hadoop分布式环境安装

1. 下载hadoop和jdk安装包到指定目录,并安装java环境。

 

2.解压hadoop到指定目录，配置环境变量。vim /etc/profile

export JAVA_HOME=/home/xiaozw/soft/java/jdk1.8.0_201
export HADOOP_HOME=/home/xiaozw/soft/java/hadoop-3.1.2
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib:$CLASSPATH
export JAVA_PATH=${JAVA_HOME}/bin:${JRE_HOME}/bin
export PATH=$PATH:${JAVA_PATH}:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

3. 复制配置文件到新文件夹，备份用。

cp -r hadoop hadoop_cluster

重命名配置文件。

mv hadoop hadoop_bak

创建软链接
ln -s hadoop hadoop_cluster

 

修改配置文件,路径：soft/java/hadoop-3.1.2/etc/hadoop_cluster/

分别修改

 core-site.xml

<property>
<name>fs.defaultFS</name>
<value>hdfs://c0:9000/</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/home/xiaozw/soft/tmp/hadoop-${user.name}</value>
</property>

hdfs-site.xml

<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>c3:9868</value>
</property>

mapred-site.xml

<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>

yarn-site.xml

<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.recourcemanager.hostname</name>
<value>c3</value>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
<property>
<name>yarn.log-aggregation.retain-seconds</name>
<value>604800</value>
</property>

修改hadoop_cluster/hadoop-env.sh

export JAVA_HOME=/home/xiaozw/soft/java/jdk1.8.0_201

4. 克隆多台机器。修改hostname

 分别修改每台机器。

vim /etc/hostname
c0

每台机器都一样配置。

vim /etc/hosts

192.168.132.143 c0
192.168.132.144 c1
192.168.132.145 c2
192.168.132.146 c3

4台服务器需要ssh免密码登录。 

设置2台服务器为data-node。进入配置文件目录：

cd soft/java/hadoop-3.1.2/etc/hadoop_cluster/

sudo vim workers

新建脚本方便拷贝文件到多台服务器上面。

bat.sh

for((i=1;i<=3;i++))
{
#scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/hadoop-env.sh xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/hadoop-env.sh
#scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/hdfs-site.xml xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/hdfs-site.xml
#scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/core-site.xml xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/core-site.xml
#scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/mapred-site.xml xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/mapred-site.xml
#scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/yarn-site.xml xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/yarn-site.xml
scp /home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/workers xiaozw@c$i:/home/xiaozw/soft/java/hadoop-3.1.2/etc/hadoop_cluster/workers
ssh xiaozw@c$i rm -rf /home/xiaozw/soft/tmp/
#scp /etc/hosts xiaozw@c$i:/etc/hosts
}

新增权限
chmod a+x bat.sh

5. 启动hadoop 

start-all.sh

http://192.168.132.143:9870/dfshealth.html#tab-overview

 http://192.168.132.143:8088/cluster

统计最高温度demo:

public static void main(String[] args) {
            try {
                log.info("开始。。。");
                Configuration conf = new Configuration();
                String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
//            if (otherArgs.length != 2) {
//                System.err.println("Usage: wordcount  ");
//                System.exit(2);
//            }
                Job job = new Job(conf, "max tempperature");

                //运行的jar
                //job.setJarByClass(MaxTemperature2.class);
                job.setJar("/home/xiaozw/soft/download/demo-0.0.1-SNAPSHOT.jar");

                FileSystem fs=FileSystem.get(conf);
                //如果输出路径存在，删除。
                Path outDir=new Path("/home/xiaozw/soft/hadoop-data/out");
                if(fs.exists(outDir)){
                    fs.delete(outDir,true);
                }

                Path tmpDir=new Path("/home/xiaozw/soft/tmp");
                if(fs.exists(tmpDir)){
                    fs.delete(tmpDir,true);
                }

                //job执行作业时输入和输出文件的路径
                FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
                FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

                //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(TempReduce.class);

                //设置最后输出结果的Key和Value的类型
                job.setOutputKeyClass(Text.class);
                job.setOutputValueClass(IntWritable.class);

                //提交作业并等待它完成
                System.exit(job.waitForCompletion(true) ? 0 : 1);
            } catch (Exception e) {
                e.printStackTrace();
            }
            log.info("结束。。。");
    }

package com.example.demo;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TempMapper extends Mapper<Object, Text, Text, IntWritable>{
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        String lineValue = value.toString();
        String year = lineValue.substring(0, 4);
        int temperature = Integer.parseInt(lineValue.substring(8));
        context.write(new Text(year), new IntWritable(temperature));
    }
}

package com.example.demo;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TempReduce extends Reducer<Text, IntWritable, Text, IntWritable>{
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int maxTemp = Integer.MIN_VALUE;
        for(IntWritable value : values){
            maxTemp = Math.max(maxTemp, value.get());
        }
        context.write(key, new IntWritable(maxTemp));
    }
}

网盘文件和代码下载地址：

链接：https://pan.baidu.com/s/14wdv5CTXzw_0pmDisCa0uA
提取码：auao