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Hadoop: MapReduce2的几个基本示例

日期：2015-05-29点击：640收藏

1) WordCount

这个就不多说了,满大街都是,网上有几篇对WordCount的详细分析

http://www.sxt.cn/u/235/blog/5809

http://www.cnblogs.com/zhanghuijunjava/archive/2013/04/27/3036549.html

这二篇都写得不错, 特别几张图画得很清晰

2) 去重处理(Distinct)

类似于db中的select distinct(x) from table , 去重处理甚至比WordCount还要简单,假如我们要对以下文件的内容做去重处理(注:该文件也是后面几个示例的输入参数)

2 8 8 3 2 3 5 3 0 2 7

基本上啥也不用做,在map阶段,把每一行的值当成key分发下去,然后在reduce阶段回收上来就可以了.

注:里面用到了一个自己写的类HDFSUtil,可以在 hadoop: hdfs API示例一文中找到.

原理:map阶段完成后,在reduce开始之前,会有一个combine的过程,相同的key值会自动合并,所以自然而然的就去掉了重复.

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.NullWritable;  6 import org.apache.hadoop.io.Text;  7 import org.apache.hadoop.mapreduce.Job;  8 import org.apache.hadoop.mapreduce.Mapper;  9 import org.apache.hadoop.mapreduce.Reducer; 10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 11 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 12 import org.apache.hadoop.util.GenericOptionsParser; 13 14 import yjmyzz.util.HDFSUtil; 15 16 import java.io.IOException; 17 18 19 public class RemoveDup { 20 21 public static class RemoveDupMapper 22 extends Mapper<Object, Text, Text, NullWritable> { 23 24 public void map(Object key, Text value, Context context) 25 throws IOException, InterruptedException { 26  context.write(value, NullWritable.get()); 27 //System.out.println("map: key=" + key + ",value=" + value); 28  } 29 30  } 31 32 public static class RemoveDupReducer extends Reducer<Text, NullWritable, Text, NullWritable> { 33 public void reduce(Text key, Iterable<NullWritable> values, Context context) 34 throws IOException, InterruptedException { 35  context.write(key, NullWritable.get()); 36 //System.out.println("reduce: key=" + key); 37  } 38  } 39 40 public static void main(String[] args) throws Exception { 41 Configuration conf = new Configuration(); 42 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 43 if (otherArgs.length < 2) { 44 System.err.println("Usage: RemoveDup <in> [<in>...] <out>"); 45 System.exit(2); 46  } 47 48 //删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在) 49 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 50 51 Job job = Job.getInstance(conf, "RemoveDup"); 52 job.setJarByClass(RemoveDup.class); 53 job.setMapperClass(RemoveDupMapper.class); 54 job.setCombinerClass(RemoveDupReducer.class); 55 job.setReducerClass(RemoveDupReducer.class); 56 job.setOutputKeyClass(Text.class); 57 job.setOutputValueClass(NullWritable.class); 58 59 60 for (int i = 0; i < otherArgs.length - 1; ++i) { 61 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 62  } 63  FileOutputFormat.setOutputPath(job, 64 new Path(otherArgs[otherArgs.length - 1])); 65 System.exit(job.waitForCompletion(true) ? 0 : 1); 66  } 67 68 69 }

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输出:

0 2 3 5 7 8

3) 记录计数(Count)

