MapReducer基础案例整理

最新推荐文章于 2024-11-10 11:21:24 发布

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最新推荐文章于 2024-11-10 11:21:24 发布

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文章标签：大数据 r语言

原文链接：https://juejin.im/post/5b4dbebde51d451926484671

本文介绍了使用MapReduce实现数据去重、排序加序号及计算平均成绩三个案例的具体实现过程，包括Mapper、Reducer的设计与测试类的编写。

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一、数据去重--Mapper类：

public class DataMapper extends Mapper<LongWritable,Text,Text,Text> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String[] split = value.toString().split(" ");
        context.write(new Text(split[0]),new Text(split[1]+" "+split[2]));

    }
}
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二、Reducer类:

public class DataReducer extends Reducer<Text,Text,Text,Text> {
    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {

        StringBuffer d = new StringBuffer();

        for (Text value : values) {
            d.append(value);
        }
        context.write(key,new Text(d.toString()));

    }
}
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三、测试类：

public class Main {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(Main.class);

        job.setMapperClass(DataMapper.class);
        job.setReducerClass(DataReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        File file = new File("E:\\fengzeze\\输出");
        if (file.exists()) {
            FileUtils.deleteDirectory(file);
        }
        FileInputFormat.setInputPaths(job, new Path("E:\\fengzeze\\数据"));
        FileOutputFormat.setOutputPath(job, new Path("E:\\fengzeze\\输出"));
        job.setNumReduceTasks(1);
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}
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设计思路：

[数据去重]的最终目标是让[原始数据]中[出现次数][超过一次]的数据在输出文件中只出现一次。我们自然而然会想到将同一个数据的所有记录都交给一台reduce机器，无论这个数据出现多少次，只要在最终结果中输出一次就可以了。具体就是[reduce的输入]应该以数据作为key，而对value-list则没有要求。当reduce接收到一个<key，value-list>时就直接将key复制到输出的key中，并将value设置成空值。

==========================================================================

一、数据排序加序号--Mapper类：

public class DataSorting extends Mapper<LongWritable,Text,Text,NullWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        context.write(new Text(value.toString()),NullWritable.get());
    }
}
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二、Reducer类：

用for循环递增1，在Key位置上输出i+1。

public class DataReducer extends Reducer<Text,NullWritable,Text,Text> {
    List<Integer> lien = new ArrayList<>();

    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        lien.add(Integer.parseInt(key.toString()));
    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        Collections.sort(lien);
        for (int i = 0; i < lien.size(); i++) {
            context.write(new Text(String.valueOf(i+1)), new Text(String.valueOf(lien.get(i))));
        }
    }
}
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三、测试类：

public class Test {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(Test.class);

        job.setMapperClass(DataSorting.class);
        job.setReducerClass(DataReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        File file = new File("E:\\fengzeze\\数据排序\\输出");
        if (file.exists()){
            FileUtils.deleteDirectory(file);
        }
        FileInputFormat.setInputPaths(job,new Path("E:\\fengzeze\\数据排序\\数据"));
        FileOutputFormat.setOutputPath(job,new Path("E:\\fengzeze\\数据排序\\输出"));

        job.setNumReduceTasks(1);

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}
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设计思路：

但是在使用之前首先需要了解它的默认排序规则。它是按照key值进行排序的，如果key为封装int的IntWritable类型，那么MapReduce按照数字大小对key排序，如果key为封装为String的Text类型，那么MapReduce按照字典顺序对字符串排序。没有用Combiner。

==========================================================================

一、平均成绩--Mapper类：

public class AMapper extends Mapper<LongWritable,Text,Text,IntWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String[] split = value.toString().split(" ");

        context.write(new Text(split[0]),new IntWritable(Integer.parseInt(split[1])));
    }
}
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二、Reducer类：

public class AReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int courseCount = 0;
        //定义科目的数量
        int sum = 0;
        //定义总成绩
        int average = 0;
        //定义平均值

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

        average = sum / courseCount;
        context.write(new Text(key),new IntWritable(average));
    }
}
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测试类：

public class AvTest {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(AvTest.class);

        job.setMapperClass(AMapper.class);
        job.setReducerClass(AReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        File file = new File("E:\\fengzeze\\平均成绩\\输出");
        if (file.exists()){
            FileUtils.deleteDirectory(file);
        }
        FileInputFormat.setInputPaths(job,new Path("E:\\fengzeze\\平均成绩\\数据"));
        FileOutputFormat.setOutputPath(job,new Path("E:\\fengzeze\\平均成绩\\输出"));

        job.setNumReduceTasks(1);

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}
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