标签:封装 规则 nts oop ring set inpu 出现 大小
题目:
1 现有如此三份数据:(这里只需用后两份) 2 1、users.dat 数据格式为: 2::M::56::16::70072 3 对应字段为:UserID BigInt, Gender String, Age Int, Occupation String, Zipcode String 4 对应字段中文解释:用户id,性别,年龄,职业,邮政编码 5 6 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
7 对应字段为:MovieID BigInt, Title String, Genres String 8 对应字段中文解释:电影ID,电影名字,电影类型 9 10 3、ratings.dat 数据格式为: 1::1193::5::978300760 ; 1::661::3::978302109 ; 1::914::3::978301968 11 对应字段为:UserID BigInt, MovieID BigInt, Rating Double, Timestamped String 12 对应字段中文解释:用户ID,电影ID,评分,评分时间戳 13 14 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 15 userid, movieId, rate, ts, gender, age, occupation, zipcode, movieName, movieType 16 需求: 17 关联两张表。 18 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。 19 (1):按照年份进行分组,要求结果展示形式: 20 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:
1 package com.lhb.demo; 2 import org.apache.hadoop.conf.Configuration; 3 import org.apache.hadoop.fs.FileSystem; 4 import org.apache.hadoop.fs.Path; 5 import org.apache.hadoop.io.DoubleWritable; 6 import org.apache.hadoop.io.LongWritable; 7 import org.apache.hadoop.io.Text; 8 import org.apache.hadoop.mapreduce.Job; 9 import org.apache.hadoop.mapreduce.Mapper; 10 import org.apache.hadoop.mapreduce.Reducer; 11 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 13 import java.io.IOException; 14 15 public class Test1AvgRate { 16 //map端 17 public static class Test1AvgRateMapper extends Mapper<LongWritable, Text, LongWritable, DoubleWritable> { 18 protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 19 String[] split = value.toString().split("::"); 20 if (split.length >= 4) { 21 context.write(new LongWritable(Long.valueOf(split[1])), new DoubleWritable(Double.valueOf(split[2]))); 22 } 23 } 24 } 25 //reducer端 26 public static class Test1AvgRateReducer extends Reducer<LongWritable, DoubleWritable, LongWritable, DoubleWritable> { 27 protected void reduce(LongWritable key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException { 28 double sum = 0.0; 29 int num = 0; 30 for (DoubleWritable value : values) { 31 sum += value.get(); 32 num++; 33 } 34 Double avg = sum / num; 35 context.write(key, new DoubleWritable(avg)); 36 } 37 } 38 public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { 39 Configuration conf = new Configuration(); 40 Job job = Job.getInstance(conf); 41 job.setJarByClass(Test1AvgRate.class); 42 job.setMapperClass(Test1AvgRateMapper.class); 43 job.setReducerClass(Test1AvgRateReducer.class); 44 45 //指定map和reduce输出数据的类型 46 job.setMapOutputKeyClass(LongWritable.class); 47 job.setMapOutputValueClass(DoubleWritable.class); 48 job.setOutputKeyClass(LongWritable.class); 49 job.setOutputValueClass(DoubleWritable.class); 50 51 FileInputFormat.setInputPaths(job, new Path("文件所在路径")); 52 FileSystem fs = FileSystem.get(conf); 53 Path outPath = new Path("输出路径"); 54 //判断文件是否存在 55 if (fs.exists(outPath)) { 56 fs.delete(outPath, true); 57 } 58 FileOutputFormat.setOutputPath(job, outPath); 59 boolean b = job.waitForCompletion(true); 60 System.exit(b ? 0 : 1); 61 } 62 }
平均评分部分显示结果:
案例1代码:
1 package com.lhb.demo; 2 3 import org.apache.hadoop.io.WritableComparable; 4 import java.io.DataInput; 5 import java.io.DataOutput; 6 import java.io.IOException; 7 8 public class MovieBean1 implements WritableComparable<MovieBean1> { 9 private int movie_year; 10 private long movie_id; 11 private String movie_name; 12 private double movie_avg_rae; 13 14 public MovieBean1() { 15 } 16 17 public MovieBean1(int movie_year, long movie_id, String movie_name, double movie_avg_rae) { 18 this.movie_year = movie_year; 19 this.movie_id = movie_id; 20 this.movie_name = movie_name; 21 this.movie_avg_rae = movie_avg_rae; 22 } 23 24 public int getMovie_year() { 25 return movie_year; 26 } 27 28 public void setMovie_year(int movie_year) { 29 this.movie_year = movie_year; 30 } 31 32 public long getMovie_id() { 33 return movie_id; 34 } 35 36 public void setMovie_id(long movie_id) { 37 this.movie_id = movie_id; 38 } 39 40 public String getMovie_name() { 41 return movie_name; 42 } 43 44 public void setMovie_name(String movie_name) { 45 this.movie_name = movie_name; 46 } 47 48 public double getMovie_avg_rae() { 49 return movie_avg_rae; 50 } 51 52 public void setMovie_avg_rae(double movie_avg_rae) { 53 this.movie_avg_rae = movie_avg_rae; 54 } 55 56 public String toString() { 57 return "movie{" + 58 "year=" + movie_year + 59 ", id=" + movie_id + 60 ", name=‘" + movie_name + ‘\‘‘ + 61 ", avg=" + movie_avg_rae + 62 ‘}‘; 63 } 64 65 public int compareTo(MovieBean1 o) { 66 if (o.movie_year == this.movie_year) { 67 return o.movie_avg_rae > this.movie_avg_rae ? 