MapReduce（WordCount）算法、简单求和计数 （小白也能看得懂）

@Override
protected void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {

    String datas = value.toString();

    String[] s = datas.split(" ");


    for (String s1 : s) {
        if ("".equals(s1)){
            continue;
        }
        context.write(new Text(datas),new LongWritable(Integer.parseInt(s1)));
    }

}

@Override
protected void reduce(Text key,Iterablevalues,Context context) throws IOException, InterruptedException {

    long sum=0;

    for (LongWritable value : values) {
        sum=sum+value.get();
    }

    context.write(key,new LongWritable(sum));


}

    //将已经编写好的Map Reduce 添加到计算框架中

    //1. 实例一个job
    Job job = Job.getInstance(new Configuration(), "WordCount");

    //2. 使用job 设置读取数据（包括数据的路径）
    job.setInputFormatClass(TextInputFormat.class);
    TextInputFormat.addInputPath(job,new Path("D:\\wordcount\\input\\SumData.txt"));

    //3. 使用job设置MAP类（map  输出的类型）
    job.setMapperClass(WordCountMap.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(LongWritable.class);

    //4. 使用job 设置Reduce类（Reduce 输入和输出的类型）
    job.setReducerClass(WordCountReduce.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(LongWritable.class);

    //5. 使用job 设置数据的输出路径
    job.setOutputFormatClass(TextOutputFormat.class);
    TextOutputFormat.setOutputPath(job,new Path("D:\\wordcount\\output\\a.txt"));
    //6. 返回执行状态编号
    return job.waitForCompletion(true)?0:1;
}


public static void main(String[] args) throws Exception {

    int run = ToolRunner.run(new WordCountDriver(), args);



}