MapReduce初级编程实践(mapreduce初级编程实践实验心得)
目的
1.通过实验掌握基本的MapReduce编程方法;
2.掌握用MapReduce解决一些常见的数据处理问题。
平台
已经配置完成的Hadoop伪分布式环境。
实验内容和要求
假设HDFS中/user/hadoop/input文件夹下有文件wordfile1.txt和wordfile2.txt。现在需要设计一个词频统计程序,统计input文件夹下所有文件中每个单词的出现次数。
1、使用Eclipse编译运行MapReduce程序(运行过程结果截图)
参考链接:dblab.xmu.edu.cn/blog/hadoop…
2、使用命令行编译打包运行自己的MapReduce程序(运行过程结果截图)
参考链接:dblab.xmu.edu.cn/blog/hadoop…
实验步骤
第一,首先创建一个MapReduce项目
通过以下命令
cp /usr/local/hadoop/etc/hadoop/core-site.xml ~/workspace/WordCount/src
cp /usr/local/hadoop/etc/hadoop/hdfs-site.xml ~/workspace/WordCount/src
cp /usr/local/hadoop/etc/hadoop/log4j.properties ~/workspace/WordCount/src
编写java代码
package org.apache.hadoop.examples; import java.io.IOException; import java.util.Iterator; 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; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { public WordCount() { } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs(); if (otherArgs.length < 2) { System.err.println("Usage: wordcount <in> [<in>...] <out>"); System.exit(2); } Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(WordCount.TokenizerMapper.class); job.setCombinerClass(WordCount.IntSumReducer.class); job.setReducerClass(WordCount.IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); for (int i = 0; i < otherArgs.length - 1; ++i) { FileInputFormat.addInputPath(job, new Path(otherArgs[i])); } FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { private static final IntWritable one = new IntWritable(1); private Text word = new Text(); public TokenizerMapper() { } public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { this.word.set(itr.nextToken()); context.write(this.word, one); } } } public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public IntSumReducer() { } public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException { int sum = 0; IntWritable val; for (Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) { val = (IntWritable)i$.next(); } this.result.set(sum); context.write(key, this.result); } } } 复制代码
运行出现如下结果
第二,打包
右击项目->Export
第二,创建两个文件
上传到hadoop
第四
词频统计结果被写入到HDFS的/user/hadoop/output/目录中,运行结束后查看词频统计结果
./bin/hadoop jar ./myapp/WordCount.jar input output
./bin/hdfs dfs -cat output/*
四、实验中遇到的问题及解决方案
问题一
原因:output文件已存在
解决方案:删除output文件
问题二
Exception in thread "main" org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://localhost:9000/user/hadoop/input
原因:在路径hdfs://localhost:9000/user/hadoop/input下,找不到input文件
解决方案:需要将core-site.xml的如下内容配置正确
问题三
hadoop_eclipse-plugin-2.6.0.jar插件导入失败,导致进入eclipse没有DFS Location
并且也没有Hadoop Map/Reduce
原因:eclipse版本与插件版本不匹配
解决方案:之前用的eclipse是用安装包安装的,无法与插件适配。在虚拟机自带的应用商店下载了ecipse后成功适配
作者:Wave
链接:https://juejin.cn/post/7031526688370458637