基于hadoop源码开发环境搭建

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基于hadoop源码开发环境搭建

 

在开发hadoop的MR，以及研究hadoop源码，都需要将hadoop源码java部署到开发工具中，

例如常用的eclipse，具体做法如下：

　　第一步：在Eclipse新建一个Java项目
　　第二步：将Hadoop程序src下core, hdfs, mapred, tools几个目录copy到上述新建项目的src目录
　　第三步：修改将Java Build Path，删除src，添加src/core, src/hdfs....几个源码目录
　　第四步：为Java Build Path添加项目依赖jar，可以导入Hadoop程序的lib下所有jar包（别漏掉其子目录jar包），导入ant程序lib下所有jar包。
　　第五步：理论上第四步就OK了，但是可能会报大量如下错误：

Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library C:\Program Files\JDK\jre\lib\rt.jar xxx.java xxxx line 141 Java Problem
　　解决办法是：右键项目“propertiyes” > "Java Build Path" > "Libraries"，展开"JRE System Library"，双击"Access rules"，点击"Add"按钮，在"Resolution"下拉框选择"Accessible"，"Rule Pattern"填写"**/*"，保存后就OK了。
　　

　　测试wordcount实例：
　　reduce：

package 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 MyReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}

　　map：

package test;

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MyMap extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word;
public void map(Object key, Text value, Context context)throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word = new Text();
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
}

　　driver：

package 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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class MyDriver {
public static void main(String[] args) throws Exception, InterruptedException {
Configuration conf = new Configuration();
conf.set("dfs.permissions", "flase");
Job job = new Job(conf, "Hello Hadoop");
job.setJarByClass(MyDriver.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(MyMap.class);
job.setCombinerClass(MyReduce.class);
job.setReducerClass(MyReduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//JobClient.runJob(conf);
job.waitForCompletion(true);
}
}

　　配置响应的运行参数，运行即可。