MapReduce打包运行
1. 编写 MapReduce 程序
首先需要编写 MapReduce 程序，通常包含 Mapper、Reducer 和 Driver 类。例如，一个简单的 WordCount 程序：
java
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 TokenizerMapperextends 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 IntSumReducerextends 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);}
}
2. 创建 Maven 项目（推荐）
使用 Maven 管理依赖和打包，pom.xml示例：
xml
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.example</groupId><artifactId>mapreduce-example</artifactId><version>1.0-SNAPSHOT</version><properties><hadoop.version>3.3.6</hadoop.version><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target></properties><dependencies><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>${hadoop.version}</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>${hadoop.version}</version></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>3.4.1</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><transformers><transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"><mainClass>WordCount</mainClass></transformer></transformers></configuration></execution></executions></plugin></plugins></build>
</project>
3. 打包项目
使用 Maven 命令打包：
bash
mvn clean package
这将生成一个包含所有依赖的 JAR 文件（通常位于target/mapreduce-example-1.0-SNAPSHOT.jar）。
4. 上传输入数据到 HDFS
假设输入文件为input.txt，上传到 HDFS：
bash
hdfs dfs -mkdir -p /user/hadoop/input
hdfs dfs -put input.txt /user/hadoop/input/
5. 运行 MapReduce 作业
使用hadoop jar命令提交作业：
bash
hadoop jar target/mapreduce-example-1.0-SNAPSHOT.jar WordCount /user/hadoop/input /user/hadoop/output
- 参数说明：
target/mapreduce-example-1.0-SNAPSHOT.jar：打包后的 JAR 文件路径。WordCount：主类名（包含main方法的类）。/user/hadoop/input：HDFS 输入路径。/user/hadoop/output：HDFS 输出路径（需不存在，系统会自动创建）。
6. 查看结果
bash
hdfs dfs -cat /user/hadoop/output/part-r-00000