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hadoop基础

Brose 2023-07-14 阅读 74

Hadoop基础

什么是Hadoop?

Hadoop是一个开源的分布式计算框架,用于存储和处理大规模数据集。它的设计目标是可以在廉价的硬件上进行可靠、高效的分布式计算。

Hadoop具有以下几个核心组件:

  • Hadoop分布式文件系统(HDFS):用于存储大规模数据集的分布式文件系统。
  • Hadoop YARN:用于管理和调度集群中的资源。
  • Hadoop MapReduce:用于并行处理大规模数据集的分布式计算模型。

Hadoop的安装与配置

要使用Hadoop,首先需要安装和配置Hadoop集群。以下是在Linux环境下安装Hadoop的步骤:

  1. 下载Hadoop压缩包并解压:
wget 
tar -zxvf hadoop-3.3.0.tar.gz
  1. 配置Hadoop环境变量: 将以下内容添加到~/.bashrc文件中:
export HADOOP_HOME=/path/to/hadoop-3.3.0
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
  1. 配置Hadoop集群: 编辑$HADOOP_HOME/etc/hadoop/core-site.xml文件,设置HDFS的默认文件系统和端口号:
<configuration>
  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://localhost:9000</value>
  </property>
</configuration>

编辑$HADOOP_HOME/etc/hadoop/hdfs-site.xml文件,设置HDFS数据块的副本数量和数据存储路径:

<configuration>
  <property>
    <name>dfs.replication</name>
    <value>1</value>
  </property>
  <property>
    <name>dfs.namenode.name.dir</name>
    <value>file:/path/to/hadoop-3.3.0/data/dfs/namenode</value>
  </property>
  <property>
    <name>dfs.datanode.data.dir</name>
    <value>file:/path/to/hadoop-3.3.0/data/dfs/datanode</value>
  </property>
</configuration>
  1. 启动Hadoop集群:
start-dfs.sh
start-yarn.sh

Hadoop MapReduce示例

Hadoop MapReduce是一种分布式计算模型,用于处理大规模数据集。下面是一个简单的Hadoop MapReduce示例,用于统计文本文件中每个单词的出现次数。

首先,创建一个名为WordCount.java的Java源文件,包含以下代码:

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]));
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