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网上有一些都是在 Linux 下使用安装 Eclipse 来进行 Hadoop 应用开发，但是大部分 Java 程序员对 Linux 系统不是那么熟悉，所以需要在 Windows 下开发 Hadoop 程序，所以经过试验，总结了下如何在 Windows 下使用 Eclipse 来开发 Hadoop 程序代码。
Hadoop2.5.2 新特性 http://www.linuxidc.com/Linux/2014-11/109814.htm
CentOS 安装和配置 Hadoop2.2.0 http://www.linuxidc.com/Linux/2014-01/94685.htm
Ubuntu 13.04 上搭建 Hadoop 环境 http://www.linuxidc.com/Linux/2013-06/86106.htm
Ubuntu 12.10 +Hadoop 1.2.1 版本集群配置 http://www.linuxidc.com/Linux/2013-09/90600.htm
Ubuntu 上搭建 Hadoop 环境（单机模式 + 伪分布模式）http://www.linuxidc.com/Linux/2013-01/77681.htm
Ubuntu 下 Hadoop 环境的配置 http://www.linuxidc.com/Linux/2012-11/74539.htm
单机版搭建 Hadoop 环境图文教程详解 http://www.linuxidc.com/Linux/2012-02/53927.htm
搭建 Hadoop 环境（在 Winodws 环境下用虚拟机虚拟两个 Ubuntu 系统进行搭建）http://www.linuxidc.com/Linux/2011-12/48894.htm
1、需要下载 hadoop 的专门插件 jar 包
hadoop 版本为 2.3.0，hadoop 集群搭建在 centos6x 上面，插件包下载地址为
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FTP 地址：ftp://ftp1.linuxidc.com
用户名：ftp1.linuxidc.com
密码：www.linuxidc.com
在 2014 年 LinuxIDC.com\12 月 \Hadoop 在 Windows7 操作系统下使用 Eclipse 来搭建 Hadoop 开发环境
下载方法见 http://www.linuxidc.com/Linux/2013-10/91140.htm
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jar 包名字为 hadoop-eclipse-plugin-2.3.0，可以适用于 hadoop2x 系列软件版本。
2、把插件包放到 eclipse/plugins 目录下
为了以后方便，我这里把尽可能多的 jar 包都放进来了，如下图所示：
3、重启 eclipse，配置 Hadoop installation directory
如果插件安装成功，打开 Windows—Preferences 后，在窗口左侧会有 Hadoop Map/Reduce 选项，点击此选项，在窗口右侧设置 Hadoop 安装路径。
4、配置 Map/Reduce Locations
打开 Windows–>Open Perspective–>Other
选择 Map/Reduce，点击 OK，在右下方看到有个 Map/Reduce Locations 的图标，如下图所示：
点击 Map/Reduce Location 选项卡，点击右边小象图标，打开 Hadoop Location 配置窗口：
输入 Location Name，任意名称即可. 配置 Map/Reduce Master 和 DFS Mastrer，Host 和 Port 配置成与 core-site.xml 的设置一致即可。
去找 core-site.xml 配置：
fs.default.name hdfs://name01:9000
在界面配置如下：
点击 ”Finish” 按钮，关闭窗口。点击左侧的 DFSLocations—>myhadoop（上一步配置的 location name)，如能看到 user，表示安装成功，但是进去看到报错信息：Error: Permission denied: user=root,access=READ_EXECUTE,inode=”/tmp”;hadoop:supergroup:drwx———，如下图所示：
应该是权限问题：把 /tmp/ 目录下面所有的关于 hadoop 的文件夹设置成 hadoop 用户所有然后分配授予 777 权限。
cd /tmp/
chmod 777 /tmp/
chown -R hadoop.hadoop /tmp/hsperfdata_root
之后重新连接打开 DFS Locations 就显示正常了。
Map/Reduce Master (此处为 Hadoop 集群的 Map/Reduce 地址，应该和 mapred-site.xml 中的 mapred.job.tracker 设置相同)
（1）：点击报错：
An internal error occurred during: “Connecting to DFS hadoopname01”.
