Hadoop学习之路（二十七）MapReduce的API使用（四）

第一题

下面是三种商品的销售数据

要求：根据以上数据，用 MapReduce 统计出如下数据：

1、每种商品的销售总金额，并降序排序

2、每种商品销售额最多的三周

第二题：MapReduce 题

现有如下数据文件需要处理:

格式：CSV

数据样例：

user_a,location_a,2018-01-01 08:00:00,60

user_a,location_a,2018-01-01 09:00:00,60

user_a,location_b,2018-01-01 10:00:00,60

user_a,location_a,2018-01-01 11:00:00,60

字段：用户 ID，位置 ID，开始时间，停留时长（分钟）

数据意义：某个用户在某个位置从某个时刻开始停留了多长时间

处理逻辑：对同一个用户，在同一个位置，连续的多条记录进行合并

合并原则：开始时间取最早的，停留时长加和

要求：请编写 MapReduce 程序实现

其他：只有数据样例，没有数据。

UserLocationMR.java

 /**

 测试数据：

 user_a    location_a    2018-01-01 08:00:00    60

 user_a    location_a    2018-01-01 09:00:00    60

 user_a    location_a    2018-01-01 11:00:00    60

 user_a    location_a    2018-01-01 12:00:00    60

 user_a    location_b    2018-01-01 10:00:00    60

 user_a    location_c    2018-01-01 08:00:00    60

 user_a    location_c    2018-01-01 09:00:00    60

 user_a    location_c    2018-01-01 10:00:00    60

 user_b    location_a    2018-01-01 15:00:00    60

 user_b    location_a    2018-01-01 16:00:00    60

 user_b    location_a    2018-01-01 18:00:00    60

 结果数据：

 user_a    location_a    2018-01-01 08:00:00    120

 user_a    location_a    2018-01-01 11:00:00    120

 user_a    location_b    2018-01-01 10:00:00    60

 user_a    location_c    2018-01-01 08:00:00    180

 user_b    location_a    2018-01-01 15:00:00    120

 user_b    location_a    2018-01-01 18:00:00    60

  */

 public class UserLocationMR {

     public static void main(String[] args) throws Exception {

         // 指定hdfs相关的参数

         Configuration conf = new Configuration();

         //        conf.set("fs.defaultFS", "hdfs://hadoop02:9000");

         //        System.setProperty("HADOOP_USER_NAME", "hadoop");

         Job job = Job.getInstance(conf);

         // 设置jar包所在路径

         job.setJarByClass(UserLocationMR.class);

         // 指定mapper类和reducer类

         job.setMapperClass(UserLocationMRMapper.class);

         job.setReducerClass(UserLocationMRReducer.class);

         // 指定maptask的输出类型

         job.setMapOutputKeyClass(UserLocation.class);

         job.setMapOutputValueClass(NullWritable.class);

         // 指定reducetask的输出类型

         job.setOutputKeyClass(UserLocation.class);

         job.setOutputValueClass(NullWritable.class);

         job.setGroupingComparatorClass(UserLocationGC.class);

         // 指定该mapreduce程序数据的输入和输出路径

         Path inputPath = new Path("D:\\武文\\second\\input");

         Path outputPath = new Path("D:\\武文\\second\\output2");

         FileSystem fs = FileSystem.get(conf);

         if (fs.exists(outputPath)) {

             fs.delete(outputPath, true);

         }

         FileInputFormat.setInputPaths(job, inputPath);

         FileOutputFormat.setOutputPath(job, outputPath);

         // 最后提交任务

         boolean waitForCompletion = job.waitForCompletion(true);

         System.exit(waitForCompletion ? 0 : 1);

     }

     private static class UserLocationMRMapper extends Mapper<LongWritable, Text, UserLocation, NullWritable> {

         UserLocation outKey = new UserLocation();

         /**

          * value = user_a,location_a,2018-01-01 12:00:00,60

          */

         @Override

         protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

             String[] split = value.toString().split(",");

             outKey.set(split);

             context.write(outKey, NullWritable.get());

         }

     }

     private static class UserLocationMRReducer extends Reducer<UserLocation, NullWritable, UserLocation, NullWritable> {

         SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");

         UserLocation outKey = new UserLocation();

         /**

          * user_a    location_a    2018-01-01 08:00:00    60

          * user_a    location_a    2018-01-01 09:00:00    60

          * user_a    location_a    2018-01-01 11:00:00    60

          * user_a    location_a    2018-01-01 12:00:00    60

          */

         @Override

         protected void reduce(UserLocation key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {

             int count = 0;

             for (NullWritable nvl : values) {

                 count++;

                 // 如果是这一组key-value中的第一个元素时，直接赋值给outKey对象。基础对象

                 if (count == 1) {

                     // 复制值

                     outKey.set(key);

                 } else {

                     // 有可能连续，有可能不连续，  连续则继续变量， 否则输出

                     long current_timestamp = 0;

                     long last_timestamp = 0;

                     try {

                         // 这是新遍历出来的记录的时间戳

                         current_timestamp = sdf.parse(key.getTime()).getTime();

                         // 这是上一条记录的时间戳 和 停留时间之和

                         last_timestamp = sdf.parse(outKey.getTime()).getTime() + outKey.getDuration() * 60 * 1000;

                     } catch (ParseException e) {

                         e.printStackTrace();

                     }

                     // 如果相等，证明是连续记录，所以合并

                     if (current_timestamp == last_timestamp) {

                         outKey.setDuration(outKey.getDuration() + key.getDuration());