这个跟WordCount略有不同,类似于Select Count(*) from tables的效果,代码也超级简单,直接拿WordCount改一改就行了

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.IntWritable;  6 import org.apache.hadoop.io.Text;  7 import org.apache.hadoop.mapreduce.Job;  8 import org.apache.hadoop.mapreduce.Mapper;  9 import org.apache.hadoop.mapreduce.Reducer; 10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 11 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 12 import org.apache.hadoop.util.GenericOptionsParser; 13 import yjmyzz.util.HDFSUtil; 14 15 import java.io.IOException; 16 import java.util.StringTokenizer; 17 18 19 public class RowCount { 20 21 public static class RowCountMapper 22 extends Mapper<Object, Text, Text, IntWritable> { 23 24 private final static IntWritable one = new IntWritable(1); 25 private final static Text countKey = new Text("count"); 26 27 public void map(Object key, Text value, Context context) throws IOException, InterruptedException { 28  context.write(countKey, one); 29  } 30  } 31 32 public static class RowCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> { 33 private IntWritable result = new IntWritable(); 34 35 public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { 36 int sum = 0; 37 for (IntWritable val : values) { 38 sum += val.get(); 39  } 40  result.set(sum); 41  context.write(key, result); 42  } 43  } 44 45 public static void main(String[] args) throws Exception { 46 Configuration conf = new Configuration(); 47 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 48 if (otherArgs.length < 2) { 49 System.err.println("Usage: RowCount <in> [<in>...] <out>"); 50 System.exit(2); 51  } 52 //删除输出目录(可选) 53 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 54 55 Job job = Job.getInstance(conf, "word count"); 56 job.setJarByClass(RowCount.class); 57 job.setMapperClass(RowCountMapper.class); 58 job.setCombinerClass(RowCountReducer.class); 59 job.setReducerClass(RowCountReducer.class); 60 job.setOutputKeyClass(Text.class); 61 job.setOutputValueClass(IntWritable.class); 62 for (int i = 0; i < otherArgs.length - 1; ++i) { 63 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 64  } 65  FileOutputFormat.setOutputPath(job, 66 new Path(otherArgs[otherArgs.length - 1])); 67 System.exit(job.waitForCompletion(true) ? 0 : 1); 68  } 69 70 71 }

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输出: count 11

注:如果只想输出一个数字,不需要"count"这个key,可以改进一下:

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.LongWritable;  6 import org.apache.hadoop.io.NullWritable;  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 org.apache.hadoop.util.GenericOptionsParser; 14 import yjmyzz.util.HDFSUtil; 15 16 import java.io.IOException; 17 18 19 public class RowCount2 { 20 21 public static class RowCount2Mapper 22 extends Mapper<LongWritable, Text, LongWritable, NullWritable> { 23 24 public long count = 0; 25 26 public void map(LongWritable key, Text value, Context context) 27 throws IOException, InterruptedException { 28 count += 1; 29  } 30 31 protected void cleanup(Context context) throws IOException, InterruptedException { 32 context.write(new LongWritable(count), NullWritable.get()); 33  } 34 35  } 36 37 public static class RowCount2Reducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable> { 38 39 public long count = 0; 40 41 public void reduce(LongWritable key, Iterable<NullWritable> values, Context context) 42 throws IOException, InterruptedException { 43 count += key.get(); 44  } 45 46 47 protected void cleanup(Context context) throws IOException, InterruptedException { 48 context.write(new LongWritable(count), NullWritable.get()); 49  } 50 51  } 52 53 public static void main(String[] args) throws Exception { 54 Configuration conf = new Configuration(); 55 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 56 if (otherArgs.length < 2) { 57 System.err.println("Usage: FindMax <in> [<in>...] <out>"); 58 System.exit(2); 59  } 60 61 //删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在) 62 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 63 64 Job job = Job.getInstance(conf, "RowCount2"); 65 job.setJarByClass(RowCount2.class); 66 job.setMapperClass(RowCount2Mapper.class); 67 job.setCombinerClass(RowCount2Reducer.class); 68 job.setReducerClass(RowCount2Reducer.class); 69 job.setOutputKeyClass(LongWritable.class); 70 job.setOutputValueClass(NullWritable.class); 71 72 for (int i = 0; i < otherArgs.length - 1; ++i) { 73 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 74  } 75  FileOutputFormat.setOutputPath(job, 76 new Path(otherArgs[otherArgs.length - 1])); 77 System.exit(job.waitForCompletion(true) ? 0 : 1); 78  } 79 80 81 }

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这样输出结果就只有一个数字11了.

注意: 这里context.write(xxx)只能写在cleanup方法中, 该方法在Mapper和Reducer接口中都有, 在map方法及reduce方法执行完后,会触发cleanup方法. 大家可以尝试下,把context.write(xxx)写在map和reduce方法中试试看,结果会出现多行记录,而不是预期的仅1个数字.

