一、概述
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1.實驗使用的Hadoop集群為偽分布式模式,eclipse相關(guān)配置已完成;
2.軟件版本為hadoop-2.7.3.tar.gz、apache-maven-3.5.0.rar。
二、使用eclipse連接hadoop集群進行開發(fā)
1.在開發(fā)主機上配置hadoop
①將hadoop-2.7.3.tar.gz解壓到本地主機上
②使用windows版本的hadoop中的bin替換目標中的bin文件夾
③配置windows上的hadoop環(huán)境變量
2.在eclipse上配置hadoop集群信息
①在eclipse中添加hadoop路徑
②配置hadoop集群訪問信息
3.在hadoop集群中取消權(quán)限驗證
hdfs-site.xml <property> <name>dfs.permissions</name> <value>false</value> </property>
4.創(chuàng)建一個文件測試連接權(quán)限
5.安裝maven
①將maven解壓到開發(fā)主機上
②在eclipse上添加maven路徑
5.新建maven工程
6.修改maven配置文件(maven/pom.xml)
<dependencies> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>3.8.1</version> <scope>test</scope> </dependency> </dependencies>
7.新建一個類用于測試(WordCount)
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; import org.apache.hadoop.util.GenericOptionsParser; 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(); 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(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(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); } }
8.配置WordCount
①將log4j.properties移動到WordCount類下
②設(shè)置WordCount的運行自變量
8.運行測試
三、jar包的導出與提交執(zhí)行
1.導出WordCount
2.將導出的jar包上傳到hadoop集群
[hadoop@hadoop ~]$ ls wc.jar
3.運行
[hadoop@hadoop ~]$ hadoop jar wc.jar WordCount /user/hadoop/input/* /user/hadoop/output/out 17/09/06 22:36:56 INFO client.RMProxy: Connecting to ResourceManager at hadoop/192.168.100.141:8032 17/09/06 22:36:57 INFO input.FileInputFormat: Total input paths to process : 1 17/09/06 22:36:58 INFO mapreduce.JobSubmitter: number of splits:1 17/09/06 22:36:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1504744740212_0001 17/09/06 22:36:59 INFO impl.YarnClientImpl: Submitted application application_1504744740212_0001 17/09/06 22:36:59 INFO mapreduce.Job: The url to track the job: http://hadoop:8088/proxy/application_1504744740212_0001/ 17/09/06 22:36:59 INFO mapreduce.Job: Running job: job_1504744740212_0001 17/09/06 22:37:36 INFO mapreduce.Job: Job job_1504744740212_0001 running in uber mode : false 17/09/06 22:37:36 INFO mapreduce.Job: map 0% reduce 0% 17/09/06 22:38:26 INFO mapreduce.Job: map 100% reduce 0% 17/09/06 22:38:42 INFO mapreduce.Job: map 100% reduce 100% 17/09/06 22:38:46 INFO mapreduce.Job: Job job_1504744740212_0001 completed successfully
4.查看運行結(jié)果
[hadoop@hadoop ~]$ hdfs dfs -cat /user/hadoop/output/out/part-r-00000 "AS 1 "GCC 1 "License"); 1 & 1 'Aalto 1 'Apache 4 'ArrayDeque', 1 'Bouncy 1 'Caliper', 1 'Compress-LZF', 1 ……
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