hadoop三大发行版本

实现Hadoop三大发行版本的步骤指南

介绍

Hadoop是一个开源的分布式计算平台，广泛用于大数据处理。它有三个主要的发行版本：Apache Hadoop、Cloudera CDH和Hortonworks HDP。本文将教你如何实现这三大发行版本，并提供相应的代码和注释。

实现步骤

下面是实现Hadoop三大发行版本的步骤，使用甘特图表示：

gantt
    dateFormat YYYY-MM-DD
    title Hadoop三大发行版本的实现步骤

    section Apache Hadoop
    安装Hadoop    :done, 2021-01-01, 1d
    配置Hadoop集群   :done, 2021-01-02, 2d
    编写Hadoop应用程序 :done, 2021-01-04, 3d

    section Cloudera CDH
    安装Cloudera Manager    :done, 2021-01-05, 1d
    使用Cloudera Manager配置CDH集群   :done, 2021-01-06, 2d
    部署Hadoop应用程序 :done, 2021-01-08, 3d

    section Hortonworks HDP
    安装Ambari    :done, 2021-01-09, 1d
    使用Ambari配置HDP集群   :done, 2021-01-10, 2d
    开发Hadoop应用程序 :done, 2021-01-12, 3d

Apache Hadoop

安装Hadoop

在Apache Hadoop官方网站下载最新的Hadoop发行版本并解压缩。这里假设你已经安装了Java Development Kit (JDK)。

    // 下载Hadoop发行版本
    wget 
    
    // 解压缩
    tar -xzvf hadoop-x.x.x.tar.gz

配置Hadoop集群

编辑hadoop-x.x.x/etc/hadoop/core-site.xml文件，添加以下属性：

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

接下来，编辑hadoop-x.x.x/etc/hadoop/hdfs-site.xml文件，添加以下属性：

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

编写Hadoop应用程序

创建一个新的Java项目，并导入Hadoop的相关库。编写一个简单的WordCount程序作为示例：

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

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;

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(args[0]));
        FileOutputFormat.setOutputPath(job,