1 : -1; 68 } else { 69 return o.movie_year > this.movie_year ? 1 : -1; 70 } 71 } 72 73 public void write(DataOutput dataOutput) throws IOException { 74 dataOutput.writeInt(this.movie_year); 75 dataOutput.writeLong(this.movie_id); 76 dataOutput.writeUTF(this.movie_name); 77 dataOutput.writeDouble(this.movie_avg_rae); 78 } 79 80 public void readFields(DataInput dataInput) throws IOException { 81 this.movie_year = dataInput.readInt(); 82 this.movie_id = dataInput.readLong(); 83 this.movie_name = dataInput.readUTF(); 84 this.movie_avg_rae = dataInput.readDouble(); 85 } 86 }
1 package com.lhb.test.homework.test; 2 import org.apache.hadoop.io.NullWritable; 3 import org.apache.hadoop.mapreduce.Partitioner; 4 5 public class YearPartitioner extends Partitioner<MovieBean1, NullWritable> { 6 public int getPartition(MovieBean1 movieBean1, NullWritable nullWritable, int i) { 7 int movie_year = movieBean1.getMovie_year(); 8 return movie_year % i; 9 } 10 }
1 package com.lhb.demo; 2 3 import org.apache.commons.lang.StringUtils; 4 import org.apache.hadoop.conf.Configuration; 5 import org.apache.hadoop.fs.FileSystem; 6 import org.apache.hadoop.fs.Path; 7 import org.apache.hadoop.io.LongWritable; 8 import org.apache.hadoop.io.NullWritable; 9 import org.apache.hadoop.io.Text; 10 import org.apache.hadoop.mapreduce.Job; 11 import org.apache.hadoop.mapreduce.Mapper; 12 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 13 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 14 import java.io.BufferedReader; 15 import java.io.FileReader; 16 import java.io.IOException; 17 import java.util.HashMap; 18 import java.util.Map; 19 import java.util.regex.Matcher; 20 import java.util.regex.Pattern; 21 22 public class Test01 { 23 public static class Test01Mapper extends Mapper<LongWritable, Text, MovieBean1, NullWritable> { 24 Map<Long, Double> rateMap; 25 26 protected void setup(Context context) throws IOException, InterruptedException { 27 rateMap = new HashMap<Long, Double>(); 28 29 BufferedReader br = new BufferedReader(new FileReader("求出平均评分的目录")); 30 String line = ""; 31 while (StringUtils.isNotBlank((line = br.readLine()))) { 32 String[] split = line.split("\t"); 33 if (split.length >= 2) { 34 rateMap.put(Long.valueOf(split[0]), Double.valueOf(split[1])); 35 } 36 } 37 System.out.println(rateMap); 38 } 39 40 protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 41 String[] split = value.toString().split("::"); 42 String pattern = "\\(\\d{4}\\)"; 43 String line = value.toString(); 44 Pattern r = Pattern.compile(pattern); 45 Matcher matcher = r.matcher(line); 46 String s = ""; 47 if (matcher.find()) { 48 s = matcher.group(0); 49 s = s.replaceAll("\\(", "").replaceAll("\\)", ""); 50 } 51 if (split.length >= 3) { 52 Double avg_score = rateMap.getOrDefault(Long.valueOf(split[0]), 0.0); 53 MovieBean1 movieBean1 = new MovieBean1(); 54 movieBean1.setMovie_avg_rae(avg_score); 55 movieBean1.setMovie_name(split[1]); 56 movieBean1.setMovie_year(Integer.valueOf(s)); 57 movieBean1.setMovie_id(Long.valueOf(split[0])); 58 context.write(movieBean1, NullWritable.get()); 59 } 60 } 61 } 62 63 public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { 64 Configuration conf = new Configuration(); 65 Job job = Job.getInstance(conf); 66 job.setJarByClass(Test01.class); 67 job.setMapperClass(Test01Mapper.class); 68 69 //指定map输出数据的类型 70 job.setMapOutputKeyClass(MovieBean1.class); 71 job.setMapOutputValueClass(NullWritable.class); 72 73 //局部优化 74 job.setPartitionerClass(YearPartitioner.class); 75 //分区 76 job.setNumReduceTasks(20); 77 78 FileInputFormat.setInputPaths(job, new Path("movie的目录")); 79 FileSystem fs = FileSystem.get(conf); 80 Path outPath = new Path("输出目录"); 81 if (fs.exists(outPath)) { 82 fs.delete(outPath, true); 83 } 84 85 FileOutputFormat.setOutputPath(job, outPath); 86 boolean b = job.waitForCompletion(true); 87 System.exit(b ? 0 : 1); 88 } 89 }
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
MapReduce案例----影评分析(年份,电影id,电影名字,平均评分)
标签:封装 规则 nts oop ring set inpu 出现 大小
原文地址:https://www.cnblogs.com/hong-bo/p/11448207.html