1.使用ECLIPSE工具打包運行WORDCOUNT實例，統計莎士比亞文集各單詞計數（文件SHAKESPEARE.TXT）。 ①WorldCount.java 中的main函數修改如下： ②導出WordCount的jar包： export->jar file->next->next->Main cl ...
1.使用ECLIPSE工具打包運行WORDCOUNT實例，統計莎士比亞文集各單詞計數（文件SHAKESPEARE.TXT）。
①WorldCount.java 中的main函數修改如下：
public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); //設置輸入文本路徑 FileInputFormat.addInputPath(job, new Path("/input")); //設置mp結果輸出路徑 FileOutputFormat.setOutputPath(job, new Path("/output/wordcount")); System.exit(job.waitForCompletion(true) ? 0 : 1); }
②導出WordCount的jar包：
export->jar file->next->next->Main class裡面選擇WordCount->Finish。
③使用scp將wc.jar拷貝到node1機器，創建目錄：hadoop fs –mkdir /input,將shakespeare.txt上傳到hdfs上，運行wc.jar文件：hadoop jar wc.jar
④使用hadoop fs -cat /output/wordcount/part-r-00000 grep|head -n 30 查看前30條輸出結果：
2.對於SOGOU_500W_UTF文件，完成下列程式設計內容：
（1） 統計每個用戶搜索的關鍵字總長度
Mapreduce程式：
public class sougou3 { public static class Sougou3Map extends Mapper<Object, Text, Text, Text> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split("\t"); String uid = vals[1]; String search = vals[2]; context.write(new Text(uid), new Text(search+"|"+search.length())); } } public static class Sougou3Reduce extends Reducer<Text, Text, Text, Text> { public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String result = ""; for (Text value : values) { String strVal = value.toString(); result += (strVal+" "); } context.write(new Text(key + "\t"), new Text(result)); } } }
輸出結果：
（2） 統計2011年12月30日1點到2點之間，搜索過的UID有哪些？
Mapreduce程式：
public class sougou1 { public static class Sougou1Map extends Mapper<Object, Text, Text, Text> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split("\t"); String time = vals[0]; String uid = vals[1]; //2008-07-10 19:20:00 String formatTime = time.substring(0,4)+"-"+time.substring(4,6)+"-"+time.substring(6,8)+" " +time.substring(8,10)+":"+time.substring(10,12)+":"+time.substring(12,14); SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); Date date; try { date = sdf.parse(formatTime); Date date1 = sdf.parse("2011-12-30 01:00:00"); Date date2 = sdf.parse("2011-12-30 02:00:00"); //日期在範圍區間上 if (date.getTime() > date1.getTime() && date.getTime() < date2.getTime()){ context.write(new Text(uid), new Text(formatTime)); } } catch (ParseException e) { e.printStackTrace(); } } } public static class Sougou1Reduce extends Reducer<Text, Text, Text, Text> { public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String result = ""; for (Text value : values) { result += value.toString()+"|"; } context.write(key, new Text(result)); } } }
輸出結果：
左邊是用戶id，右邊分別是時間，以“|”作為分割。
（3） 統計搜索過‘仙劍奇俠’的每個UID搜索該關鍵詞的次數。
Mapreduce程式：
public class sougou2 { public static class Sougou2Map extends Mapper<Object, Text, Text, IntWritable> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split("\t"); String uid = vals[1]; String search = vals[2]; if (search.equals("仙劍奇俠")){ context.write(new Text(uid), new IntWritable(1)); } } } public static class Sougou2Reduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int result = 0; for (IntWritable value : values) { result += value.get(); } context.write(new Text(key+"\t"), new IntWritable(result)); } } }
輸出結果：
UID為：6856e6e003a05cc912bfe13ebcea8a04的用戶搜索過“仙劍奇俠”共1次。
3.使用MAPREDUCE程式設計實現對文件中下列數據的排序操作78 11 56 87 25 63 19 22 55
Mapreduce程式：
public class Sort { //map將輸入中的value化成IntWritable類型，作為輸出的key public static class Map extends Mapper<Object,Text,IntWritable,NullWritable>{ private static IntWritable data=new IntWritable(); //實現map函數 public void map(Object key,Text value,Context context) throws IOException,InterruptedException{ String line=value.toString(); data.set(Integer.parseInt(line)); context.write(data, NullWritable.get()); } } //reduce將輸入中的key複製到輸出數據的key上， //然後根據輸入的value-list中元素的個數決定key的輸出次數 //用全局linenum來代表key的位次 public static class Reduce extends Reducer<IntWritable,NullWritable,IntWritable,NullWritable>{ //實現reduce函數 public void reduce(IntWritable key,Iterable<NullWritable> values,Context context) throws IOException,InterruptedException{ for(NullWritable val:values){ context.write(key, NullWritable.get()); } } } }
輸出內容為:
4.學生成績文件TXT內容（欄位用TAB鍵分隔）如下，使用MAPREDUCE計算每個學生的平均成績
李平 87 89 98 75
張三 66 78 69 70
李四 96 82 78 90
王五 82 77 74 86
趙六 88 72 81 76
Mapreduce 程式：
public class Score { public static class ScoreMap extends Mapper<Object, Text, Text, NullWritable> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { context.write(value, NullWritable.get()); } } public static class ScoreReduce extends Reducer<Text, NullWritable, Text, IntWritable> { public void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException { for (NullWritable nullWritable : values) { String line = key.toString(); String[] vals = line.split("\t"); String name = vals[0]; int val1 = Integer.parseInt(vals[1]); int val2 = Integer.parseInt(vals[2]); int val3 = Integer.parseInt(vals[3]); int average = (val1 + val2 + val3) / 3; context.write(new Text(name), new IntWritable(average)); } } } }
輸出結果為
