本机的环境如下：
Eclipse 3.6
Hadoop-0.20.2
Hive-0.5.0-dev
1. 安装hadoop-0.20.2-eclipse-plugin的插件。注意：Hadoop目录中的/hadoop-0.20.2/contrib /eclipse-plugin/hadoop-0.20.2-eclipse-plugin.jar在Eclipse3.6下有问题，无法在 Hadoop Server上运行，可以从http://code.google.com/p/hadoop-eclipse-plugin/下载
2. 选择Map/Reduce视图：window -> open pers.. -> other.. -> map/reduce
3. 增加DFS Locations：点击Map/Reduce Locations—> New Hadoop Loaction，填写对应的host和port
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Map/Reduce Master: Host: 10.10.xx.xx Port: 9001 DFS Master: Host: 10.10.xx.xx（选中 User M/R Master host即可） Port: 9000 User name: root 更改Advance parameters 中的 hadoop.job.ugi, 默认是 DrWho,Tardis, 改成：root,Tardis。如果看不到选项，则使用Eclipse -clean重启Eclipse 否则，可能会报错org.apache.hadoop.security.AccessControlException |
4. 设置本机的Host：
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10.10.xx.xx zw-hadoop-master. zw-hadoop-master
#注意后面需要还有一个zw-hadoop-master.，否则运行Map/Reduce时会报错：
java.lang.IllegalArgumentException: Wrong FS: hdfs://zw-hadoop-master:9000/user/root/oplog/out/_temporary/_attempt_201008051742_0135_m_000007_0, expected: hdfs://zw-hadoop-master.:9000
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:352)
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5. 新建一个Map/Reduce Project，新建Mapper,Reducer,Driver类，注意，自动生成的代码是基于老版本的Hadoop，自己修改：
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package com.sohu.hadoop.test; import java.util.StringTokenizer; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class MapperTest extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String userid = value.toString().split("[|]")[2]; context.write(new Text(userid), new IntWritable(1)); } } package com.sohu.hadoop.test; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class ReducerTest 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); } } package com.sohu.hadoop.test; 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.io.compress.CompressionCodec; import org.apache.hadoop.io.compress.GzipCodec; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; public class DriverTest { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args) .getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: DriverTest <in> <out>"); System.exit(2); } Job job = new Job(conf, "Driver Test"); job.setJarByClass(DriverTest.class); job.setMapperClass(MapperTest.class); job.setCombinerClass(ReducerTest.class); job.setReducerClass(ReducerTest.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); conf.setBoolean("mapred.output.compress", true); conf.setClass("mapred.output.compression.codec", GzipCodec.class,CompressionCodec.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } |
6. 在DriverTest上，点击Run As —> Run on Hadoop，选择对应的Hadoop Locaion即可