MapReduce案例----影评分析（年份，电影id，电影名字，平均评分）

题目：

现有如此三份数据：（这里只需用后两份）
1、users.dat    数据格式为：  2::M::56::16::70072
对应字段为：UserID BigInt, Gender String, Age Int, Occupation String, Zipcode String
对应字段中文解释：用户id，性别，年龄，职业，邮政编码
 
2、movies.dat        数据格式为：1::Toy Story (1995)::Animation|Children's|Comedy  ；  2::Jumanji (1995)::Adventure|Children's|Fantasy  ；  3::Grumpier Old Men (1995)::Comedy|Romance

对应字段为：MovieID BigInt, Title String, Genres String
对应字段中文解释：电影ID，电影名字，电影类型
 
3、ratings.dat        数据格式为：  1::1193::5::978300760  ；  1::661::3::978302109  ；  1::914::3::978301968
对应字段为：UserID BigInt, MovieID BigInt, Rating Double, Timestamped String
对应字段中文解释：用户ID，电影ID，评分，评分时间戳
 
用户ID，电影ID，评分，评分时间戳，性别，年龄，职业，邮政编码，电影名字，电影类型
userid, movieId, rate, ts, gender, age, occupation, zipcode, movieName, movieType
需求：
    关联两张表。
    计算每部电影的平均评分，并按评分大小进行排序。评分一样，按照电影名排序。
    （1）：按照年份进行分组，要求结果展示形式：
        年份，电影id，电影名字，平均分。

思路：

　　首先从 ratings.dat 中计算出电影id，平均评分。得出一个中间表。

　　通过分析，中间表比 movis.dat 要小，所以优先考虑将中间表加载到内存中，写入到一个hashmap中，做 map join。

　　Map 端处理movies.dat 中的数据，根据电影 id 关联 hashmap，得到该电影的平均评分，并提取出电影的年份。

　　将年份，电影id，电影名字，平均评分封装到一个对象中，然后自定义排序规则。按照电影平均评分大小排序。

　　然后自定义分区，将相同年份的分到一个分区中。使得相同年份的数据出现在一个文件中。

求出平均评分代码：

package com.lhb.demo;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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 Test1AvgRate {
    //map端
    public static class Test1AvgRateMapper extends Mapper<LongWritable, Text, LongWritable, DoubleWritable> {
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] split = value.toString().split("::");
            if (split.length >= 4) {
                context.write(new LongWritable(Long.valueOf(split[1])), new DoubleWritable(Double.valueOf(split[2])));
            }
        }
    }
    //reducer端
    public static class Test1AvgRateReducer extends Reducer<LongWritable, DoubleWritable, LongWritable, DoubleWritable> {
        protected void reduce(LongWritable key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {
            double sum = 0.0;
            int num = 0;
            for (DoubleWritable value : values) {
                sum += value.get();
                num++;
            }
            Double avg = sum / num;
            context.write(key, new DoubleWritable(avg));
        }
    }
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(Test1AvgRate.class);
        job.setMapperClass(Test1AvgRateMapper.class);
        job.setReducerClass(Test1AvgRateReducer.class);

        //指定map和reduce输出数据的类型
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(DoubleWritable.class);
        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(DoubleWritable.class);

        FileInputFormat.setInputPaths(job, new Path("文件所在路径"));
        FileSystem fs = FileSystem.get(conf);
        Path outPath = new Path("输出路径");
        //判断文件是否存在
        if (fs.exists(outPath)) {
            fs.delete(outPath, true);
        }
        FileOutputFormat.setOutputPath(job, outPath);
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}        

 

平均评分部分显示结果：

 

案例1代码：

package com.lhb.demo;