java.net.UnknownHostException: name01
直接在 hostname 那一栏里面设置 ip 地址为：192.168.52.128，即可，这样就正常打开了，如下图所示：
5、新建 WordCount 项目
File—>Project，选择 Map/Reduce Project，输入项目名称 WordCount 等。
在 WordCount 项目里新建 class，名称为 WordCount，报错代码如下：Invalid Hadoop Runtime specified; please click ‘Configure Hadoop install directory’ or fill in library location input field，报错原因是目录选择不对，不能选择在跟目录 E:\hadoop 下，换成 E:\u\hadoop\ 就可以了，如下所示：
一路下一步过去，点击 Finished 按钮，完成工程创建，Eclipse 控制台下面出现如下信息：
14-12-9 下午 04 时 03 分 10 秒: Eclipse is running in a JRE, but a JDK is required
Some Maven plugins may not work when importing projects or updating source folders.
14-12-9 下午 04 时 03 分 13 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Updating index central|http://repo1.maven.org/maven2
14-12-9 下午 04 时 04 分 10 秒: Updated index for central|http://repo1.maven.org/maven2
更多详情见请继续阅读下一页的精彩内容 ：http://www.linuxidc.com/Linux/2014-12/111061p2.htm
6，Lib 包导入：
需要添加的 Hadoop 相应 jar 包有：
/hadoop-2.3.0/share/hadoop/common 下所有 jar 包，及里面的 lib 目录下所有 jar 包，
/hadoop-2.3.0/share/hadoop/hdfs 下所有 jar 包，不包括里面 lib 下的 jar 包，
/hadoop-2.3.0/share/hadoop/mapreduce 下所有 jar 包，不包括里面 lib 下的 jar 包，
/hadoop-2.3.0/share/hadoop/yarn 下所有 jar 包，不包括里面 lib 下的 jar 包，
大概 18 个 jar 包左右。
7，Eclipse 直接提交 mapreduce 任务所需要环境配置代码如下所示：
package wc;
import java.io.IOException;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class W2 {
public static class TokenizerMapper extends
Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration(); System.setProperty(\
8、运行
8.1、在 HDFS 上创建目录 input
[hadoop@name01 hadoop-2.3.0]$ hadoop fs -ls /
[hadoop@name01 hadoop-2.3.0]$ hadoop fs -mkdir input
mkdir: `input’: No such file or directory
[hadoop@name01 hadoop-2.3.0]$ PS：fs 需要全目录的方式来创建文件夹
如果 Apache hadoop 版本是 0.x 或者 1.x,
bin/hadoop hdfs fs -mkdir -p /in
bin/hadoop hdfs fs -put /home/du/input /in
如果 Apache hadoop 版本是 2.x.
bin/hdfs dfs -mkdir -p /in
bin/hdfs dfs -put /home/du/input /in
如果是发行版的 hadoop, 比如 Cloudera CDH,IBM BI,Hortonworks HDP 则第一种命令即可。要注意创建目录的全路径。另外 hdfs 的根目录是 /
2、拷贝本地 README.txt 到 HDFS 的 input 里
[hadoop@name01 hadoop-2.3.0]$ find . -name README.txt
./share/doc/hadoop/common/README.txt
[hadoop@name01 ~]$ hadoop fs -copyFromLocal ./src/hadoop-2.3.0/share/doc/hadoop/common/README.txt /data/input
[hadoop@name01 ~]$
[hadoop@name01 ~]$ hadoop fs -ls /
Found 2 items
drwxr-xr-x – hadoop supergroup 0 2014-12-15 23:34 /data
-rw-r–r– 3 hadoop supergroup 88 2014-08-26 02:21 /input
You have new mail in /var/spool/mail/root
[hadoop@name01 ~]$
3，运行 hadoop 结束后，查看输出结果
（1），直接在 hadoop 服务器上面查看
[hadoop@name01 ~]$ hadoop fs -ls /data/
Found 2 items
drwxr-xr-x – hadoop supergroup 0 2014-12-15 23:29 /data/input
drwxr-xr-x – hadoop supergroup 0 2014-12-15 23:34 /data/output
[hadoop@name01 ~]$
（2），去 Eclipse 下查看
（3），在控制台上查看信息
2014-12-16 15:34:01,303 INFO [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(996)) – session.id is deprecated. Instead, use dfs.metrics.session-id
2014-12-16 15:34:01,309 INFO [main] jvm.JvmMetrics (JvmMetrics.java:init(76)) – Initializing JVM Metrics with processName=JobTracker, sessionId=
2014-12-16 15:34:02,047 INFO [main] input.FileInputFormat (FileInputFormat.java:listStatus(287)) – Total input paths to process : 1
2014-12-16 15:34:02,120 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(396)) – number of splits:1
2014-12-16 15:34:02,323 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(479)) – Submitting tokens for job: job_local1764589720_0001
2014-12-16 15:34:02,367 WARN [main] conf.Configuration (Configuration.java:loadProperty(2345)) – file:/tmp/hadoop-hadoop/mapred/staging/hadoop1764589720/.staging/job_local1764589720_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval; Ignoring.
