MapReduce求top实例——导入hive

通过对各个城市的直销拒单率，求得省份的直销拒单率，并按拒单率降序排序，取前8写入hive；

(1).编写Bean类

import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class bean implements WritableComparable {

    private String province;
    public bean() {
        super();
    }
    private double norate;

    public String getProvince() {
        return province;
    }

    public void setProvince(String province) {
        this.province = province;
    }

    public double getNorate() {
        return norate;
    }

    public void setNorate(double norate) {
        this.norate = norate;
    }

    @Override
    public String toString() {
        return  province +"t" + norate;
    }

    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(province);
        dataOutput.writeDouble(norate);
    }

    @Override
    public void readFields(DataInput dataInput) throws IOException {
        this.province = dataInput.readUTF();
        this.norate = dataInput.readDouble();
    }

    @Override
    //排序，大到小
    public int compareTo(bean o) {
        int res;
        if (o.getNorate()>norate){
            res=1;
        }else if (o.getNorate() 
(2).编写Mapper类
 
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class m4 extends Mapper {
    @Override
    protected void map(LongWritable key, Text value, Mapper.Context context) throws IOException, InterruptedException {
        //获取一行数据，将数据进行切割
        String[] words = value.toString().split(",");
        //判断所需数据是否为空，为空则忽略
        if (words[24].equals("null") || words[24].equals("")||words[24].equals("城市直销拒单率")||words[4].equals("")||words[4].equals("null")) {
            return;
        } else {
            //省份和城市作为key，直销拒单率作为value
            context.write(new Text(words[3]+"t"+words[4]),new DoubleWritable(Double.parseDouble(words[24].replace("%",""))/100));
        }
    }
}
 
（3).编写Reduce类
 
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.spark.sql.catalyst.expressions.If;

import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;

public class r4 extends Reducer {
    //定义一个容器用于存放数据
    private TreeMap treeMap = new TreeMap();

    @Override
    protected void reduce(Text key, Iterable values, Reducer.Context context) throws IOException, InterruptedException {
        bean bean = new bean();
        //定义i,用于累加拒单率总和
        Double sum = 0.0;
        //定义c,用于累加城市酒店总和
        Double count = 0.0;
        for (DoubleWritable value : values
        ) {
            sum = value.get() + sum;
            count++;
        }
        //v为省份的直销拒单率
        double v = sum / count;
        bean.setNorate(v);
        bean.setProvince(key.toString().split("t")[1]);
        //添加数据
        treeMap.put(bean, NullWritable.get());
        //当数据条数大于8，删除拒单率最小的数据
        if (treeMap.size() > 8) {
            treeMap.remove(treeMap.lastKey());
        }
    }

    @Override
    protected void cleanup(Reducer.Context context) throws IOException, InterruptedException {
        //遍历treemap集合
        Iterator iterator = treeMap.keySet().iterator();
        while (iterator.hasNext()) {
            five.bean next = iterator.next();
            context.write(next, NullWritable.get());
        }
    }
}

 
(4).编写drive类
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class d4 {
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
        //获取job对象
        Job job = Job.getInstance(new Configuration());
        //指点map/reduce类
        job.setMapperClass(m4.class);
        job.setReducerClass(r4.class);
        //指定jar包运行的路劲
        job.setJarByClass(d4.class);
        //指定map输出的数据类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(DoubleWritable.class);
        //指定最后输出的数据类型
        job.setOutputKeyClass(bean.class);
        job.setOutputValueClass(NullWritable.class);
        //指定输入/输出路劲
        FileInputFormat.setInputPaths(job, new Path("hdfs://master:9000/file3_1/jd_4706.csv"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://master:9000//bbbbbb"));
        job.waitForCompletion(true);
    }
}

 
(5).将项目打包提交到Hadoop集群
 
 (6).查看文件内容
 
 (7).在hive创建数据库five和表five_tb
 (8).导入数据到表five_tb中
 (9).查看表five_tb
 
 简简单单写代码