MapReduce的几个实现

1.倒排索引的实现

 import java.io.IOException;
 import java.util.StringTokenizer;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 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.input.FileSplit;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

 public class InvertedIndex {

     public static class InvertedIndexMap extends Mapper<Object,Text,Text,Text>{

         private Text valueInfo = new Text();
         private Text keyInfo = new Text();
         private FileSplit split;

         public void map(Object key, Text value,Context context)
                 throws IOException, InterruptedException {
             //获取<key value>对所属的FileSplit对象
             split = (FileSplit) context.getInputSplit();
             StringTokenizer stk = new StringTokenizer(value.toString());
             while (stk.hasMoreElements()) {
                 //key值由（单词：URI）组成
                 keyInfo.set(stk.nextToken()+":"+split.getPath().toString());
                 //词频
                 valueInfo.set("1");
                 context.write(keyInfo, valueInfo);
             }
         }
     } 

     public static class InvertedIndexCombiner extends Reducer<Text,Text,Text,Text>{

         Text info = new Text();

         public void reduce(Text key, Iterable<Text> values,Context contex)
                 throws IOException, InterruptedException {
             int sum = 0;
             for (Text value : values) {
                 sum += Integer.parseInt(value.toString());
             }
             int splitIndex = key.toString().indexOf(":");
             //重新设置value值由（URI+:词频组成）
             info.set(key.toString().substring(splitIndex+1) +":"+ sum);
             //重新设置key值为单词
             key.set(key.toString().substring(0,splitIndex));
             contex.write(key, info);
         }
     }

     public static class InvertedIndexReduce extends Reducer<Text,Text,Text,Text>{

         private Text result = new Text();

         public void reduce(Text key, Iterable<Text> values,Context contex)
                 throws IOException, InterruptedException {
             //生成文档列表
             String fileList = new String();
             for (Text value : values) {
                 fileList += value.toString()+";";
             }
             result.set(fileList);
             contex.write(key, result);
         }
     }

     public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

         Configuration conf = new Configuration();

         Job job = new Job(conf,"InvertedIndex");

         job.setJarByClass(InvertedIndex.class);

         job.setMapperClass(InvertedIndexMap.class);
         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(Text.class);

         job.setCombinerClass(InvertedIndexCombiner.class);

         job.setReducerClass(InvertedIndexReduce.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(Text.class);

         FileInputFormat.addInputPath(job, new Path("./in/invertedindex/"));
         FileOutputFormat.setOutputPath(job, new Path("./out/"));

         System.exit(job.waitForCompletion(true)?0:1);

     }
 }

2.word count

 import java.io.IOException;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.IntWritable;
 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 org.apache.hadoop.util.GenericOptionsParser;

 public class WordCount {
     public static class WordMapper extends Mapper<Object, String, String, IntWritable> {
         private static final IntWritable one = new IntWritable(1);
         public void map(Object key, String value, Context context) throws IOException, InterruptedException {
             String[] words = value.split(" ");
             for (String word : words) {
                 context.write(word, one);
             }
         }
     }
     public static class WordReducer extends Reducer<String, Iterable<IntWritable>, String, IntWritable> {
         private static IntWritable ans = new IntWritable();
         public void reduce(String key, Iterable<IntWritable> value, Context context) throws IOException, InterruptedException {
             int sum = 0;
             for (IntWritable count : value) {
                 sum += count.get();
             }
             ans.set(sum);
             context.write(key, ans);
         }
     }
     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
         Configuration conf = new Configuration();
         String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
         if (otherArgs.length != 2) {
             System.err.println("Usage: wordCount <int> <count>");
             System.exit(2);
         }
         Job job = new Job(conf, "word count");
         job.setJarByClass(WordCount.class);
         job.setMapperClass(WordMapper.class);
         job.setCombinerClass(WordReducer.class);
         job.setReducerClass(WordReducer.class);
         job.setOutputKeyClass(String.class);
         job.setOutputValueClass(IntWritable.class);
         FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
         FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
         System.exit(job.waitForCompletion(true)?0:1);
     }
 }

3.找出访问量最多的表

 import org.apache.commons.lang.StringUtils;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 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;  

 /** 

  * 用Hadoop分析海量日志文件，每行日志记录了如下数据： 

  * TableName(表名)，Time(时间)，User(用户)，TimeSpan(时间开销) 

  * 要求编写MapReduce程序算出高峰时间段（如9-10点）哪张表被访问的最频繁 

  * 以及这段时间访问这张表最多的用户，以及这个用户访问这张表的总时间开销。 

  * @author drguo 

  *t003 6:00 u002 180 

  *t003 7:00 u002 180 

  *t003 7:08 u002 180 

  *t003 7:25 u002 180 

  *t002 8:00 u002 180 

  *t001 8:00 u001 240 

  *t001 9:00 u002 300 

  *t001 9:11 u001 240 

  *t003 9:26 u001 180 

  *t001 9:39 u001 300 

  * 

  * 

  * 先找出9-10点访问量最大的表 

  * 

  */
 //club.drguo.xx.mapreduce.tablecount.TableCount
 public class TableCount {
     public static class TableCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
         private Text k = new Text();
         @Override
         protected void map(LongWritable key, Text value, Context context)
                 throws IOException, InterruptedException {
             String line = value.toString();
             String[] strings = StringUtils.split(line, " ");
             String tabName = strings[0];
             String time = strings[1];
             String[] times = time.split(":");
             int hour = Integer.parseInt(times[0]);
             k.set(tabName);
             if(hour==9){
                 context.write(k, new LongWritable(1));
                 System.out.println("-----------------------------------------------"+k);
             }
         }
     }
     public static class TableCountReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
         private TreeMap<Text, Long> map = new TreeMap<Text, Long>();
         @Override
         protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
             Text tabName = new Text(key.toString());//不要直接Text tabName = key;
             long count = 0;
             for(LongWritable value : values){
                 count += value.get();
             }
             System.out.println(tabName+"--------------------------"+count);
             map.put(tabName, count);
         }
         @Override
         protected void cleanup(Reducer<Text, LongWritable, Text, LongWritable>.Context context)
                 throws IOException, InterruptedException {
             Text tableName = null;
             Long maxCount = 0L;
             for(Text key : map.keySet()){
                 System.out.println("key="+key+"-----------------value="+map.get(key));
                 while(map.get(key)>maxCount){
                     maxCount = map.get(key);
                     tableName = key;
                 }
             }
             context.write(tableName, new LongWritable(maxCount));
         }
     }
     public static void main(String[] args) throws Exception {
         Configuration configuration = new Configuration();
         Job job = Job.getInstance(configuration,"tablejob");
         job.setJarByClass(TableCount.class);  

         job.setMapperClass(TableCountMapper.class);
         job.setReducerClass(TableCountReducer.class);  

         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(LongWritable.class);  

         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(LongWritable.class);  

         FileInputFormat.setInputPaths(job, "hdfs://localhost:9000/log");
         FileOutputFormat.setOutputPath(job, new Path("hdfs://localhost:9000/tablecount"));  

         System.exit(job.waitForCompletion(true)?0:1);
     }
 }