2014-12-16 15:34:02,368 WARN [main] conf.Configuration (Configuration.java:loadProperty(2345)) – file:/tmp/hadoop-hadoop/mapred/staging/hadoop1764589720/.staging/job_local1764589720_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts; Ignoring.
2014-12-16 15:34:02,682 WARN [main] conf.Configuration (Configuration.java:loadProperty(2345)) – file:/tmp/hadoop-hadoop/mapred/local/localRunner/hadoop/job_local1764589720_0001/job_local1764589720_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval; Ignoring.
2014-12-16 15:34:02,682 WARN [main] conf.Configuration (Configuration.java:loadProperty(2345)) – file:/tmp/hadoop-hadoop/mapred/local/localRunner/hadoop/job_local1764589720_0001/job_local1764589720_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts; Ignoring.
2014-12-16 15:34:02,703 INFO [main] mapreduce.Job (Job.java:submit(1289)) – The url to track the job: http://localhost:8080/
2014-12-16 15:34:02,704 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1334)) – Running job: job_local1764589720_0001
2014-12-16 15:34:02,707 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(471)) – OutputCommitter set in config null
2014-12-16 15:34:02,719 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(489)) – OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
2014-12-16 15:34:02,853 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(448)) – Waiting for map tasks
2014-12-16 15:34:02,857 INFO [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:run(224)) – Starting task: attempt_local1764589720_0001_m_000000_0
2014-12-16 15:34:02,919 INFO [LocalJobRunner Map Task Executor #0] util.ProcfsBasedProcessTree (ProcfsBasedProcessTree.java:isAvailable(129)) – ProcfsBasedProcessTree currently is supported only on Linux.
2014-12-16 15:34:03,281 INFO [LocalJobRunner Map Task Executor #0] mapred.Task (Task.java:initialize(581)) – Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@2e1022ec
2014-12-16 15:34:03,287 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:runNewMapper(733)) – Processing split: hdfs://192.168.52.128:9000/data/input/README.txt:0+1366
2014-12-16 15:34:03,304 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:createSortingCollector(388)) – Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
2014-12-16 15:34:03,340 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:setEquator(1181)) – (EQUATOR) 0 kvi 26214396(104857584)
2014-12-16 15:34:03,341 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(975)) – mapreduce.task.io.sort.mb: 100
2014-12-16 15:34:03,341 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(976)) – soft limit at 83886080
2014-12-16 15:34:03,341 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(977)) – bufstart = 0; bufvoid = 104857600
2014-12-16 15:34:03,341 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(978)) – kvstart = 26214396; length = 6553600
2014-12-16 15:34:03,708 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1355)) – Job job_local1764589720_0001 running in uber mode : false
2014-12-16 15:34:03,710 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1362)) – map 0% reduce 0%
2014-12-16 15:34:04,121 INFO [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) –
2014-12-16 15:34:04,128 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:flush(1435)) – Starting flush of map output
2014-12-16 15:34:04,128 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:flush(1453)) – Spilling map output
2014-12-16 15:34:04,128 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:flush(1454)) – bufstart = 0; bufend = 2055; bufvoid = 104857600
2014-12-16 15:34:04,128 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:flush(1456)) – kvstart = 26214396(104857584); kvend = 26213684(104854736); length = 713/6553600
2014-12-16 15:34:04,179 INFO [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:sortAndSpill(1639)) – Finished spill 0
2014-12-16 15:34:04,194 INFO [LocalJobRunner Map Task Executor #0] mapred.Task (Task.java:done(995)) – Task:attempt_local1764589720_0001_m_000000_0 is done. And is in the process of committing
2014-12-16 15:34:04,207 INFO [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) – map
2014-12-16 15:34:04,208 INFO [LocalJobRunner Map Task Executor #0] mapred.Task (Task.java:sendDone(1115)) – Task \’attempt_local1764589720_0001_m_000000_0\’ done.
