第一題
下面是三種商品的銷售數據

要求:根據以上數據,用 MapReduce 統計出如下數據:
1、每種商品的銷售總金額,並降序排序
2、每種商品銷售額最多的三周
第二題:MapReduce 題
現有如下數據文件需要處理:
格式:CSV
數據樣例:
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:用戶 ID,位置 ID,開始時間,停留時長(分鍾)
數據意義:某個用戶在某個位置從某個時刻開始停留了多長時間
處理邏輯: 對同一個用戶,在同一個位置,連續的多條記錄進行合並
合並原則:開始時間取最早的,停留時長加和
要求:請編寫 MapReduce 程序實現
其他:只有數據樣例,沒有數據。
UserLocationMR.java
1 /** 2 測試數據: 3 user_a location_a 2018-01-01 08:00:00 60 4 user_a location_a 2018-01-01 09:00:00 60 5 user_a location_a 2018-01-01 11:00:00 60 6 user_a location_a 2018-01-01 12:00:00 60 7 user_a location_b 2018-01-01 10:00:00 60 8 user_a location_c 2018-01-01 08:00:00 60 9 user_a location_c 2018-01-01 09:00:00 60 10 user_a location_c 2018-01-01 10:00:00 60 11 user_b location_a 2018-01-01 15:00:00 60 12 user_b location_a 2018-01-01 16:00:00 60 13 user_b location_a 2018-01-01 18:00:00 60 14 15 16 結果數據: 17 user_a location_a 2018-01-01 08:00:00 120 18 user_a location_a 2018-01-01 11:00:00 120 19 user_a location_b 2018-01-01 10:00:00 60 20 user_a location_c 2018-01-01 08:00:00 180 21 user_b location_a 2018-01-01 15:00:00 120 22 user_b location_a 2018-01-01 18:00:00 60 23 24 25 */ 26 public class UserLocationMR { 27 28 public static void main(String[] args) throws Exception { 29 // 指定hdfs相關的參數 30 Configuration conf = new Configuration(); 31 // conf.set("fs.defaultFS", "hdfs://hadoop02:9000"); 32 // System.setProperty("HADOOP_USER_NAME", "hadoop"); 33 34 Job job = Job.getInstance(conf); 35 // 設置jar包所在路徑 36 job.setJarByClass(UserLocationMR.class); 37 38 // 指定mapper類和reducer類 39 job.setMapperClass(UserLocationMRMapper.class); 40 job.setReducerClass(UserLocationMRReducer.class); 41 42 // 指定maptask的輸出類型 43 job.setMapOutputKeyClass(UserLocation.class); 44 job.setMapOutputValueClass(NullWritable.class); 45 // 指定reducetask的輸出類型 46 job.setOutputKeyClass(UserLocation.class); 47 job.setOutputValueClass(NullWritable.class); 48 49 job.setGroupingComparatorClass(UserLocationGC.class); 50 51 // 指定該mapreduce程序數據的輸入和輸出路徑 52 Path inputPath = new Path("D:\\武文\\second\\input"); 53 Path outputPath = new Path("D:\\武文\\second\\output2"); 54 FileSystem fs = FileSystem.get(conf); 55 if (fs.exists(outputPath)) { 56 fs.delete(outputPath, true); 57 } 58 FileInputFormat.setInputPaths(job, inputPath); 59 FileOutputFormat.setOutputPath(job, outputPath); 60 61 // 最后提交任務 62 boolean waitForCompletion = job.waitForCompletion(true); 63 System.exit(waitForCompletion ? 0 : 1); 64 } 65 66 private static class UserLocationMRMapper extends Mapper<LongWritable, Text, UserLocation, NullWritable> { 67 68 UserLocation outKey = new UserLocation(); 69 70 /** 71 * value = user_a,location_a,2018-01-01 12:00:00,60 72 */ 73 @Override 74 protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 75 76 String[] split = value.toString().split(","); 77 78 outKey.set(split); 79 80 context.write(outKey, NullWritable.get()); 81 } 82 } 83 84 private static class UserLocationMRReducer extends Reducer<UserLocation, NullWritable, UserLocation, NullWritable> { 85 86 SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); 87 88 UserLocation outKey = new UserLocation(); 89 90 /** 91 * user_a location_a 2018-01-01 08:00:00 60 92 * user_a location_a 2018-01-01 09:00:00 60 93 * user_a location_a 2018-01-01 11:00:00 60 94 * user_a location_a 2018-01-01 12:00:00 60 95 */ 96 @Override 97 protected void reduce(UserLocation key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException { 98 99 int count = 0; 100 for (NullWritable nvl : values) { 101 count++; 102 // 如果是這一組key-value中的第一個元素時,直接賦值給outKey對象。基礎對象 103 if (count == 1) { 104 // 復制值 105 outKey.set(key); 106 } else { 107 108 // 有可能連續,有可能不連續, 連續則繼續變量, 否則輸出 109 long current_timestamp = 0; 110 long last_timestamp = 0; 111 try { 112 // 這是新遍歷出來的記錄的時間戳 113 current_timestamp = sdf.parse(key.getTime()).getTime(); 114 // 這是上一條記錄的時間戳 和 停留時間之和 115 last_timestamp = sdf.parse(outKey.getTime()).getTime() + outKey.getDuration() * 60 * 1000; 116 } catch (ParseException e) { 117 e.printStackTrace(); 118 } 119 120 // 如果相等,證明是連續記錄,所以合並 121 if (current_timestamp == last_timestamp) { 122 123 outKey.setDuration(outKey.getDuration() + key.getDuration()); 124 125 } else { 126 127 // 先輸出上一條記錄 128 context.write(outKey, nvl); 129 130 // 然后再次記錄當前遍歷到的這一條記錄 131 outKey.set(key); 132 } 133 } 134 } 135 // 最后無論如何,還得輸出最后一次 136 context.write(outKey, NullWritable.get()); 137 } 138 } 139 }
