 23:48:43,558 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.ReduceTask Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1b9caf3f
2019-08-29 23:48:43,575 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl MergerManager: memoryLimit=1265788544, maxSingleShuffleLimit=316447136, mergeThreshold=835420480, ioSortFactor=10, memToMemMergeOutputsThreshold=10
2019-08-29 23:48:43,578 [EventFetcher for fetching Map Completion Events] [INFO ] org.apache.hadoop.mapreduce.task.reduce.EventFetcher attempt_local1702410896_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
2019-08-29 23:48:43,632 [localfetcher#1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.LocalFetcher localfetcher#1 about to shuffle output of map attempt_local1702410896_0001_m_000000_0 decomp: 177 len: 181 to MEMORY
2019-08-29 23:48:43,647 [localfetcher#1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput Read 177 bytes from map-output for attempt_local1702410896_0001_m_000000_0
2019-08-29 23:48:43,671 [localfetcher#1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl closeInMemoryFile -> map-output of size: 177, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->177
2019-08-29 23:48:43,673 [EventFetcher for fetching Map Completion Events] [INFO ] org.apache.hadoop.mapreduce.task.reduce.EventFetcher EventFetcher is interrupted.. Returning
2019-08-29 23:48:43,675 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner 1 / 1 copied.
2019-08-29 23:48:43,676 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
2019-08-29 23:48:43,687 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Merger Merging 1 sorted segments
2019-08-29 23:48:43,688 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Merger Down to the last merge-pass, with 1 segments left of total size: 173 bytes
2019-08-29 23:48:43,689 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl Merged 1 segments, 177 bytes to disk to satisfy reduce memory limit
2019-08-29 23:48:43,690 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl Merging 1 files, 181 bytes from disk
2019-08-29 23:48:43,691 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl Merging 0 segments, 0 bytes from memory into reduce
2019-08-29 23:48:43,691 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Merger Merging 1 sorted segments
2019-08-29 23:48:43,693 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Merger Down to the last merge-pass, with 1 segments left of total size: 173 bytes
2019-08-29 23:48:43,693 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner 1 / 1 copied.
2019-08-29 23:48:43,723 [pool-6-thread-1] [INFO ] org.apache.hadoop.conf.Configuration.deprecation mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
2019-08-29 23:48:43,857 [main] [INFO ] org.apache.hadoop.mapreduce.Job Job job_local1702410896_0001 running in uber mode : false
2019-08-29 23:48:43,859 [main] [INFO ] org.apache.hadoop.mapreduce.Job map 100% reduce 0%
2019-08-29 23:48:44,324 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Task Task:attempt_local1702410896_0001_r_000000_0 is done. And is in the process of committing
2019-08-29 23:48:44,340 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner 1 / 1 copied.
2019-08-29 23:48:44,340 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Task Task attempt_local1702410896_0001_r_000000_0 is allowed to commit now
2019-08-29 23:48:44,379 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter Saved output of task 'attempt_local1702410896_0001_r_000000_0' to hdfs://192.168.56.202:9000/user/output/_temporary/0/task_local1702410896_0001_r_000000
2019-08-29 23:48:44,380 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner reduce > reduce
2019-08-29 23:48:44,380 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.Task Task 'attempt_local1702410896_0001_r_000000_0' done.
2019-08-29 23:48:44,380 [pool-6-thread-1] [INFO ] org.apache.hadoop.mapred.LocalJobRunner Finishing task: attempt_local1702410896_0001_r_000000_0
2019-08-29 23:48:44,380 [Thread-3] [INFO ] org.apache.hadoop.mapred.LocalJobRunner reduce task executor complete.
2019-08-29 23:48:44,860 [main] [INFO ] org.apache.hadoop.mapreduce.Job map 100% reduce 100%
2019-08-29 23:48:44,861 [main] [INFO ] org.apache.hadoop.mapreduce.Job Job job_local1702410896_0001 completed successfully
2019-08-29 23:48:44,886 [main] [INFO ] org.apache.hadoop.mapreduce.Job Counters: 35
File System Counters
FILE: Number of bytes read=726
FILE: Number of bytes written=549119
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=170
HDFS: Number of bytes written=106
HDFS: Number of read operations=13
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Map-Reduce Framework
Map input records=2
Map output records=15
Map output bytes=145
Map output materialized bytes=181
Input split bytes=115
Combine input records=0
Combine output records=0
Reduce input groups=13
Reduce shuffle bytes=181
Reduce input records=15
Reduce output records=13
Spilled Records=30
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=18
Total committed heap usage (bytes)=494927872
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=85
File Output Format Counters
Bytes Written=106
9）、运行成功之后，查看结果，这里我们直接在eclipse的Project Explorer视图中点击我们的输出目录，一般包含两个文件，一个是_SUCCESS，另一个就是输出内容的文件part-r-00000。
至此，一个简单的wordcount程序就通过eclipse以及他的插件帮助我们在本地运行成功了。而无需反复打包，然后在hadoop部署机器上运行。
这里有几个注意的问题：
hadoop在本机的安装，可以是真实的安装，也可以是直接下载一个hadoop预编译版本，不用在本机运行，只需要解压，然后在环境变量中配置HADOOP_HOME等环境变量。个人理解这个hadoop的安装是为了设置windows->preferences->hadoop install，最后，我们创建mapreduce工程的时候，自动添加了这个hadoop安装目录下对应的jar包。
另外一个问题，就是我们直接在Eclipse里运行一次mapreduce程序之后，还想再次运行，那么需要注意的是，需要手动删除输出目录，为了解决这个问题，我们可以在程序运行之前先检查是否存在输出目录，如果存在，就删除。这里还是以wordcount为例，我们可以这么修改main函数。
public static void main(String[] args) throws Exception {
String input = args[0];
String output = args[1];
System.setProperty("HADOOP_USER_NAME", "root");
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://192.168.56.202:9000");
FileSystem fs = FileSystem.get(conf);
boolean exists = fs.exists(new Path(output));
if(exists) {
fs.delete(new Path(output),true);
}
Job job = Job.getInstance(conf);
job.setJarByClass(WordCountApp.class);
FileInputFormat.addInputPath(job, new Path(input));
FileOutputFormat.setOutputPath(job, new Path(output));
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true)?0:1);
}