2014-12-16 15:34:04,208 INFO [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:run(249)) – Finishing task: attempt_local1764589720_0001_m_000000_0
2014-12-16 15:34:04,208 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(456)) – map task executor complete.
2014-12-16 15:34:04,211 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(448)) – Waiting for reduce tasks
2014-12-16 15:34:04,211 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:run(302)) – Starting task: attempt_local1764589720_0001_r_000000_0
2014-12-16 15:34:04,221 INFO [pool-6-thread-1] util.ProcfsBasedProcessTree (ProcfsBasedProcessTree.java:isAvailable(129)) – ProcfsBasedProcessTree currently is supported only on Linux.
2014-12-16 15:34:04,478 INFO [pool-6-thread-1] mapred.Task (Task.java:initialize(581)) – Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@36154615
2014-12-16 15:34:04,483 INFO [pool-6-thread-1] mapred.ReduceTask (ReduceTask.java:run(362)) – Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@e2b02a3
2014-12-16 15:34:04,500 INFO [pool-6-thread-1] reduce.MergeManagerImpl (MergeManagerImpl.java:<init>(193)) – MergerManager: memoryLimit=949983616, maxSingleShuffleLimit=237495904, mergeThreshold=626989184, ioSortFactor=10, memToMemMergeOutputsThreshold=10
2014-12-16 15:34:04,503 INFO [EventFetcher for fetching Map Completion Events] reduce.EventFetcher (EventFetcher.java:run(61)) – attempt_local1764589720_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
2014-12-16 15:34:04,543 INFO [localfetcher#1] reduce.LocalFetcher (LocalFetcher.java:copyMapOutput(140)) – localfetcher#1 about to shuffle output of map attempt_local1764589720_0001_m_000000_0 decomp: 1832 len: 1836 to MEMORY
2014-12-16 15:34:04,548 INFO [localfetcher#1] reduce.InMemoryMapOutput (InMemoryMapOutput.java:shuffle(100)) – Read 1832 bytes from map-output for attempt_local1764589720_0001_m_000000_0
2014-12-16 15:34:04,553 INFO [localfetcher#1] reduce.MergeManagerImpl (MergeManagerImpl.java:closeInMemoryFile(307)) – closeInMemoryFile -> map-output of size: 1832, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->1832
2014-12-16 15:34:04,564 INFO [EventFetcher for fetching Map Completion Events] reduce.EventFetcher (EventFetcher.java:run(76)) – EventFetcher is interrupted.. Returning
2014-12-16 15:34:04,566 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) – 1 / 1 copied.
2014-12-16 15:34:04,566 INFO [pool-6-thread-1] reduce.MergeManagerImpl (MergeManagerImpl.java:finalMerge(667)) – finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
2014-12-16 15:34:04,585 INFO [pool-6-thread-1] mapred.Merger (Merger.java:merge(589)) – Merging 1 sorted segments
2014-12-16 15:34:04,585 INFO [pool-6-thread-1] mapred.Merger (Merger.java:merge(688)) – Down to the last merge-pass, with 1 segments left of total size: 1823 bytes
2014-12-16 15:34:04,605 INFO [pool-6-thread-1] reduce.MergeManagerImpl (MergeManagerImpl.java:finalMerge(742)) – Merged 1 segments, 1832 bytes to disk to satisfy reduce memory limit
2014-12-16 15:34:04,605 INFO [pool-6-thread-1] reduce.MergeManagerImpl (MergeManagerImpl.java:finalMerge(772)) – Merging 1 files, 1836 bytes from disk
2014-12-16 15:34:04,606 INFO [pool-6-thread-1] reduce.MergeManagerImpl (MergeManagerImpl.java:finalMerge(787)) – Merging 0 segments, 0 bytes from memory into reduce
2014-12-16 15:34:04,607 INFO [pool-6-thread-1] mapred.Merger (Merger.java:merge(589)) – Merging 1 sorted segments
2014-12-16 15:34:04,608 INFO [pool-6-thread-1] mapred.Merger (Merger.java:merge(688)) – Down to the last merge-pass, with 1 segments left of total size: 1823 bytes
2014-12-16 15:34:04,608 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) – 1 / 1 copied.
