Linux下搭建Hadoop环境步骤分享

简介

Hadoop是当下最为流行的分布式计算框架之一，能够处理海量数据，并提供并行处理能力。本文将详细介绍如何在Linux系统下搭建Hadoop环境。

步骤

1. 安装JDK

首先需要安装JDK，步骤如下：

sudo apt update
sudo apt install default-jdk

2. 下载Hadoop

从官网下载Hadoop安装包，地址为 https://hadoop.apache.org。

wget https://downloads.apache.org/hadoop/common/hadoop-3.3.1/hadoop-3.3.1.tar.gz

3. 解压Hadoop

将下载的Hadoop压缩包解压，并移动到指定目录。

tar -zxvf hadoop-3.3.1.tar.gz
sudo mv hadoop-3.3.1 /usr/local/hadoop

4. 配置环境变量

编辑 ~/.bashrc，添加以下内容：

export HADOOP_HOME=/usr/local/hadoop
export PATH=$HADOOP_HOME/bin:$PATH

使环境变量生效：

source ~/.bashrc

5. 配置Hadoop

进入Hadoop配置目录，并编辑hadoop-env.sh，添加Java环境变量。

cd $HADOOP_HOME/etc/hadoop
sudo vim hadoop-env.sh

将以下内容添加到文件末尾：

export JAVA_HOME=/usr/lib/jvm/default-java

编辑core-site.xml，添加以下内容：

<configuration>
   <property>
      <name>fs.default.name</name>
      <value>hdfs://localhost:9000</value>
   </property>
</configuration>

编辑hdfs-site.xml，添加以下内容：

<configuration>
   <property>
      <name>dfs.replication</name>
      <value>1</value>
   </property>
   <property>
      <name>dfs.namenode.name.dir</name>
      <value>file:/usr/local/hadoop/hadoop_data/hdfs/namenode</value>
   </property>
   <property>
      <name>dfs.datanode.data.dir</name>
      <value>file:/usr/local/hadoop/hadoop_data/hdfs/datanode</value>
   </property>
   <property>
      <name>dfs.namenode.secondary.http-address</name>
      <value>localhost:9001</value>
   </property>
</configuration>

编辑mapred-site.xml.template，并复制一份，并命名为mapred-site.xml：

cp mapred-site.xml.template mapred-site.xml

编辑mapred-site.xml，添加以下内容：

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

编辑yarn-site.xml，添加以下内容：

<configuration>
   <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
   </property>
   <property>
      <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
      <value>org.apache.hadoop.mapred.ShuffleHandler</value>
   </property>
   <property>
      <name>yarn.resourcemanager.hostname</name>
      <value>localhost</value>
   </property>
</configuration>

6. 启动Hadoop

启动Hadoop：

start-all.sh

查看进程：

jps

7. 示例说明

示例1：WordCount

在Hadoop上运行最简单的MapReduce示例WordCount。首先需要在本地创建一个输入文件。

echo "Hello World Again Again Wow!" > input.txt

将该文件上传到Hadoop文件系统上：

hdfs dfs -copyFromLocal input.txt input/input.txt

编写Java代码，实现WordCount：

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;
import java.util.StringTokenizer;

public class WordCount {

  public static class TokenizerMapper
      extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class IntSumReducer
      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);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path("input"));
    FileOutputFormat.setOutputPath(job, new Path("output"));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

将上述代码编译打包：

hadoop com.sun.tools.javac.Main WordCount.java
jar cf wc.jar WordCount*.class

运行WordCount：

hadoop jar wc.jar WordCount input output

在Hadoop文件系统上查看输出：

hdfs dfs -ls output
hdfs dfs -cat output/part*

示例2：HDFS读写

首先在Hadoop文件系统上创建一个目录并上传一个文件。

hdfs dfs -mkdir input
echo "Hello World Again Again Wow!" > input/input.txt

编写Java代码，实现HDFS读写：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.util.stream.Collectors;

public class HdfsReadWrite {
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    FileSystem fileSystem = FileSystem.get(conf);

    // 读取HDFS上的文件
    BufferedReader reader = new BufferedReader(
        new InputStreamReader(
            fileSystem.open(new Path("input/input.txt")))
    );
    String content = reader.lines().collect(Collectors.joining("\n"));
    System.out.println(content);

    // 向HDFS上写入文件
    fileSystem.create(new Path("output/output.txt"))
        .write(content.getBytes());

    fileSystem.close();
  }
}