配置hadoop-1.2.1 eclipse开发环境
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转自:http://blog.csdn.net/poisonchry/article/details/27535333
配置hadoop-eclipse开发环境
由于hadoop-eclipse-1.2.1插件需要自行编译,所以为了图省事而从网上直接下载了这个jar包,所以如果有需要可以从点击并下载资源。下载这个jar包后,将它放置在eclipse/plugins目录下,并重启eclipse即可。
如果你需要自己编译该插件,请参考文献。
配置hadoop-1.2.1与eclipse链接信息
如果没有意外,在你的eclipse的右上角应该出现了一只蓝色的大象logo,请点击那只大象。之后,在正下方的区域将会多出一项Map/Reduce Locations的选项卡,点击该选项卡,并右键新建New Hadoop Location。
这时应该会弹出一个对话框,需要你填写这些内容:
- Location name
- Map/Reduce Master
- DFS Master
- User name
Location name 指的是当前创建的链接名字,可以任意指定;Map/Reduce Master 指的是执行MR的主机地址,并且需要给定hdfs协议的通讯地址; DFS Master 指的是Distribution File System的主机地址,并且需要给定hdfs协议的通讯地址; User name 指定的是链接至Hadoop的用户名。
参考上一篇文章的设计,hadoop-1.2.1集群搭建,这里的配置信息将沿用上一篇文章的设定。
因此,我们的设置情况如下
参数名配置参数说明Location namehadoop MapReduce MasterHost: 192.168.145.100NameNode 的IP地址MapReduce MasterPort: 8021MapReduce Port,参考自己配置的mapred-site.xmlDFS MasterPort: 8020DFS Port,参考自己配置的core-site.xmlUser namehadoop之后,切换到Advanced parameters,而你需要修改的有如下参数
参数名配置参数说明fs.default.namehdfs://192.168.145.100:8020参考core-site.xmlhadoop.tmp.dir/home/hadoop/hadoopdata/tmp参考core-site.xmlmapred.job.trackerhdfs://192.168.145.100:8021参考mapred-site.xml之后确认,这样便在eclipse左边出现了HDFS的文件结构。但是现在你只能查看,而不能添加修改文件。因此你还需要手工登录到HDFS上,并使用命令修改权限。
./bin/hadoop fs -chmod -R 777 /
在完成这些步骤后,需要配置最后的开发环境了。
配置开发环境
如果是在Windows上模拟远程开发,那么你需要将hadoop-1.2.1.tar.gz解压一份,我们将解压后得到的hadoop-1.2.1放置在documents里
C:\Users\ISCAS\Documents\src\hadoop-1.2.1
之后,打开 eclipse -> Preferences -> Hadoop Map/Reduce,将解压后的路径添加在 hadoop installation directory 中,并点击apply使设置生效。
这个时候,我们可以试着编译一两个Hadoop程序, File -> Map/Reduce -> Map/Reduce Project 或者直接通过 Project Wizzard 新建一个Hadoop项目,并命名该项目为 Hadoop Test。
我们的第一个程序是 wordcount, 源代码可以从 ..\hadoop-1.2.1\src\examples\org\apache\hadoop\examples 中获得。
/** * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.hadoop.examples;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> <out>"); System.exit(2); } Job job = new Job(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(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); }}
这里面,为了方便,我们直接贴出该代码。准备好后,就可以直接点击 Run 命令,对代码进行编译。不过在编译前,会弹出一个小窗口,选择 Run on Hadoop,并确认。
等待一段时间,编译后并执行后,你会发现出现一段提示:
Usage: wordcount <in> <out>
WordCount例程,需要输入文件,并且需要指定输出的文件存放目录。因此,我们还需要为程序设定参数。方法是,在Run命令下,选择Run Configurations。
在 Arguments 选项卡中,Program arguments一栏里,指定输入和输出的参数。
我们给定的需要进行统计的文本存放在 /Data/words。
Mary had a little lambits fleece very white as snowand everywhere that Mary wentthe lamb was sure to go
所以设定的参数为:
hdfs://192.168.145.100:8020/Data/words hdfs://192.168.145.100:8020/out
配置好参数,并运行,如果你使用的是Windows版本的eclipse,会报出这个错误:
14/05/29 13:49:16 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable14/05/29 13:49:16 ERROR security.UserGroupInformation: PriviledgedActionException as:ISCAS cause:java.io.IOException: Failed to set permissions of path: \tmp\hadoop-ISCAS\mapred\staging\ISCAS1655603947\.staging to 0700Exception in thread "main" java.io.IOException: Failed to set permissions of path: \tmp\hadoop-ISCAS\mapred\staging\ISCAS1655603947\.staging to 0700 at org.apache.hadoop.fs.FileUtil.checkReturnValue(FileUtil.java:691) at org.apache.hadoop.fs.FileUtil.setPermission(FileUtil.java:664) at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:514) at org.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:349) at org.apache.hadoop.fs.FilterFileSystem.mkdirs(FilterFileSystem.java:193) at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:126) at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:942) at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:936) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Unknown Source) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1190) at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:936) at org.apache.hadoop.mapreduce.Job.submit(Job.java:550) at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:580) at org.apache.hadoop.examples.WordCount.main(WordCount.java:82)