2014-12-16 15:34:04,643 INFO [pool-6-thread-1] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(996)) – mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
2014-12-16 15:34:04,714 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1362)) – map 100% reduce 0%
2014-12-16 15:34:04,842 INFO [pool-6-thread-1] mapred.Task (Task.java:done(995)) – Task:attempt_local1764589720_0001_r_000000_0 is done. And is in the process of committing
2014-12-16 15:34:04,850 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) – 1 / 1 copied.
2014-12-16 15:34:04,850 INFO [pool-6-thread-1] mapred.Task (Task.java:commit(1156)) – Task attempt_local1764589720_0001_r_000000_0 is allowed to commit now
2014-12-16 15:34:04,881 INFO [pool-6-thread-1] output.FileOutputCommitter (FileOutputCommitter.java:commitTask(439)) – Saved output of task \’attempt_local1764589720_0001_r_000000_0\’ to hdfs://192.168.52.128:9000/data/output/_temporary/0/task_local1764589720_0001_r_000000
2014-12-16 15:34:04,884 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:statusUpdate(591)) – reduce > reduce
2014-12-16 15:34:04,884 INFO [pool-6-thread-1] mapred.Task (Task.java:sendDone(1115)) – Task \’attempt_local1764589720_0001_r_000000_0\’ done.
2014-12-16 15:34:04,885 INFO [pool-6-thread-1] mapred.LocalJobRunner (LocalJobRunner.java:run(325)) – Finishing task: attempt_local1764589720_0001_r_000000_0
2014-12-16 15:34:04,885 INFO [Thread-4] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(456)) – reduce task executor complete.
2014-12-16 15:34:05,714 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1362)) – map 100% reduce 100%
2014-12-16 15:34:05,714 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1373)) – Job job_local1764589720_0001 completed successfully
2014-12-16 15:34:05,733 INFO [main] mapreduce.Job (Job.java:monitorAndPrintJob(1380)) – Counters: 38
File System Counters
FILE: Number of bytes read=34542
FILE: Number of bytes written=470650
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2732
HDFS: Number of bytes written=1306
HDFS: Number of read operations=15
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Map-Reduce Framework
Map input records=31
Map output records=179
Map output bytes=2055
Map output materialized bytes=1836
Input split bytes=113
Combine input records=179
Combine output records=131
Reduce input groups=131
Reduce shuffle bytes=1836
Reduce input records=131
Reduce output records=131
Spilled Records=262
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=13
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=440664064
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=1366
File Output Format Counters
Bytes Written=1306
更多 Hadoop 相关信息见 Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=13
网上有一些都是在 Linux 下使用安装 Eclipse 来进行 Hadoop 应用开发，但是大部分 Java 程序员对 Linux 系统不是那么熟悉，所以需要在 Windows 下开发 Hadoop 程序，所以经过试验，总结了下如何在 Windows 下使用 Eclipse 来开发 Hadoop 程序代码。
Hadoop2.5.2 新特性 http://www.linuxidc.com/Linux/2014-11/109814.htm
CentOS 安装和配置 Hadoop2.2.0 http://www.linuxidc.com/Linux/2014-01/94685.htm
