MapReduce - A - 分组 - Comparator

旧城等待， 2024-04-18 14:12 42阅读 0赞

> 题目：  
> 求每个通信商的上行、下行、总流量 (输出：通信商 上行 下行 总的)

> 思路：  
> 通过手机号的前三位区分通信运营商  
> 按照运营商分组后，在每个Reduce里计算各运营商总和即可

> 代码：

//分组比较器
    
    package Comparator02;
    
    import org.apache.hadoop.io.WritableComparable;
    import org.apache.hadoop.io.WritableComparator;
    
    public class MyComparator extends WritableComparator {
        //将要重新定义的分组的key的类型传进来
        public MyComparator(){
            super(RecordWritable.class, true);
        }
    
        public int compare(WritableComparable a, WritableComparable b){
            RecordWritable a1 = (RecordWritable)a;
            RecordWritable b1 = (RecordWritable)b;
            return a1.getProduct().compareTo(b1.getProduct());
        }
    }

//实体类
    
    package Comparator02;
    
    import org.apache.hadoop.io.Writable;
    import org.apache.hadoop.io.WritableComparable;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    import java.io.IOException;
    
    public class RecordWritable implements WritableComparable {
        private String product;//运营商
    
        public void write(DataOutput dataOutput) throws IOException {
            dataOutput.writeUTF(this.product);
    
        }
    
        public void readFields(DataInput dataInput) throws IOException {
            this.product = dataInput.readUTF();
    
        }
    
        public int compareTo(Object o) {
            RecordWritable b = (RecordWritable) o;
            return this.product.compareTo(b.getProduct());
        }
    
        @Override
        public String toString() {
            return "RecordWritable{" +
                    "product=" + product +
                    '}';
        }
    
        public String getProduct() {
            return product;
        }
    
        public void setProduct(String product) {
            this.product = product;
        }
    
    
    }

//MapReduce
    
    package Comparator02;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.LongWritable;
    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;
    
    import java.io.IOException;
    
    public class MainDemo {
        public static  void main(String[] args){
            try{
                Configuration conf = new Configuration();
    
                Job job = Job.getInstance(conf, "Partitioner02-MainDemo");
    
                job.setJarByClass(MainDemo.class);
    
                job.setMapperClass(MyMapper.class);
                job.setMapOutputKeyClass(RecordWritable.class);
                job.setMapOutputValueClass(LongWritable.class);
    
                job.setReducerClass(MyReducer.class);
                job.setOutputKeyClass(Text.class);
                job.setOutputValueClass(LongWritable.class);
    
                //设置自定义分组
                job.setGroupingComparatorClass(MyComparator.class);
    
                FileInputFormat.addInputPath(job, new Path("C:/Users/Administrator/Desktop/Data/input/flow.log"));
                FileOutputFormat.setOutputPath(job, new Path("C:/Users/Administrator/Desktop/Data/output/02/00"));
    
                int success = job.waitForCompletion(true) ? 0: 1;
    
                System.exit(success);
            }
            catch (Exception e){
                e.printStackTrace();
            }
        }
        //自定义的Mapper类
        public static class MyMapper extends Mapper<Object, Text, RecordWritable, LongWritable>{
            RecordWritable k = new RecordWritable();
            LongWritable v = new LongWritable();
            @Override
            protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                String tov = "";
                String row = value.toString();
                String[] words = row.split("\t");
                int len = words.length;
                v.set(Integer.parseInt(words[len-3]) + Integer.parseInt(words[len-2]));
                String[] st = words[1].split(" ");
                String st2 = st[0].substring(0,3);
                if(st2.matches("(139)|(138)|(137)|(136)|(135)|(134)|(159)|(158)|(157)|(150)|(151)|(152)|(147)|(188)|(187)|(182)|(183)|(184)|(178)")){
                    k.setProduct("移动");
                }
                else if(st2.matches("(130)|(131)|(132)|(156)|(155)|(186)|(185)|(145)|(176)")){
                    k.setProduct("联通");
                }
                else if(st2.matches("(133)|(153)|(189)|(180)|(181)|(177)|(173)")){
                    k.setProduct("电信");
                }
                else {
                    k.setProduct("其它");
                }
                context.write(k, v);
            }
        }
        //自定义的Reducer类
        public static class MyReducer extends Reducer<RecordWritable, LongWritable, Text, LongWritable>{
            public Text k = new Text();
            public LongWritable v = new LongWritable();
            @Override
            protected void reduce(RecordWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
                long sum = 0;
                for(LongWritable lw: values){
                    sum += lw.get();
                }
                k.set(key.getProduct());
                v.set(sum);
                context.write(k, v);
            }
        }
    }