4)求最大值(Max)

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.LongWritable;  6 import org.apache.hadoop.io.NullWritable;  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 org.apache.hadoop.util.GenericOptionsParser; 14 import yjmyzz.util.HDFSUtil; 15 16 import java.io.IOException; 17 18 19 public class Max { 20 21 public static class MaxMapper 22 extends Mapper<LongWritable, Text, LongWritable, NullWritable> { 23 24 public long max = Long.MIN_VALUE; 25 26 public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 27 max = Math.max(Long.parseLong(value.toString()), max); 28  } 29 30 protected void cleanup(Mapper.Context context) throws IOException, InterruptedException { 31 context.write(new LongWritable(max), NullWritable.get()); 32  } 33 34  } 35 36 public static class MaxReducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable> { 37 38 public long max = Long.MIN_VALUE; 39 40 public void reduce(LongWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException { 41 42 max = Math.max(max, key.get()); 43 44  } 45 46 47 protected void cleanup(Reducer.Context context) throws IOException, InterruptedException { 48 context.write(new LongWritable(max), NullWritable.get()); 49  } 50 51  } 52 53 public static void main(String[] args) throws Exception { 54 Configuration conf = new Configuration(); 55 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 56 if (otherArgs.length < 2) { 57 System.err.println("Usage: Max <in> [<in>...] <out>"); 58 System.exit(2); 59  } 60 61 //删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在) 62 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 63 64 Job job = Job.getInstance(conf, "Max"); 65 job.setJarByClass(Max.class); 66 job.setMapperClass(MaxMapper.class); 67 job.setCombinerClass(MaxReducer.class); 68 job.setReducerClass(MaxReducer.class); 69 job.setOutputKeyClass(LongWritable.class); 70 job.setOutputValueClass(NullWritable.class); 71 72 for (int i = 0; i < otherArgs.length - 1; ++i) { 73 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 74  } 75  FileOutputFormat.setOutputPath(job, 76 new Path(otherArgs[otherArgs.length - 1])); 77 System.exit(job.waitForCompletion(true) ? 0 : 1); 78  } 79 80 81 }

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输出结果:8

如果看懂了刚才的Count2版本的代码,这个自然不用多解释.

5)求和(Sum)

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.LongWritable;  6 import org.apache.hadoop.io.NullWritable;  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 org.apache.hadoop.util.GenericOptionsParser; 14 import yjmyzz.util.HDFSUtil; 15 16 import java.io.IOException; 17 18 19 public class Sum { 20 21 public static class SumMapper 22 extends Mapper<LongWritable, Text, LongWritable, NullWritable> { 23 24 public long sum = 0; 25 26 public void map(LongWritable key, Text value, Context context) 27 throws IOException, InterruptedException { 28 sum += Long.parseLong(value.toString()); 29  } 30 31 protected void cleanup(Context context) throws IOException, InterruptedException { 32 context.write(new LongWritable(sum), NullWritable.get()); 33  } 34 35  } 36 37 public static class SumReducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable> { 38 39 public long sum = 0; 40 41 public void reduce(LongWritable key, Iterable<NullWritable> values, Context context) 42 throws IOException, InterruptedException { 43 sum += key.get(); 44  } 45 46 47 protected void cleanup(Context context) throws IOException, InterruptedException { 48 context.write(new LongWritable(sum), NullWritable.get()); 49  } 50 51  } 52 53 public static void main(String[] args) throws Exception { 54 Configuration conf = new Configuration(); 55 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 56 if (otherArgs.length < 2) { 57 System.err.println("Usage: Sum <in> [<in>...] <out>"); 58 System.exit(2); 59  } 60 61 //删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在) 62 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 63 64 Job job = Job.getInstance(conf, "Sum"); 65 job.setJarByClass(Sum.class); 66 job.setMapperClass(SumMapper.class); 67 job.setCombinerClass(SumReducer.class); 68 job.setReducerClass(SumReducer.class); 69 job.setOutputKeyClass(LongWritable.class); 70 job.setOutputValueClass(NullWritable.class); 71 72 for (int i = 0; i < otherArgs.length - 1; ++i) { 73 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 74  } 75  FileOutputFormat.setOutputPath(job, 76 new Path(otherArgs[otherArgs.length - 1])); 77 System.exit(job.waitForCompletion(true) ? 0 : 1); 78  } 79 80 81 }

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输出结果:43

Sum与刚才的Max原理如出一辙,不多解释了,依旧利用了cleanup方法

6)求平均值(Avg)

