Hadoop MapReduce2 几个常用的功能代码示例
1) WordCount
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 }
View Code
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 }
View Code
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 }
View Code
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 }
View Code
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 }
View Code
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 }
View Code
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 }
View Code
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
作者：菩提树下的杨过
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