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:
- 下载Hadoop压缩包并解压:
wget
tar -zxvf hadoop-3.3.0.tar.gz
- 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
- 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>
- 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]));