大数据学习（4）MapReduce编程Helloworld：WordCount

Maven依赖：

<dependency>
        <groupId>jdk.tools</groupId>
        <artifactId>jdk.tools</artifactId>
        <version>1.6</version>
        <scope>system</scope>
        <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>2.6.5</version>
    </dependency>
        
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>2.6.5</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-core</artifactId>
        <version>2.6.5</version>
    </dependency>

Mapper类：

public class WordcountMapper extends Mapper<LongWritable,Text,Text,IntWritable>{

    @Override
    protected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {
        
        String line = value.toString();
        
        for(String word : line.split(" ")) {
            context.write(new Text(word), new IntWritable(1));
        }
        
    }
}

Reducer类：

public class WordcountReducer extends Reducer<Text, IntWritable,Text, IntWritable> {

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
        int count = 0;
        for(IntWritable value : values) {
            count += value.get();
        }
        context.write(key , new IntWritable(count));
    }
}

启动类：

public class WordcountLancher {

    public static void main(String[] args) throws Exception{
        String inputPath = args[0];
        String outputPath = args[1];
        
        Job job = Job.getInstance();
        
        job.setMapperClass(WordcountMapper.class);
        job.setReducerClass(WordcountReducer.class);
        
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
        
        FileInputFormat.setInputPaths(job, new Path(inputPath));
        FileOutputFormat.setOutputPath(job, new Path(outputPath));
        
        boolean success = job.waitForCompletion(true);
        
        System.exit(success ? 0 : 1);
        
    }
    
}

在HDFS中准备输入数据：

hadoop fs -mkdir -p /wordcount/input

hadoop fs -put LICENSE.txt /wordcount/input

记得启动yarn：

start-yarn.sh

启动map-reduce程序：

 hadoop jar wordcount.jar me.huqiao.hadoop.mr.WordcountLancher /wordcount/input /wordcount/output

查看结果：

hadoop fs -cat /wordcount/output/part-r-00000 |more

大数据学习（4）MapReduce编程Helloworld：WordCount

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