需求

对以下txt文档进行单词出现次数统计（txt文档在/Users/lizhengi/test/input/目录下）

hadoop take spring
spark hadoop hdfs
mapreduce take Tomcat
tomcat
kafka kafka flume
flume
hive

实现

1、新建Maven工程，pom.xml依赖如下

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.lizhengi</groupId><artifactId>Hadoop-API</artifactId><version>1.0-SNAPSHOT</version><dependencies><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>RELEASE</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-core</artifactId><version>2.8.2</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>3.2.1</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>3.2.1</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-hdfs</artifactId><version>3.2.1</version></dependency></dependencies></project>

2、src/main/resources目录下，新建一个文件，命名为“log4j.properties”，添加内容如下

log4j.rootLogger=INFO, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

3、编写Mapper类-WcMapper

package com.lizhengi.wordcount;import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;/*** @author lizhengi* @create 2020-07-20*/
public class WcMapper extends Mapper<LongWritable, Text, Text, IntWritable> {Text k = new Text();IntWritable v = new IntWritable(1);@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {// 1 拿到传入进来的一行内容，把数据类型转化为StringString line = value.toString();// 2 将这一行内容按照分隔符进行一行内容的切割 切割成一个单词数组String[] words = line.split(" ");// 3 遍历数组，每出现一个单词  就标记一个数字1  <单词，1>for (String word : words) {//使用mr程序的上下文context 把mapper阶段处理的数据发送出去//作为reduce节点的输入数据k.set(word);context.write(k, v);}}
}

4、编写Reducer类-WcReducer

package com.lizhengi.wordcount;import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;/*** @author lizhengi* @create 2020-07-20*/
public class WcReducer extends Reducer<Text, IntWritable, Text, IntWritable>{int sum;IntWritable v = new IntWritable();@Overrideprotected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {// 1 定义一个计数器sum = 0;// 2 遍历一组迭代器，把每一个数量1累加起来就构成了单词的总次数for (IntWritable count : values) {sum += count.get();}// 3 输出最终的结果v.set(sum);context.write(key,v);}
}

5、编写Driver驱动类-WcDriver

package com.lizhengi.wordcount;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.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/*** @author lizhengi* @create 2020-07-20*/
public class WcDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {// 1 获取配置信息以及封装任务Configuration configuration = new Configuration();Job job = Job.getInstance(configuration);// 2 设置jar加载路径job.setJarByClass(WcDriver.class);// 3 设置map和reduce类job.setMapperClass(WcMapper.class);job.setReducerClass(WcReducer.class);// 4 设置map输出job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);// 5 设置最终输出kv类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);// 6 设置输入和输出路径FileInputFormat.setInputPaths(job, "/Users/lizhengi/test/input");FileOutputFormat.setOutputPath(job, new Path("/Users/lizhengi/test/output"));// 7 提交boolean result = job.waitForCompletion(true);System.exit(result ? 0 : 1);}
}

结果

[root@carlota1]ls /Users/lizhengi/test/output/
#多了两个文件
_SUCCESS	part-r-00000

[root@carlota1 output]cat part-r-00000
flume	2
hadoop	2
hdfs	1
hive	1
kafka	2
mapreduce	1
spark 	1
spring	1
take	2
tomcat		2