编译hadoop eclipse的插件（hadoop1.0）

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在hadoop-1.0中，不像0.20.2版本，有现成的eclipse-plugin源码包，而是在HADOOP_HOME/src/contrib/eclipse-plugin目录下放置了eclipse插件的源码，这篇文章，我想详细记录一下自己是如何编译此源码生成适用于Hadoop1.0的eclipse插件

1、安装环境

操作系统：Ubuntu14.4
软件：
eclipse
java
Hadoop 1.0

2、编译步骤

（1）首先下载ant与ivy的安装包

将安装包解压缩到指定的目录，然后将ivy包中的ivy-2.2.0.jar包放到ant安装目录的lib目录下，然后在/etc/profile中添加以下内容以设置配置环境：

export ANT_HOME=/home/wu/opt/apache-ant-1.8.3
export PATH=”$ANT_HOME/bin:$PATH”

（2）终端转到hadoop安装目录下，执行ant compile，结果如下：

……………………

compile:
[echo] contrib: vaidya
[javac] /home/wu/opt/hadoop-1.0.1/src/contrib/build-contrib.xml:185: warning: ‘includeantruntime’ was not set, defaulting to build.sysclasspath=last; set to false for repeatable builds
[javac] Compiling 14 source files to /home/wu/opt/hadoop-1.0.1/build/contrib/vaidya/classes
[javac] Note: /home/wu/opt/hadoop-1.0.1/src/contrib/vaidya/src/java/org/apache/hadoop/vaidya/statistics/job/JobStatistics.java uses unchecked or unsafe operations.
[javac] Note: Recompile with -Xlint:unchecked for details.

compile-ant-tasks:
[javac] /home/wu/opt/hadoop-1.0.1/build.xml:2170: warning: ‘includeantruntime’ was not set, defaulting to build.sysclasspath=last; set to false for repeatable builds
[javac] Compiling 5 source files to /home/wu/opt/hadoop-1.0.1/build/ant

compile:

BUILD SUCCESSFUL

Total time: 12 minutes 29 seconds

可以看到编译成功！花的时间比较长，可以泡壶茶休息一下～～

（3）再将终端定位到HADOOP_HOME/src/contrib/eclipse-plugin，然后执行下面的命令：

ant -Declipse.home=/home/wu/opt/eclipse -Dversion=1.0.1 jar

编译完成后就可以找到eclipse插件了

3、安装步骤

（1）伪分布式的配置过程也很简单，只需要修改几个文件，在代码的conf文件夹内，就可以找到下面几个配置文件，具体过程我就不多说了，这里列出我的配置：

core-site.xml

<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/home/wu/hadoop-0.20.2/tmp</value>
</property>
</configuration>

hdfs-site.xml

<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>

mapred-site.xml

<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
<property>
<name>mapred.job.tracker</name>
<value>hdfs://localhost:9001</value>
</property>
</configuration>

进入conf文件夹，修改配置文件：hadoop-env.sh，将里面的JAVA_HOME注释打开，并把里面的地址配置正确

（2）运行hadoop

进入hadoop目录，首次运行，需要格式化文件系统，输入命令：

bin/hadoop namenode -format

输入命令，启动所有进出：

bin/start-all.sh

关闭hadoop可以用：

bin/stop-all.sh

最后验证hadoop是否安装成功，打开浏览器，分别输入：

http://localhost:50030/ （MapReduce的web页面）

http://localhost:50070/ （HDFS的web页面）

用jps命令看一下有几个java进程在运行，如果是下面几个就正常了

wu@ubuntu:~/opt/hadoop-1.0.1$ jps
4113 SecondaryNameNode
4318 TaskTracker
3984 DataNode
3429
3803 NameNode
4187 JobTracker
4415 Jps

系统启动正常后，现在来跑个程序:

$mkdir input
$cd input
$echo "hello world">test1.txt
$echo "hello hadoop">test2.txt
$cd ..
$bin/hadoop dfs -put input in
$bin/hadoop jar hadoop-examples-1.0.1.jar wordcount in out
$bin/hadoop dfs -cat out/*

出现一长串的运行过程：

****hdfs://localhost:9000/user/wu/in
15/05/29 10:51:41 INFO input.FileInputFormat: Total input paths to process : 2
15/05/29 10:51:42 INFO mapred.JobClient: Running job: job_201505291029_0001
15/05/29 10:51:43 INFO mapred.JobClient: map 0% reduce 0%
15/05/29 10:52:13 INFO mapred.JobClient: map 100% reduce 0%
15/05/29 10:52:34 INFO mapred.JobClient: map 100% reduce 100%
15/05/29 10:52:39 INFO mapred.JobClient: Job complete: job_201505291029_0001
15/05/29 10:52:39 INFO mapred.JobClient: Counters: 29
15/05/29 10:52:39 INFO mapred.JobClient: Job Counters
15/05/29 10:52:39 INFO mapred.JobClient: Launched reduce tasks=1
15/05/29 10:52:39 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=43724
15/05/29 10:52:39 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
15/05/29 10:52:39 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
15/05/29 10:52:39 INFO mapred.JobClient: Launched map tasks=2
15/05/29 10:52:39 INFO mapred.JobClient: Data-local map tasks=2
15/05/29 10:52:39 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=20072
15/05/29 10:52:39 INFO mapred.JobClient: File Output Format Counters
15/05/29 10:52:39 INFO mapred.JobClient: Bytes Written=25
15/05/29 10:52:39 INFO mapred.JobClient: FileSystemCounters
15/05/29 10:52:39 INFO mapred.JobClient: FILE_BYTES_READ=55
15/05/29 10:52:39 INFO mapred.JobClient: HDFS_BYTES_READ=239
15/05/29 10:52:39 INFO mapred.JobClient: FILE_BYTES_WRITTEN=64837
15/05/29 10:52:39 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=25
15/05/29 10:52:39 INFO mapred.JobClient: File Input Format Counters
15/05/29 10:52:39 INFO mapred.JobClient: Bytes Read=25
15/05/29 10:52:39 INFO mapred.JobClient: Map-Reduce Framework
15/05/29 10:52:39 INFO mapred.JobClient: Map output materialized bytes=61
15/05/29 10:52:39 INFO mapred.JobClient: Map input records=2
15/05/29 10:52:39 INFO mapred.JobClient: Reduce shuffle bytes=61
15/05/29 10:52:39 INFO mapred.JobClient: Spilled Records=8
15/05/29 10:52:39 INFO mapred.JobClient: Map output bytes=41
15/05/29 10:52:39 INFO mapred.JobClient: CPU time spent (ms)=7330
15/05/29 10:52:39 INFO mapred.JobClient: Total committed heap usage (bytes)=247275520
15/05/29 10:52:39 INFO mapred.JobClient: Combine input records=4
15/05/29 10:52:39 INFO mapred.JobClient: SPLIT_RAW_BYTES=214
15/05/29 10:52:39 INFO mapred.JobClient: Reduce input records=4
15/05/29 10:52:39 INFO mapred.JobClient: Reduce input groups=3
15/05/29 10:52:39 INFO mapred.JobClient: Combine output records=4
15/05/29 10:52:39 INFO mapred.JobClient: Physical memory (bytes) snapshot=338845696
15/05/29 10:52:39 INFO mapred.JobClient: Reduce output records=3
15/05/29 10:52:39 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1139433472
15/05/29 10:52:39 INFO mapred.JobClient: Map output records=4