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.*;  6 import org.apache.hadoop.mapreduce.Job;  7 import org.apache.hadoop.mapreduce.Mapper;  8 import org.apache.hadoop.mapreduce.Reducer;  9 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  10 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;  11 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  12 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;  13 import org.apache.hadoop.util.GenericOptionsParser;  14 import yjmyzz.util.HDFSUtil;  15  16 import java.io.IOException;  17  18  19 public class Average {  20  21 public static class AvgMapper  22 extends Mapper<LongWritable, Text, LongWritable, LongWritable> {  23  24 public long sum = 0;  25 public long count = 0;  26  27 public void map(LongWritable key, Text value, Context context)  28 throws IOException, InterruptedException {  29 sum += Long.parseLong(value.toString());  30 count += 1;  31  }  32  33 protected void cleanup(Context context) throws IOException, InterruptedException {  34 context.write(new LongWritable(sum), new LongWritable(count));  35  }  36  37  }  38  39 public static class AvgCombiner extends Reducer<LongWritable, LongWritable, LongWritable, LongWritable> {  40  41 public long sum = 0;  42 public long count = 0;  43  44 public void reduce(LongWritable key, Iterable<LongWritable> values, Context context)  45 throws IOException, InterruptedException {  46 sum += key.get();  47 for (LongWritable v : values) {  48 count += v.get();  49  }  50  }  51  52 protected void cleanup(Context context) throws IOException, InterruptedException {  53 context.write(new LongWritable(sum), new LongWritable(count));  54  }  55  56  }  57  58 public static class AvgReducer extends Reducer<LongWritable, LongWritable, DoubleWritable, NullWritable> {  59  60 public long sum = 0;  61 public long count = 0;  62  63 public void reduce(LongWritable key, Iterable<LongWritable> values, Context context)  64 throws IOException, InterruptedException {  65 sum += key.get();  66 for (LongWritable v : values) {  67 count += v.get();  68  }  69  }  70  71  72 protected void cleanup(Context context) throws IOException, InterruptedException {  73 context.write(new DoubleWritable(new Double(sum)/count), NullWritable.get());  74  }  75  76  }  77  78 public static void main(String[] args) throws Exception {  79 Configuration conf = new Configuration();  80 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();  81 if (otherArgs.length < 2) {  82 System.err.println("Usage: Avg <in> [<in>...] <out>");  83 System.exit(2);  84  }  85  86 //删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在)  87 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]);  88  89 Job job = Job.getInstance(conf, "Avg");  90 job.setJarByClass(Average.class);  91 job.setMapperClass(AvgMapper.class);  92 job.setCombinerClass(AvgCombiner.class);  93 job.setReducerClass(AvgReducer.class);  94  95 //注意这里:由于Mapper与Reducer的输出Key,Value类型不同,所以要单独为Mapper设置类型  96 job.setMapOutputKeyClass(LongWritable.class);  97 job.setMapOutputValueClass(LongWritable.class);  98  99 100 job.setOutputKeyClass(DoubleWritable.class); 101 job.setOutputValueClass(NullWritable.class); 102 103 for (int i = 0; i < otherArgs.length - 1; ++i) { 104 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 105  } 106  FileOutputFormat.setOutputPath(job, 107 new Path(otherArgs[otherArgs.length - 1])); 108 System.exit(job.waitForCompletion(true) ? 0 : 1); 109  } 110 111 112 }

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输出:3.909090909090909

这个稍微要复杂一点,平均值大家都知道=Sum/Count,所以这其实前面Count与Max的综合运用而已,思路是在输出的key-value中,用max做key,用count做value,最终形成{sum,count}的输出,然后在最后的cleanup中,sum/count即得avg,但是有一个特点要注意的地方,由于Mapper与Reducer的output {key,value}类型并不一致,所以96-101行这里,分别设置了Map及Reduce的key,value输出类型,如果没有96-97这二行,100-101这二行会默认把Mapper,Combiner,Reducer这三者的输出类型设置成相同的类型.

