一、概述
1.实验使用的Hadoop集群为伪分布式模式,eclipse相关配置已完成;
2.软件版本为hadoop-2.7.3.tar.gz、apache-maven-3.5.0.rar。
二、使用eclipse连接hadoop集群进行开发
1.在开发主机上配置hadoop
①将hadoop-2.7.3.tar.gz解压到本地主机上
②使用windows版本的hadoop中的bin替换目标中的bin文件夹
③配置windows上的hadoop环境变量
2.在eclipse上配置hadoop集群信息
①在eclipse中添加hadoop路径
②配置hadoop集群访问信息
3.在hadoop集群中取消权限验证
hdfs-site.xml <property> <name>dfs.permissions</name> <value>false</value> </property>
4.创建一个文件测试连接权限
5.安装maven
①将maven解压到开发主机上
②在eclipse上添加maven路径
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.新建一个类用于测试(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将log4j.properties移动到WordCount类下
②设置WordCount的运行自变量
8.运行测试
三、jar包的导出与提交执行
1.导出WordCount
2.将导出的jar包上传到hadoop集群
[hadoop@hadoop ~]$ ls wc.jar
3.运行
[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.查看运行结果
[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 ……
另外有需要云服务器可以了解下创新互联scvps.cn,海内外云服务器15元起步,三天无理由+7*72小时售后在线,公司持有idc许可证,提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案,具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势,专为企业上云打造定制,能够满足用户丰富、多元化的应用场景需求。
网站栏目:eclipse上搭建hadoop开发环境-创新互联
路径分享:https://www.cdcxhl.com/article26/dsjccg.html
成都网站建设公司_创新互联,为您提供网页设计公司、用户体验、面包屑导航、建站公司、网站建设、自适应网站
声明:本网站发布的内容(图片、视频和文字)以用户投稿、用户转载内容为主,如果涉及侵权请尽快告知,我们将会在第一时间删除。文章观点不代表本网站立场,如需处理请联系客服。电话:028-86922220;邮箱:631063699@qq.com。内容未经允许不得转载,或转载时需注明来源: 创新互联