Hadoop实战演练：搜索数据分析----TopK计算（2）

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工程源码下载：https://github.com/appleappleapple/BigDataLearning/tree/master/Hadoop-Demo

这里接上文Hadoop实战演练：搜索数据分析----数据去重 （1）

本文要根据上面算出来的结果我们要计算TopK搜索词。要得到热搜永词，首先得到每个词的搜索次数。

记得在上文的输出数据源如下格式，这里要当前本次运算的输入源

1、WordCount。首先计算每个关键词出现的次数

package com.lin.keyword;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.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.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;/** * 功能概要：统计搜索词 *  * @author linbingwen */public class KeyWordCount {public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {private final static IntWritable one = new IntWritable(1);        @Overridepublic void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {// 将输入的纯文本文件的数据转化成StringString line = value.toString();// 将输入的数据首先按行进行分割StringTokenizer tokenizerArticle = new StringTokenizer(line, "\n");// 分别对每一行进行处理while (tokenizerArticle.hasMoreElements()) {    // 每行按空格划分    StringTokenizer tokenizerLine = new StringTokenizer(tokenizerArticle.nextToken());    String c1 = tokenizerLine.nextToken();//    String c2 = tokenizerLine.nextToken();// 关键词    Text newline = new Text(c2);    context.write(newline, one);}}}public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {        private IntWritable result =new IntWritable();        // 实现reduce函数        @Overridepublic void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {int count = 0; for(IntWritable val:values){  count += val.get(); }        result.set(count);context.write(key, result);}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();//设置hadoop的机器、端口conf.set("mapred.job.tracker", "10.75.201.125:9000");//设置输入输出文件目录String[] ioArgs = new String[] { "hdfs://hmaster:9000/clean_same_out", "hdfs://hmaster:9000/Key_out" };String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();if (otherArgs.length != 2) {System.err.println("Usage:  <in> <out>");System.exit(2);}//设置一个jobJob job = Job.getInstance(conf, "key Word count");job.setJarByClass(KeyWordCount.class);// 设置Map、Combine和Reduce处理类job.setMapperClass(KeyWordCount.Map.class);job.setCombinerClass(KeyWordCount.Reduce.class);job.setReducerClass(KeyWordCount.Reduce.class);// 设置输出类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);// 将输入的数据集分割成小数据块splites，提供一个RecordReder的实现job.setInputFormatClass(TextInputFormat.class);// 提供一个RecordWriter的实现，负责数据输出job.setOutputFormatClass(TextOutputFormat.class);// 设置输入和输出目录FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);}}

输出数据格式如下：

2、TopK

在wordCount上面输出的数据基础上来做运算

package com.lin.keyword;import java.io.IOException;import java.util.TreeMap;import org.apache.hadoop.conf.Configuration;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.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;/** *  * 功能概要：统计top 热搜词 *  * @author linbingwen */public class TopK {public static final int K = 100;public static class KMap extends Mapper<LongWritable, Text, IntWritable, Text> {TreeMap<Integer, String> map = new TreeMap<Integer, String>();         @Overridepublic void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();if(line.trim().length() > 0 && line.indexOf("\t") != -1) {String[] arr = line.split("\t", 2);String name = arr[0];Integer num = Integer.parseInt(arr[1]);map.put(num, name);if(map.size() > K) {map.remove(map.firstKey());}}}@Overrideprotected void cleanup(Mapper<LongWritable, Text, IntWritable, Text>.Context context) throws IOException, InterruptedException {for(Integer num : map.keySet()) {context.write(new IntWritable(num), new Text(map.get(num)));}}}public static class KReduce extends Reducer<IntWritable, Text, IntWritable, Text> {TreeMap<Integer, String> map = new TreeMap<Integer, String>();        @Overridepublic void reduce(IntWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {map.put(key.get(), values.iterator().next().toString());if(map.size() > K) {map.remove(map.firstKey());}}@Overrideprotected void cleanup(Reducer<IntWritable, Text, IntWritable, Text>.Context context) throws IOException, InterruptedException {for(Integer num : map.keySet()) {context.write(new IntWritable(num), new Text(map.get(num)));}}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();//设置hadoop的机器、端口conf.set("mapred.job.tracker", "10.75.201.125:9000");//设置输入输出文件目录String[] ioArgs = new String[] { "hdfs://hmaster:9000/Key_out", "hdfs://hmaster:9000/top_out" };String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();if (otherArgs.length != 2) {System.err.println("Usage:  <in> <out>");System.exit(2);}//设置一个jobJob job = Job.getInstance(conf, "top K");        job.setJarByClass(TopK.class);// 设置Map、Combine和Reduce处理类job.setMapperClass(KMap.class);job.setCombinerClass(KReduce.class);job.setReducerClass(KReduce.class);// 设置输出类型job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(Text.class);// 将输入的数据集分割成小数据块splites，提供一个RecordReder的实现job.setInputFormatClass(TextInputFormat.class);// 提供一个RecordWriter的实现，负责数据输出job.setOutputFormatClass(TextOutputFormat.class);// 设置输入和输出目录FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);}}

输出结果：这是从小到大的热搜索词，这里只统计了前100个

结果正确输出没问题