摘要：MapReduce的IDEA配置及WordCount案例
Maven项目配置
创建一个空的Maven项目
打开根目录下的 pom.xml文件，参考配置：
UTF-8
3.2.2
junit
junit
4.10
test
org.apache.hadoop
hadoop-client
${hadoop.version}
org.apache.hadoop
hadoop-common
${hadoop.version}
org.apache.hadoop
hadoop-hdfs
${hadoop.version}
在项目的 src/main/java/resources下新建 log4j.properties，参考配置
参考配置1
log4j.rootLogger = info,console
log4j.appender.console = org.apache.log4j.ConsoleAppender
log4j.appender.console.Target = System.out
log4j.appender.console.layout = org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern = %d{ABSOLUTE} %5p %c:%L - %m%n
glibc lib version diff
log4j.logger.org.apache.hadoop.util.NativeCodeLoader=ERROR
参考配置2
log4j.rootLogger = debug,stdout
### 输出信息到控制台 ###
log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target = System.out
log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern = [%-5p] %d{yyyy-MM-dd HH:mm:ss,SSS} method:%l%n%m%n
编写应用程序
/**
* 导入包
*/
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;
/**
* WordCount应用程序
*/
public class WordCountApp {
/**
* Mapper
*/
public static class MyMapper extends Mapper {
LongWritable one = new LongWritable(1);
@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 接收到的每一行数据
String line = value.toString();
// 按照指定分隔符进行拆分
String[] words = line.split(" ");
for(String word: words){
// 通过上下文把map的处理结果输出
context.write(new Text(word), one);
}
}
}
/**
* Reduce归并
*/
public static class MyReducer extends Reducer{
@Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException{
long sum = 0;
for(LongWritable value: values){
// 求key出现的次数
sum += value.get();
}
// 最终统计结果输出
context.write(key, new LongWritable(sum));
}
}
/**
* 定义Driver：封装了MapReduce作业的所有信息
*/
public static void main(String[] args) throws Exception{
// windows版本设置HADOOP_HOME环境变量后，若不重启电脑，需要填加该语句
//System.setProperty("hadoop.home.dir", "C:/Development/hadoop");
System.setProperty("hadoop.home.dir", "/usr/local/hadoop");
// 设置操作用户，默认root
System.setProperty("HADOOP_USER_NAME", "root");
//创建Configuration
Configuration configuration = new Configuration();
// 设置fs.defaultFS参数，默认本地读取
configuration.set("fs.defaultFS", "hdfs://master:9000");
// 若参数数量不为2，报错退出，第一个参数读取是输入目录（HDFS），第二个参数是输出目录
if (args.length != 2) {
System.err.println("Usage: MyDriver ");
System.exit(2);
}
// 如果输出目录存在，则删除
Path mypath = new Path(args[1]);
FileSystem hdfs = mypath.getFileSystem(configuration);
if (hdfs.isDirectory(mypath)) {
hdfs.delete(mypath, true);
}
//创建Job
Job job = Job.getInstance(configuration, "wordcount");
//设置job的处理类
job.setJarByClass(WordCountApp.class);
//设置作业处理的输入路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
//设置map相关参数
job.setMapperClass(MyMapper.class);
//设置Map阶段的输出类型: k2 和V2的类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
//分区，排序，规约，分组步骤采用默认方式
//设置reduce相关参数
job.setReducerClass(MyReducer.class);
//设置Reduce阶段的输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
//设置作业处理的输出路径
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
IDEA配置
编辑运行环境
使用远程环境，设置ssh连接，添加input和output目录
Debug
Original: https://www.cnblogs.com/dominickk/p/16361606.html
Author: DominicKK
Title: MapReduce入门实例——WordCount
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