1、虚拟机集群搭建部署hadoop

利用VMware、centOS-7、Xshell(secureCrt)等软件搭建集群部署hadoop

远程连接工具使用Xshell:

HDFS文件操作

2.1 HDFS接口编程

调用HDFS文件接口实现对分布式文件系统中文件的访问,如创建、修改、删除等

三、MAPREDUCE并行程序开发

求每年最高气温

本实验是编写完成相关代码后,将该项目打包成jar包,上传至centos后利用hadoop命令进行运行。

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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;
public class Temperature {
/**
* 四个泛型类型分别代表:
* KeyIn Mapper的输入数据的Key,这里是每行文字的起始位置(0,11,...)
* ValueIn Mapper的输入数据的Value,这里是每行文字
* KeyOut Mapper的输出数据的Key,这里是每行文字中的“年份”
* ValueOut Mapper的输出数据的Value,这里是每行文字中的“气温”
*/
static class TempMapper extends
Mapper<LongWritable, Text, Text, IntWritable> {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 打印样本: Before Mapper: 0, 2000010115
System.out.print("Before Mapper: " + key + ", " + value);
String line = value.toString();
String year = line.substring(0, 4);
int temperature = Integer.parseInt(line.substring(8));
context.write(new Text(year), new IntWritable(temperature));
// 打印样本: After Mapper:2000, 15
System.out.println(
"======" +
"After Mapper:" + new Text(year) + ", " + new IntWritable(temperature));
}
} static class TempReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int maxValue = Integer.MIN_VALUE;
StringBuffer sb = new StringBuffer();
//取values的最大值
for (IntWritable value : values) {
maxValue = Math.max(maxValue, value.get());
sb.append(value).append(", ");
}
// 打印样本: Before Reduce: 2000, 15, 23, 99, 12, 22,
System.out.print("Before Reduce: " + key + ", " + sb.toString());
context.write(key, new IntWritable(maxValue));
// 打印样本: After Reduce: 2000, 99
System.out.println(
"======" +
"After Reduce: " + key + ", " + maxValue);
}
} public static void main(String[] args) throws Exception {
//输入路径
String dst = "hdfs://localhost:9000/intput.txt";
//输出路径,必须是不存在的,空文件加也不行。
String dstOut = "hdfs://localhost:9000/output";
Configuration hadoopConfig = new Configuration(); hadoopConfig.set("fs.hdfs.impl",
org.apache.hadoop.hdfs.DistributedFileSystem.class.getName()
);
hadoopConfig.set("fs.file.impl",
org.apache.hadoop.fs.LocalFileSystem.class.getName()
);
Job job = new Job(hadoopConfig); //如果需要打成jar运行,需要下面这句
job.setJarByClass(NewMaxTemperature.class); //job执行作业时输入和输出文件的路径
FileInputFormat.addInputPath(job, new Path(dst));
FileOutputFormat.setOutputPath(job, new Path(dstOut)); //指定自定义的Mapper和Reducer作为两个阶段的任务处理类
job.setMapperClass(TempMapper.class);
job.setReducerClass(TempReducer.class); //设置最后输出结果的Key和Value的类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//执行job,直到完成
job.waitForCompletion(true);
System.out.println("Finished");
}
}

词频统计

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper; public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>{ @Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
//super.map(key, value, context);
//String[] words = StringUtils.split(value.toString());
String[] words = StringUtils.split(value.toString(), " ");
for(String word:words)
{
context.write(new Text(word), new LongWritable(1)); }
}
} reducer:
package cn.edu.bupt.wcy.wordcount; import java.io.IOException; import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer; public class WordCountReducer extends Reducer<Text, LongWritable, Text, LongWritable> { @Override
protected void reduce(Text arg0, Iterable<LongWritable> arg1,
Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
// TODO Auto-generated method stub
//super.reduce(arg0, arg1, arg2);
int sum=0;
for(LongWritable num:arg1)
{
sum += num.get(); }
context.write(arg0,new LongWritable(sum)); }
} runner:
package cn.edu.bupt.wcy.wordcount; import java.io.IOException; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable;
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 WordCountRunner { public static void main(String[] args) throws IllegalArgumentException, IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
Job job = new Job(conf);
job.setJarByClass(WordCountRunner.class);
job.setJobName("wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[1]));
FileOutputFormat.setOutputPath(job, new Path(args[2]));
job.waitForCompletion(true);
} }

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