标签：tokenize package stand world clu 官网 运行 重复 demo
以前的公司和现在的公司，都用到了hadoop和hdfs。一直没入门，今天照着官网写了一个hadoop worldcount demo
1. hadoop是一个框架，什么是框架，spring是一个框架、mybatis是一个框架，框架是把系统中通用的功能写进去，减少开发工作量。比如基于spring boot开发一个web应用，直接写一个java类，加一些注解，打成jar包，java -jar demo.java即完成应用开发。
spring boot也是基于java serlet、tomcat、jetty等封装的一个框架，有了这个框架，我们就不用再写servlet实现类，配置web.xml等重复工作
2. hadoop需要的数据存放在hdfs里面，这里参照官网，在本机运行了一个伪分布式的hdfs
3. demo组成，写worldcount类，打成jar包，放到本机hadoop运行，从hdfs读文件内容，把结果写到hdfs中
4. 注意参考官网
pom.xml
<?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.gxf</groupId> <artifactId>hadoop_demo</artifactId> <version>1.0-SNAPSHOT</version> <dependencies> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <version>1.2.1</version> </dependency> </dependencies> </project>
WordCount.java这个直接从官网copy过来的
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.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.output.FileOutputFormat; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
这里没有加package，因为我搞不定，所以去掉了包名
接着就是打成jar包、准备文本文件放到hdfs、使用hadoop运行jar、查看结果。这些步骤在官网上有
标签：tokenize package stand world clu 官网 运行 重复 demo
原文地址：https://www.cnblogs.com/luckygxf/p/10054379.html
