下载Hadoop教程

1. Introduction

Hadoop是一个开源框架，用于处理大规模数据集的分布式计算。它使用简单且可扩展的模型，能够在成百上千台普通计算机上分布式运行。本教程将介绍如何下载和安装Hadoop，并提供一些常见的代码示例来帮助您入门。

2. 下载Hadoop

您可以从Hadoop官方网站下载最新的Hadoop版本。下载链接：[Hadoop官方网站](

3. 安装Hadoop

下载完成后，解压缩Hadoop压缩包到您选择的目录。接下来，我们需要进行一些配置。

3.1 配置环境变量

在~/.bashrc文件中添加以下内容：

export HADOOP_HOME=/path/to/hadoop
export PATH=$PATH:$HADOOP_HOME/bin

3.2 配置Hadoop

进入Hadoop安装目录，编辑etc/hadoop/hadoop-env.sh文件，并将以下行添加到文件的末尾：

export JAVA_HOME=/path/to/java

3.3 配置核心设置

编辑etc/hadoop/core-site.xml文件，将以下内容添加到<configuration>标签之间：

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

3.4 配置HDFS设置

编辑etc/hadoop/hdfs-site.xml文件，将以下内容添加到<configuration>标签之间：

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

3.5 格式化HDFS

运行以下命令，格式化HDFS：

hdfs namenode -format

4. Hadoop示例代码

现在，让我们来看一些常见的Hadoop代码示例。

4.1 Word Count

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(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

4.2 Pi计算

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
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.Random;

public class PiCalculation {
  
  public static class MonteCarloMapper extends Mapper<Object, NullWritable, NullWritable, NullWritable> {
    
    private long numPoints;