Hadoop单机版快速搭建及测试

一、快速配置Hadoop并启动(为了快速上手用单机搭建)&＃xff1a;

hadoop下载地载:http://mirror.bit.edu.cn/apache/hadoop/ 
1、修改配置文件&＃xff1a;
core-site.xml


   
        fs.defaultFS
        hdfs://localhost:9000
   


hdfs-site.xml


   
        dfs.replication
        1
   


mapred-site.xml


   
        mapreduce.framework.name
        yarn
   


yarn-site.xml


   
        yarn.nodemanager.aux-services
        mapreduce_shuffle
   


hadoop-env.sh

export JAVA_HOME&＃61;/usr/java/jdk1.8.0_121

2、格式化文件系统

./hdfs namenode -format

3、启动名称节点和数据节点后台进程

./sbin/start-dfs.sh

 启动ResourceManger和NodeManager后台进程

./sbin/start-yarn.sh

或者只用

./sbin/start-all.sh

二、测试

2.1 HDFS测试

使用浏览器查看hdfs目录&＃xff0c;端口号是50070:

操作材料下载

https://pan.baidu.com/s/1hs62YTe

进入hadoop解压目录下的bin目录, HDFS创建目录:

./hdfs dfs -mkdir /wordcount
./hdfs dfs -mkdir /wordcount/result
./hadoop fs -rmr /wordcount/result

拷贝input文件夹到HDFS目录下

./hdfs dfs -put /opt/input /wordcount

查看文件列表&＃xff1a;

./hadoop fs -ls /wordcount/input

2.2 MapReduce测试

是参考官方文档的wordcount实验&＃xff0c;将wordcount的代码译并打包&＃xff0c;放到服务器的目录(/opt/testsource)下&＃xff08;注意不是hdfs的目录下&＃xff09;

并将测试的要进行wordcount的文件放入hdfs的/wordcount/input目录下

执行hadoop job

./hadoop jar /opt/testsource/learning.jar
hadoop.WordCount /wordcount/input /wordcount/result

确认执行结果

hdfs dfs -cat /wordcount/result/*

附wordcount代码&＃xff1a;

package hadoop;
/**
* Created by BD-PC11 on 2017/3/29.
*/
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class WordCount {
public static class Map extends MapReduceBase
implements Mapper {
private final static IntWritable one &＃61; new IntWritable(1);
private Text word &＃61; new Text();
public void map(LongWritable key, Text value, OutputCollector output,
Reporter reporter) throws IOException {
String line &＃61; value.toString();
StringTokenizer tokenizer &＃61; new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}
public static class Reduce extends MapReduceBase
implements Reducer {
public void reduce(Text key, Iterator values,
OutputCollector output,
Reporter reporter) throws IOException {
int sum &＃61; 0;
while (values.hasNext()) {
sum &＃43;&＃61; values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
JobConf conf &＃61; new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}