部署自己写的地图/reduce程序的方法

部署自己写的map/reduce程序的方法
部署自己写的map/reduce程序的方法

【1】：首先就是打包了。需要把的程序（ class文件）、配置目录（conf/）、jar包 放在同一级目录 ，下面是 build.xml
<?xml version="1.0" encoding="UTF-8"?>

<project name="mapreducetest" default="dist">

<property name="name" value="mapreducetest" />
<property name="version" value="0.1" />
<property name="build" value="build" />
<property name="build.classes" value="${build}/classes" />
<property name="dist" value="dist" />
<property name="src" value="src" />
<property name="lib" value="lib" />
<property name="conf" value="conf" />

<path id="project.class.path">
<fileset dir="${lib}">
<include name="*.jar" />
</fileset>
</path>

<target name="init">
<delete dir="${build}" />
<delete dir="${dist}" />
<mkdir dir="${build}" />
<mkdir dir="${build.classes}" />
<mkdir dir="${dist}" />
</target>

<target name="compile" depends="init">
<javac debug="true" srcdir="${src}" destdir="${build.classes}">
<classpath refid="project.class.path" />
</javac>
</target>

<target name="dist" depends="compile">
<copy todir="${build.classes}">
<fileset dir="${src}">
<include name="**/*.xml" />
<include name="**/*.dtd" />
<include name="**/*.properties" />
<include name="**/*.vm" />
<include name="**/*.xsd" />
</fileset>
<fileset dir="${lib}">
<include name="*.jar"/>
</fileset>
</copy>
<mkdir dir="${dist}" />
<jar jarfile="${dist}/${name}.jar" index="true">
<fileset dir="${build.classes}">
<include name="**" />
</fileset>
<fileset dir="${conf}">
<include name="**" />
</fileset>
<manifest>
<attribute name="Built-By" value="${author}" />
<section name="main">
<attribute name="Specification-Version" value="${version}" />
<attribute name="Implementation-Title" value="main" />
<attribute name="Implementation-Version" value="${version} ${TODAY}" />
</section>
</manifest>
</jar>
</target>
<target name="clean">
<delete dir="${build}" />
<delete dir="${dist}" />
</target>

</project>
【2】 ： 把/dist/mapreducetest.jar 拷贝到指定的目录 --- ( hadoop@user-desktop:~/hadoop-0.19.2$ ) 接下来就是运行了。
【3】 ： bin/hadoop jar mapreducetest.jar com.mapreduce.demo.DB.ReadAccess /hadoop/readout  如果能看到下面东东。就说明你的map/reduce运行成功了。
10/07/07 15:39:58 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
10/07/07 15:40:02 INFO mapred.JobClient: Running job: job_201007071102_0002
10/07/07 15:40:03 INFO mapred.JobClient:  map 0% reduce 0%
10/07/07 15:40:08 INFO mapred.JobClient:  map 50% reduce 0%
10/07/07 15:40:10 INFO mapred.JobClient:  map 100% reduce 0%
10/07/07 15:40:15 INFO mapred.JobClient:  map 100% reduce 100%
10/07/07 15:40:16 INFO mapred.JobClient: Job complete: job_201007071102_0002
10/07/07 15:40:16 INFO mapred.JobClient: Counters: 14
10/07/07 15:40:16 INFO mapred.JobClient:   File Systems
10/07/07 15:40:16 INFO mapred.JobClient:     HDFS bytes written=80
10/07/07 15:40:16 INFO mapred.JobClient:     Local bytes read=134
10/07/07 15:40:16 INFO mapred.JobClient:     Local bytes written=330
10/07/07 15:40:16 INFO mapred.JobClient:   Job Counters
10/07/07 15:40:16 INFO mapred.JobClient:     Launched reduce tasks=1
10/07/07 15:40:16 INFO mapred.JobClient:     Launched map tasks=2
10/07/07 15:40:16 INFO mapred.JobClient:   Map-Reduce Framework
10/07/07 15:40:16 INFO mapred.JobClient:     Reduce input groups=6
10/07/07 15:40:16 INFO mapred.JobClient:     Combine output records=0
10/07/07 15:40:16 INFO mapred.JobClient:     Map input records=6
10/07/07 15:40:16 INFO mapred.JobClient:     Reduce output records=6
10/07/07 15:40:16 INFO mapred.JobClient:     Map output bytes=116
10/07/07 15:40:16 INFO mapred.JobClient:     Map input bytes=6
10/07/07 15:40:16 INFO mapred.JobClient:     Combine input records=0
10/07/07 15:40:16 INFO mapred.JobClient:     Map output records=6
10/07/07 15:40:16 INFO mapred.JobClient:     Reduce input records=6

【4】 ： 命令查看输出
bin/hadoop fs -cat /hadoop/readout/part-00000| head -13