                     } else {

                         // 先输出上一条记录

                         context.write(outKey, nvl);

                         // 然后再次记录当前遍历到的这一条记录

                         outKey.set(key);

                     }

                 }

             }

             // 最后无论如何，还得输出最后一次

             context.write(outKey, NullWritable.get());

         }

     }

 }

UserLocation.java

 public class UserLocation implements WritableComparable<UserLocation> {

     private String userid;

     private String locationid;

     private String time;

     private long duration;

     @Override

     public String toString() {

         return userid + "\t" + locationid + "\t" + time + "\t" + duration;

     }

     public UserLocation() {

         super();

     }

     public void set(String[] split){

         this.setUserid(split[0]);

         this.setLocationid(split[1]);

         this.setTime(split[2]);

         this.setDuration(Long.parseLong(split[3]));

     }

     public void set(UserLocation ul){

         this.setUserid(ul.getUserid());

         this.setLocationid(ul.getLocationid());

         this.setTime(ul.getTime());

         this.setDuration(ul.getDuration());

     }

     public UserLocation(String userid, String locationid, String time, long duration) {

         super();

         this.userid = userid;

         this.locationid = locationid;

         this.time = time;

         this.duration = duration;

     }

     public String getUserid() {

         return userid;

     }

     public void setUserid(String userid) {

         this.userid = userid;

     }

     public String getLocationid() {

         return locationid;

     }

     public void setLocationid(String locationid) {

         this.locationid = locationid;

     }

     public String getTime() {

         return time;

     }

     public void setTime(String time) {

         this.time = time;

     }

     public long getDuration() {

         return duration;

     }

     public void setDuration(long duration) {

         this.duration = duration;

     }

     @Override

     public void write(DataOutput out) throws IOException {

         // TODO Auto-generated method stub

         out.writeUTF(userid);

         out.writeUTF(locationid);

         out.writeUTF(time);

         out.writeLong(duration);

     }

     @Override

     public void readFields(DataInput in) throws IOException {

         // TODO Auto-generated method stub

         this.userid = in.readUTF();

         this.locationid = in.readUTF();

         this.time = in.readUTF();

         this.duration = in.readLong();

     }

     /**

      * 排序规则

      *

      * 按照 userid  locationid  和  time 排序  都是 升序

      */

     @Override

     public int compareTo(UserLocation o) {

         int diff_userid = o.getUserid().compareTo(this.getUserid());

         if(diff_userid == 0){

             int diff_location = o.getLocationid().compareTo(this.getLocationid());

             if(diff_location == 0){

                 int diff_time = o.getTime().compareTo(this.getTime());

                 if(diff_time == 0){

                     return 0;

                 }else{

                     return diff_time > 0 ? -1 : 1;

                 }

             }else{

                 return diff_location > 0 ? -1 : 1;

             }

         }else{

             return diff_userid > 0 ? -1 : 1;

         }

     }

 }

UserLocationGC.java

 public class UserLocationGC extends WritableComparator{

     public UserLocationGC(){

         super(UserLocation.class, true);

     }

     @Override

     public int compare(WritableComparable a, WritableComparable b) {

         UserLocation ul_a = (UserLocation)a;

         UserLocation ul_b = (UserLocation)b;

         int diff_userid = ul_a.getUserid().compareTo(ul_b.getUserid());

         if(diff_userid == 0){

             int diff_location = ul_a.getLocationid().compareTo(ul_b.getLocationid());

             if(diff_location == 0){

                 return 0;

             }else{

                 return diff_location > 0 ? -1 : 1;

             }

         }else{

             return diff_userid > 0 ? -1 : 1;

         }

     }

 }

第三题：MapReduce 题--倒排索引

概念：倒排索引（Inverted Index），也常被称为反向索引、置入档案或反向档案，是一种索引方法，被用来存储在全文搜索下某个单词在一个文档或者一组文档中的存储位置的映射。它是文档检索系统中最常用的数据结构。了解详情可自行百度

有两份数据：

mapreduce-4-1.txt

huangbo love xuzheng

huangxiaoming love baby huangxiaoming love yangmi

liangchaowei love liujialing

huangxiaoming xuzheng huangbo wangbaoqiang

mapreduce-4-2.txt

hello huangbo

hello xuzheng

hello huangxiaoming

题目一：编写 MapReduce 求出以下格式的结果数据：统计每个关键词在每个文档中当中的第几行出现了多少次例如，huangxiaoming 关键词的格式：

huangixaoming mapreduce-4-1.txt:2,2; mapreduce-4-1.txt:4,1;mapreduce-4-2.txt:3,1

以上答案的意义：

关键词 huangxiaoming 在第一份文档 mapreduce-4-1.txt 中的第 2 行出现了 2 次

关键词 huangxiaoming 在第一份文档 mapreduce-4-1.txt 中的第 4 行出现了 1 次

关键词 huangxiaoming 在第二份文档 mapreduce-4-2.txt 中的第 3 行出现了 1 次

题目二：编写 MapReduce 程序求出每个关键词在每个文档出现了多少次，并且按照出现次数降序排序

例如：

huangixaoming mapreduce-4-1.txt,3;mapreduce-4-2.txt,1

以上答案的含义：表示关键词 huangxiaoming 在第一份文档 mapreduce-4-1.txt 中出现了 3 次，在第二份文档mapreduce-4-2.txt 中出现了 1 次

巴特西

Hadoop学习之路（二十七）MapReduce的API使用（四）

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