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

public class MovieBean1 implements WritableComparable<MovieBean1> {
    private int movie_year;
    private long movie_id;
    private String movie_name;
    private double movie_avg_rae;

    public MovieBean1() {
    }

    public MovieBean1(int movie_year, long movie_id, String movie_name, double movie_avg_rae) {
        this.movie_year = movie_year;
        this.movie_id = movie_id;
        this.movie_name = movie_name;
        this.movie_avg_rae = movie_avg_rae;
    }

    public int getMovie_year() {
        return movie_year;
    }

    public void setMovie_year(int movie_year) {
        this.movie_year = movie_year;
    }

    public long getMovie_id() {
        return movie_id;
    }

    public void setMovie_id(long movie_id) {
        this.movie_id = movie_id;
    }

    public String getMovie_name() {
        return movie_name;
    }

    public void setMovie_name(String movie_name) {
        this.movie_name = movie_name;
    }

    public double getMovie_avg_rae() {
        return movie_avg_rae;
    }

    public void setMovie_avg_rae(double movie_avg_rae) {
        this.movie_avg_rae = movie_avg_rae;
    }

    public String toString() {
        return "movie{" +
                "year=" + movie_year +
                ", id=" + movie_id +
                ", name='" + movie_name + '\'' +
                ", avg=" + movie_avg_rae +
                '}';
    }

    public int compareTo(MovieBean1 o) {
        if (o.movie_year == this.movie_year) {
            return o.movie_avg_rae > this.movie_avg_rae ? 1 : -1;
        } else {
            return o.movie_year > this.movie_year ? 1 : -1;
        }
    }

    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeInt(this.movie_year);
        dataOutput.writeLong(this.movie_id);
        dataOutput.writeUTF(this.movie_name);
        dataOutput.writeDouble(this.movie_avg_rae);
    }

    public void readFields(DataInput dataInput) throws IOException {
        this.movie_year = dataInput.readInt();
        this.movie_id = dataInput.readLong();
        this.movie_name = dataInput.readUTF();
        this.movie_avg_rae = dataInput.readDouble();
    }
}

package com.lhb.test.homework.test;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;

public class YearPartitioner extends Partitioner<MovieBean1, NullWritable> {
    public int getPartition(MovieBean1 movieBean1, NullWritable nullWritable, int i) {
        int movie_year = movieBean1.getMovie_year();
        return movie_year % i;
    }
}

package com.lhb.demo;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

public class Test01 {
    public static class Test01Mapper extends Mapper<LongWritable, Text, MovieBean1, NullWritable> {
        Map<Long, Double> rateMap;

        protected void setup(Context context) throws IOException, InterruptedException {
            rateMap = new HashMap<Long, Double>();

            BufferedReader br = new BufferedReader(new FileReader("求出平均评分的目录"));
            String line = "";
            while (StringUtils.isNotBlank((line = br.readLine()))) {
                String[] split = line.split("\t");
                if (split.length >= 2) {
                    rateMap.put(Long.valueOf(split[0]), Double.valueOf(split[1]));
                }
            }
            System.out.println(rateMap);
        }

        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] split = value.toString().split("::");
            String pattern = "\\(\\d{4}\\)";
            String line = value.toString();
            Pattern r = Pattern.compile(pattern);
            Matcher matcher = r.matcher(line);
            String s = "";
            if (matcher.find()) {
                s = matcher.group(0);
                s = s.replaceAll("\\(", "").replaceAll("\\)", "");
            }
            if (split.length >= 3) {
                Double avg_score = rateMap.getOrDefault(Long.valueOf(split[0]), 0.0);
                MovieBean1 movieBean1 = new MovieBean1();
                movieBean1.setMovie_avg_rae(avg_score);
                movieBean1.setMovie_name(split[1]);
                movieBean1.setMovie_year(Integer.valueOf(s));
                movieBean1.setMovie_id(Long.valueOf(split[0]));
                context.write(movieBean1, NullWritable.get());
            }
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(Test01.class);
        job.setMapperClass(Test01Mapper.class);

        //指定map输出数据的类型
        job.setMapOutputKeyClass(MovieBean1.class);
        job.setMapOutputValueClass(NullWritable.class);

        //局部优化
        job.setPartitionerClass(YearPartitioner.class);
        //分区
        job.setNumReduceTasks(20);

        FileInputFormat.setInputPaths(job, new Path("movie的目录"));
        FileSystem fs = FileSystem.get(conf);
        Path outPath = new Path("输出目录");
        if (fs.exists(outPath)) {
            fs.delete(outPath, true);
        }

        FileOutputFormat.setOutputPath(job, outPath);
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

 

运行部分结果如下：

 

数据如下：

链接: https://pan.baidu.com/s/1hc84MTWm5xosl4o_LrGoSw 提取码: z59t 

Spark案例----影评分析（年份，电影id，电影名字，平均评分）