一、簡介

在文件中統計出現最多個數的單詞，將其輸出到hdfs文件上。

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二、例子

（1）實例描述
給出三個文件，每個文件中都若干個單詞以空白符分隔，需要統計出現最多的單詞? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??

樣例輸入： ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
1）file1： ?

MapReduce is simple

2）file2： ?

MapReduce is powerful is simple?

3）file3： ?

Hello MapReduce bye MapReduce

期望輸出：

MapReduce??????4

（2）問題分析
實現"統計出現最多個數的單詞"只要關注的信息為：單詞、詞頻。

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（3）實現步驟

1）Map過程?

首先使用默認的TextInputFormat類對輸入文件進行處理，得到文本中每行的偏移量及其內容。顯然，Map過程首先必須分析輸入的<key,value>對，得到倒排索引中需要的三個信息：單詞、詞頻

2）Combine過程?
????經過map方法處理后，Combine過程將key值相同的value值累加，得到一個單詞在文檔在文檔中的詞頻，輸出作為Reduce過程的輸入。

3）Reduce過程?
經過上述兩個過程后，Reduce過程只需將相同key值的value值累加，保留最大詞頻的單詞輸出。

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（4）代碼實現

package com.mk.mapreduce;import org.apache.commons.lang.StringUtils; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; 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.input.FileSplit; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException; import java.net.URI; import java.util.*;public class MaxWord {public static class MaxWordMapper extends Mapper<LongWritable, Text, Text, IntWritable> {private final Text newKey = new Text();private final IntWritable newValue = new IntWritable(1);@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {if (StringUtils.isBlank(value.toString())) {System.out.println("空白行");return;}StringTokenizer tokenizer = new StringTokenizer(value.toString());while (tokenizer.hasMoreTokens()) {String word = tokenizer.nextToken();newKey.set(word);context.write(newKey, newValue);}}}public static class MaxWordCombiner extends Reducer<Text, IntWritable, Text, IntWritable> {private final IntWritable newValue = new IntWritable();@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {int count = 0;for (IntWritable v : values) {count += v.get();}newValue.set(count);context.write(key, newValue);}}public static class MaxWordReducer extends Reducer<Text, IntWritable, Text, IntWritable> {private String word = null;private int count = 0;@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {int c = 0;for (IntWritable v : values) {c += v.get();}if (word == null || count < c) {word = key.toString();count = c;}}@Overrideprotected void cleanup(Context context) throws IOException, InterruptedException {if (word != null) {context.write(new Text(word), new IntWritable(count));}}}public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {String uri = "hdfs://192.168.150.128:9000";String input = "/maxWord/input";String output = "/maxWord/output";Configuration conf = new Configuration();if (System.getProperty("os.name").toLowerCase().contains("win"))conf.set("mapreduce.app-submission.cross-platform", "true");FileSystem fileSystem = FileSystem.get(URI.create(uri), conf);Path path = new Path(output);fileSystem.delete(path, true);Job job = new Job(conf, "MaxWord");job.setJar("./out/artifacts/hadoop_test_jar/hadoop-test.jar");job.setJarByClass(MaxWord.class);job.setMapperClass(MaxWordMapper.class);job.setCombinerClass(MaxWordCombiner.class);job.setReducerClass(MaxWordReducer.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPaths(job, uri + input);FileOutputFormat.setOutputPath(job, new Path(uri + output));boolean ret = job.waitForCompletion(true);System.out.println(job.getJobName() + "-----" + ret);} }

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