7) 改进型的WordCount(按词频倒排)

官网示例WordCount只统计出单词出现的次数,并未按词频做倒排,下面的代码示例实现了该功能

 1 package yjmyzz.mr;  2  3 import org.apache.hadoop.conf.Configuration;  4 import org.apache.hadoop.fs.Path;  5 import org.apache.hadoop.io.IntWritable;  6 import org.apache.hadoop.io.LongWritable;  7 import org.apache.hadoop.io.NullWritable;  8 import org.apache.hadoop.io.Text;  9 import org.apache.hadoop.mapreduce.Job; 10 import org.apache.hadoop.mapreduce.Mapper; 11 import org.apache.hadoop.mapreduce.Reducer; 12 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 13 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 14 import org.apache.hadoop.util.GenericOptionsParser; 15 import yjmyzz.util.HDFSUtil; 16 17 import java.io.IOException; 18 import java.util.Comparator; 19 import java.util.StringTokenizer; 20 import java.util.TreeMap; 21 22 23 public class WordCount2 { 24 25 public static class TokenizerMapper 26 extends Mapper<Object, Text, Text, IntWritable> { 27 28 private final static IntWritable one = new IntWritable(1); 29 private Text word = new Text(); 30 31 public void map(Object key, Text value, Context context) throws IOException, InterruptedException { 32 StringTokenizer itr = new StringTokenizer(value.toString()); 33 while (itr.hasMoreTokens()) { 34  word.set(itr.nextToken()); 35  context.write(word, one); 36  } 37  } 38  } 39 40 public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { 41 42 //定义treeMap来保持统计结果,由于treeMap是按key升序排列的,这里要人为指定Comparator以实现倒排 43 private TreeMap<Integer, String> treeMap = new TreeMap<Integer, String>(new Comparator<Integer>() { 44  @Override 45 public int compare(Integer x, Integer y) { 46 return y.compareTo(x); 47  } 48  }); 49 50 public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { 51 //reduce后的结果放入treeMap,而不是向context中记入结果 52 int sum = 0; 53 for (IntWritable val : values) { 54 sum += val.get(); 55  } 56 if (treeMap.containsKey(sum)){ 57 String value = treeMap.get(sum) + "," + key.toString(); 58  treeMap.put(sum,value); 59  } 60 else { 61  treeMap.put(sum, key.toString()); 62  } 63  } 64 65 protected void cleanup(Context context) throws IOException, InterruptedException { 66 //将treeMap中的结果,按value-key顺序写入contex中 67 for (Integer key : treeMap.keySet()) { 68 context.write(new Text(treeMap.get(key)), new IntWritable(key)); 69  } 70  } 71  } 72 73 public static void main(String[] args) throws Exception { 74 Configuration conf = new Configuration(); 75 String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 76 if (otherArgs.length < 2) { 77 System.err.println("Usage: wordcount2 <in> [<in>...] <out>"); 78 System.exit(2); 79  } 80 //删除输出目录 81 HDFSUtil.deleteFile(conf, otherArgs[otherArgs.length - 1]); 82 Job job = Job.getInstance(conf, "word count2"); 83 job.setJarByClass(WordCount2.class); 84 job.setMapperClass(TokenizerMapper.class); 85 job.setCombinerClass(IntSumReducer.class); 86 job.setReducerClass(IntSumReducer.class); 87 job.setOutputKeyClass(Text.class); 88 job.setOutputValueClass(IntWritable.class); 89 for (int i = 0; i < otherArgs.length - 1; ++i) { 90 FileInputFormat.addInputPath(job, new Path(otherArgs[i])); 91  } 92  FileOutputFormat.setOutputPath(job, 93 new Path(otherArgs[otherArgs.length - 1])); 94 System.exit(job.waitForCompletion(true) ? 0 : 1); 95  } 96 97 98 }

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原理: 依然用到了cleanup,此外为了实现排序,采用了TreeMap这种内置了key排序的数据结构.

这里为了展示更直观,选用了电影<超能陆战队>主题曲的第一段歌词做为输入:

They say we are what we are But we do not have to be I am bad behavior but I do it in the best way I will be the watcher Of the eternal flame I will be the guard dog of all your fever dreams

原版的WordCount处理完后,结果如下:

But 1 I 4 Of 1 They 1 all 1 am 1 are 2 bad 1 be 3 behavior 1 best 1 but 1 do 2 dog 1 dreams 1 eternal 1 fever 1 flame 1 guard 1 have 1 in 1 it 1 not 1 of 1 say 1 the 4 to 1 watcher 1 way 1 we 3 what 1 will 2 your 1

改进后的WordCount2处理结果如下:

I,the 4 be,we 3 are,do,will 2 But,Of,They,all,am,bad,behavior,best,but,dog,dreams,eternal,fever,flame,guard,have,in,it,not,of,say,to,watcher,way,what,your 1

原文链接：https://yq.aliyun.com/articles/250806

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