Ubuntu 13.04 上搭建 Hadoop 环境 http://www.linuxidc.com/Linux/2013-06/86106.htm
Ubuntu 12.10 +Hadoop 1.2.1 版本集群配置 http://www.linuxidc.com/Linux/2013-09/90600.htm
Ubuntu 上搭建 Hadoop 环境（单机模式 + 伪分布模式）http://www.linuxidc.com/Linux/2013-01/77681.htm
Ubuntu 下 Hadoop 环境的配置 http://www.linuxidc.com/Linux/2012-11/74539.htm
单机版搭建 Hadoop 环境图文教程详解 http://www.linuxidc.com/Linux/2012-02/53927.htm
搭建 Hadoop 环境（在 Winodws 环境下用虚拟机虚拟两个 Ubuntu 系统进行搭建）http://www.linuxidc.com/Linux/2011-12/48894.htm
1、需要下载 hadoop 的专门插件 jar 包
hadoop 版本为 2.3.0，hadoop 集群搭建在 centos6x 上面，插件包下载地址为
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FTP 地址：ftp://ftp1.linuxidc.com
用户名：ftp1.linuxidc.com
密码：www.linuxidc.com
在 2014 年 LinuxIDC.com\12 月 \Hadoop 在 Windows7 操作系统下使用 Eclipse 来搭建 Hadoop 开发环境
下载方法见 http://www.linuxidc.com/Linux/2013-10/91140.htm
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jar 包名字为 hadoop-eclipse-plugin-2.3.0，可以适用于 hadoop2x 系列软件版本。
2、把插件包放到 eclipse/plugins 目录下
为了以后方便，我这里把尽可能多的 jar 包都放进来了，如下图所示：
3、重启 eclipse，配置 Hadoop installation directory
如果插件安装成功，打开 Windows—Preferences 后，在窗口左侧会有 Hadoop Map/Reduce 选项，点击此选项，在窗口右侧设置 Hadoop 安装路径。
4、配置 Map/Reduce Locations
打开 Windows–>Open Perspective–>Other
选择 Map/Reduce，点击 OK，在右下方看到有个 Map/Reduce Locations 的图标，如下图所示：
点击 Map/Reduce Location 选项卡，点击右边小象图标，打开 Hadoop Location 配置窗口：
输入 Location Name，任意名称即可. 配置 Map/Reduce Master 和 DFS Mastrer，Host 和 Port 配置成与 core-site.xml 的设置一致即可。
去找 core-site.xml 配置：
fs.default.name hdfs://name01:9000
在界面配置如下：
点击 ”Finish” 按钮，关闭窗口。点击左侧的 DFSLocations—>myhadoop（上一步配置的 location name)，如能看到 user，表示安装成功，但是进去看到报错信息：Error: Permission denied: user=root,access=READ_EXECUTE,inode=”/tmp”;hadoop:supergroup:drwx———，如下图所示：
应该是权限问题：把 /tmp/ 目录下面所有的关于 hadoop 的文件夹设置成 hadoop 用户所有然后分配授予 777 权限。
cd /tmp/
chmod 777 /tmp/
chown -R hadoop.hadoop /tmp/hsperfdata_root
之后重新连接打开 DFS Locations 就显示正常了。
Map/Reduce Master (此处为 Hadoop 集群的 Map/Reduce 地址，应该和 mapred-site.xml 中的 mapred.job.tracker 设置相同)
（1）：点击报错：
An internal error occurred during: “Connecting to DFS hadoopname01”.
java.net.UnknownHostException: name01
直接在 hostname 那一栏里面设置 ip 地址为：192.168.52.128，即可，这样就正常打开了，如下图所示：
5、新建 WordCount 项目
File—>Project，选择 Map/Reduce Project，输入项目名称 WordCount 等。
在 WordCount 项目里新建 class，名称为 WordCount，报错代码如下：Invalid Hadoop Runtime specified; please click ‘Configure Hadoop install directory’ or fill in library location input field，报错原因是目录选择不对，不能选择在跟目录 E:\hadoop 下，换成 E:\u\hadoop\ 就可以了，如下所示：
一路下一步过去，点击 Finished 按钮，完成工程创建，Eclipse 控制台下面出现如下信息：
14-12-9 下午 04 时 03 分 10 秒: Eclipse is running in a JRE, but a JDK is required
Some Maven plugins may not work when importing projects or updating source folders.
14-12-9 下午 04 时 03 分 13 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Refreshing [/WordCount/pom.xml]
14-12-9 下午 04 时 03 分 14 秒: Updating index central|http://repo1.maven.org/maven2
14-12-9 下午 04 时 04 分 10 秒: Updated index for central|http://repo1.maven.org/maven2
更多详情见请继续阅读下一页的精彩内容 ：http://www.linuxidc.com/Linux/2014-12/111061p2.htm