UserLocation.java
1 public class UserLocation implements WritableComparable<UserLocation> { 2 3 private String userid; 4 private String locationid; 5 private String time; 6 private long duration; 7 8 @Override 9 public String toString() { 10 return userid + "\t" + locationid + "\t" + time + "\t" + duration; 11 } 12 13 public UserLocation() { 14 super(); 15 } 16 17 public void set(String[] split){ 18 this.setUserid(split[0]); 19 this.setLocationid(split[1]); 20 this.setTime(split[2]); 21 this.setDuration(Long.parseLong(split[3])); 22 } 23 24 public void set(UserLocation ul){ 25 this.setUserid(ul.getUserid()); 26 this.setLocationid(ul.getLocationid()); 27 this.setTime(ul.getTime()); 28 this.setDuration(ul.getDuration()); 29 } 30 31 public UserLocation(String userid, String locationid, String time, long duration) { 32 super(); 33 this.userid = userid; 34 this.locationid = locationid; 35 this.time = time; 36 this.duration = duration; 37 } 38 39 public String getUserid() { 40 return userid; 41 } 42 43 public void setUserid(String userid) { 44 this.userid = userid; 45 } 46 47 public String getLocationid() { 48 return locationid; 49 } 50 51 public void setLocationid(String locationid) { 52 this.locationid = locationid; 53 } 54 55 public String getTime() { 56 return time; 57 } 58 59 public void setTime(String time) { 60 this.time = time; 61 } 62 63 public long getDuration() { 64 return duration; 65 } 66 67 public void setDuration(long duration) { 68 this.duration = duration; 69 } 70 71 @Override 72 public void write(DataOutput out) throws IOException { 73 // TODO Auto-generated method stub 74 out.writeUTF(userid); 75 out.writeUTF(locationid); 76 out.writeUTF(time); 77 out.writeLong(duration); 78 } 79 80 @Override 81 public void readFields(DataInput in) throws IOException { 82 // TODO Auto-generated method stub 83 this.userid = in.readUTF(); 84 this.locationid = in.readUTF(); 85 this.time = in.readUTF(); 86 this.duration = in.readLong(); 87 } 88 89 /** 90 * 排序規則 91 * 92 * 按照 userid locationid 和 time 排序 都是 升序 93 */ 94 @Override 95 public int compareTo(UserLocation o) { 96 97 int diff_userid = o.getUserid().compareTo(this.getUserid()); 98 if(diff_userid == 0){ 99 100 int diff_location = o.getLocationid().compareTo(this.getLocationid()); 101 if(diff_location == 0){ 102 103 int diff_time = o.getTime().compareTo(this.getTime()); 104 if(diff_time == 0){ 105 return 0; 106 }else{ 107 return diff_time > 0 ? -1 : 1; 108 } 109 110 }else{ 111 return diff_location > 0 ? -1 : 1; 112 } 113 114 }else{ 115 return diff_userid > 0 ? -1 : 1; 116 } 117 } 118 }
UserLocationGC.java
1 public class UserLocationGC extends WritableComparator{ 2 3 public UserLocationGC(){ 4 super(UserLocation.class, true); 5 } 6 7 @Override 8 public int compare(WritableComparable a, WritableComparable b) { 9 10 UserLocation ul_a = (UserLocation)a; 11 UserLocation ul_b = (UserLocation)b; 12 13 int diff_userid = ul_a.getUserid().compareTo(ul_b.getUserid()); 14 if(diff_userid == 0){ 15 16 int diff_location = ul_a.getLocationid().compareTo(ul_b.getLocationid()); 17 if(diff_location == 0){ 18 19 return 0; 20 21 }else{ 22 return diff_location > 0 ? -1 : 1; 23 } 24 25 }else{ 26 return diff_userid > 0 ? -1 : 1; 27 } 28 } 29 }
第三題:MapReduce 題--倒排索引
概念: 倒排索引(Inverted Index),也常被稱為反向索引、置入檔案或反向檔案,是一種索引方法, 被用來存儲在全文搜索下某個單詞在一個文檔或者一組文檔中的存儲位置的映射。它是文檔 檢索系統中最常用的數據結構。了解詳情可自行百度
有兩份數據:
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
題目一:編寫 MapReduce 求出以下格式的結果數據:統計每個關鍵詞在每個文檔中當中的 第幾行出現了多少次 例如,huangxiaoming 關鍵詞的格式:
huangixaoming mapreduce-4-1.txt:2,2; mapreduce-4-1.txt:4,1;mapreduce-4-2.txt:3,1
以上答案的意義:
關鍵詞 huangxiaoming 在第一份文檔 mapreduce-4-1.txt 中的第 2 行出現了 2 次
關鍵詞 huangxiaoming 在第一份文檔 mapreduce-4-1.txt 中的第 4 行出現了 1 次
關鍵詞 huangxiaoming 在第二份文檔 mapreduce-4-2.txt 中的第 3 行出現了 1 次
題目二:編寫 MapReduce 程序求出每個關鍵詞在每個文檔出現了多少次,並且按照出現次 數降序排序
例如:
huangixaoming mapreduce-4-1.txt,3;mapreduce-4-2.txt,1
以上答案的含義: 表示關鍵詞 huangxiaoming 在第一份文檔 mapreduce-4-1.txt 中出現了 3 次,在第二份文檔mapreduce-4-2.txt 中出現了 1 次
