MapReduce简单使用

来源：互联网发布：mysql函数大全编辑：程序博客网时间：2024/04/30 06:05

1、启动hadoop工程

2、MapReduce统计文本单词数量

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public class WordCount {
 
    private static class WordMapper extends
            Mapper<LongWritable, Text, Text, IntWritable> {
 
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
 
            String string = value.toString();
            String[] strs = string.split(" ");
            for (String str : strs) {
                context.write(new Text(str), new IntWritable(1));
            }
 
        }
 
    }
 
    private static class WordReduce extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        // key 单词 //value：{1,1}
 
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
 
            int count = 0;
            for (IntWritable value : values) {
                count += value.get();
            }
 
            context.write(key, new IntWritable(count));
 
        }
 
    }
 
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
 
        Configuration configuration = HadoopConfig.getConfiguration();
        Job job = Job.getInstance(configuration, "统计单词数目");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(WordMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setReducerClass(WordReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
 
        FileInputFormat.addInputPath(job, new Path("/data"));
        FileOutputFormat.setOutputPath(job, new Path("/ouput"));
        job.waitForCompletion(true);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
 
    }

2、MapReduce排除文本重复数据

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public class Dup {
     
    private static class DupMapper extends
            Mapper<LongWritable, Text, Text, NullWritable> {
 
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, Text, NullWritable>.Context context)
                throws IOException, InterruptedException {
 
            context.write(new Text(value), NullWritable.get());
 
        }
 
    }
 
    private static class DupReduce extends 
            Reducer<Text, NullWritable, Text, NullWritable> {
 
        @Override
        protected void reduce(Text key, Iterable<NullWritable> values,
                Reducer<Text, NullWritable, Text, NullWritable>.Context context)
                throws IOException, InterruptedException {
 
            context.write(new Text(key), NullWritable.get());
 
        }
 
    }
 
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
 
        Configuration configuration = HadoopConfig.getConfiguration();
        Job job = Job.getInstance(configuration, "去重");
 
        job.setJarByClass(Dup.class);
        job.setMapperClass(DupMapper.class);
         
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
         
        job.setReducerClass(DupReduce.class);
        FileInputFormat.addInputPath(job, new Path("/data"));
        FileOutputFormat.setOutputPath(job, new Path("/dup"));
        job.waitForCompletion(true);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
 
    }

3、MapReduce实线文本数据的简单排序

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public class Sort {
 
    private static class SortMapper extends
            Mapper<LongWritable, Text, IntWritable, IntWritable> {
           //输出，输入
        @Override
        protected void map(
                LongWritable key,
                Text value_text,
                Mapper<LongWritable, Text, IntWritable, IntWritable>.Context context)
                throws IOException, InterruptedException {
 
            int value = Integer.parseInt(value_text.toString());
            context.write(new IntWritable(value), new IntWritable(1));
             
        }
 
    }
     
    private static class SortReduce extends
            Reducer<IntWritable, IntWritable, IntWritable, NullWritable> {
 
        @Override
        protected void reduce(
                IntWritable key,
                Iterable<IntWritable> values,
                Reducer<IntWritable, IntWritable, IntWritable, NullWritable>.Context context)
                throws IOException, InterruptedException {
 
            for (IntWritable value : values) {
                context.write(key, NullWritable.get());
            }
 
        }
 
    }
 
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
 
        Configuration configuration = HadoopConfig.getConfiguration();
        Job job = Job.getInstance(configuration, "排序");
 
        job.setJarByClass(Sort.class);
        job.setMapperClass(SortMapper.class);
         
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(NullWritable.class);
         
        job.setReducerClass(SortReduce.class);
        FileInputFormat.addInputPath(job, new Path("/data"));
        FileOutputFormat.setOutputPath(job, new Path("/sort"));
        job.waitForCompletion(true);
 
    }

4、MapReduce实线单表连接

文本数据如下：

child parent

tom lucy

tom jack

lucy mary

lucy ben

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public class Single {
 
    private static class SingleMapper extends
            Mapper<LongWritable, Text, Text, Text> {
 
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {
 
            String string = value.toString();
            if (!string.contains("child")) {
 
                String[] strings = string.split(" ");
                context.write(new Text(strings[0]), new Text(strings[1] + ":1"));
                context.write(new Text(strings[1]), new Text(strings[0] + ":2"));
 
            }
        }
    }
 
    // reduce是执行key的次数
    private static class SingleReduce extends Reducer<Text, Text, Text, Text> {
 
        @Override
        protected void reduce(Text key, Iterable<Text> values,
                Reducer<Text, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {
 
            List<String> left = Lists.newArrayList();
            List<String> right = Lists.newArrayList();
     
            for (Text value : values) {  
 
                String[] strings = value.toString().split(":");
             
                if (strings[1].equals("1")) {
                    right.add(strings[0]);
                } else {
                    left.add(strings[0]);
                }
            }
             
            for (String lef : left) {
                for (String rig : right) {
                    context.write(new Text(lef), new Text(rig));
                }
            }
 
        }
 
    }
 
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
 
        Configuration configuration = HadoopConfig.getConfiguration();
        Job job = Job.getInstance(configuration, "单表连接");
 
        job.setJarByClass(Sort.class);
        job.setMapperClass(SingleMapper.class);
 
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
 
        job.setReducerClass(SingleReduce.class);
        FileInputFormat.addInputPath(job, new Path("/data"));
        FileOutputFormat.setOutputPath(job, new Path("/single"));
        job.waitForCompletion(true);
 
    }

输出结果如下：

grandchild grandparent //额外加入的，表达思路

tom mary

tom ben

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