这个错误在 Linux 系统中是不存在的,因此我们需要对 hadoop 的代码做一些小修改。
修改 Hadoop 源码
导致这一问题的是Windows文件权限问题,不过这一问题在Linux系统下是不存在的,因此如果你需要在Windows下进行编程,那么建议你按照我们提供的方法对hadoop的源码进行修改。
出现问题的文件,位于 hadoop-1.2.1\src\core\org\apache\hadoop\fs\ 下的FileUtil.java。
修改方法是将
private static void checkReturnValue(boolean rv, File p, FsPermission permission) throws IOException{ /** * comment the following, disable this function if (!rv) { throw new IOException("Failed to set permissions of path: " + p + " to " + String.format("%04o", permission.toShort())); } */ }
然后将修改好的文件重新编译,并将.class文件打包到hadoop-core-1.2.1.jar中,并重新刷新工程。这里,我们提供了已经修改后的jar文件包,如果需要可以点击下载,并替换掉原有的hadoop-1.2.1中的jar包。
运行Hadoop源码
再次运行WordCount例程,Hadoop便会正常启动了。
14/05/29 15:13:59 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable14/05/29 15:13:59 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).14/05/29 15:13:59 INFO input.FileInputFormat: Total input paths to process : 114/05/29 15:13:59 WARN snappy.LoadSnappy: Snappy native library not loaded14/05/29 15:13:59 INFO mapred.JobClient: Running job: job_local889277352_000114/05/29 15:13:59 INFO mapred.LocalJobRunner: Waiting for map tasks14/05/29 15:13:59 INFO mapred.LocalJobRunner: Starting task: attempt_local889277352_0001_m_000000_014/05/29 15:13:59 INFO mapred.Task: Using ResourceCalculatorPlugin : null14/05/29 15:13:59 INFO mapred.MapTask: Processing split: hdfs://192.168.145.100:8020/Data/words:0+10914/05/29 15:13:59 INFO mapred.MapTask: io.sort.mb = 10014/05/29 15:13:59 INFO mapred.MapTask: data buffer = 79691776/9961472014/05/29 15:13:59 INFO mapred.MapTask: record buffer = 262144/32768014/05/29 15:13:59 INFO mapred.MapTask: Starting flush of map output14/05/29 15:13:59 INFO mapred.MapTask: Finished spill 014/05/29 15:13:59 INFO mapred.Task: Task:attempt_local889277352_0001_m_000000_0 is done. And is in the process of commiting14/05/29 15:13:59 INFO mapred.LocalJobRunner: 14/05/29 15:13:59 INFO mapred.Task: Task 'attempt_local889277352_0001_m_000000_0' done.14/05/29 15:13:59 INFO mapred.LocalJobRunner: Finishing task: attempt_local889277352_0001_m_000000_014/05/29 15:13:59 INFO mapred.LocalJobRunner: Map task executor complete.14/05/29 15:13:59 INFO mapred.Task: Using ResourceCalculatorPlugin : null14/05/29 15:13:59 INFO mapred.LocalJobRunner: 14/05/29 15:13:59 INFO mapred.Merger: Merging 1 sorted segments14/05/29 15:13:59 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 219 bytes14/05/29 15:13:59 INFO mapred.LocalJobRunner: 14/05/29 15:14:00 INFO mapred.Task: Task:attempt_local889277352_0001_r_000000_0 is done. And is in the process of commiting14/05/29 15:14:00 INFO mapred.LocalJobRunner: 14/05/29 15:14:00 INFO mapred.Task: Task attempt_local889277352_0001_r_000000_0 is allowed to commit now14/05/29 15:14:00 INFO output.FileOutputCommitter: Saved output of task 'attempt_local889277352_0001_r_000000_0' to hdfs://192.168.145.100:8020/out14/05/29 15:14:00 INFO mapred.LocalJobRunner: reduce > reduce14/05/29 15:14:00 INFO mapred.Task: Task 'attempt_local889277352_0001_r_000000_0' done.14/05/29 15:14:00 INFO mapred.JobClient: map 100% reduce 100%14/05/29 15:14:00 INFO mapred.JobClient: Job complete: job_local889277352_000114/05/29 15:14:00 INFO mapred.JobClient: Counters: 1914/05/29 15:14:00 INFO mapred.JobClient: Map-Reduce Framework14/05/29 15:14:00 INFO mapred.JobClient: Spilled Records=4014/05/29 15:14:00 INFO mapred.JobClient: Map output materialized bytes=22314/05/29 15:14:00 INFO mapred.JobClient: Reduce input records=2014/05/29 15:14:00 INFO mapred.JobClient: Map input records=414/05/29 15:14:00 INFO mapred.JobClient: SPLIT_RAW_BYTES=10314/05/29 15:14:00 INFO mapred.JobClient: Map output bytes=19514/05/29 15:14:00 INFO mapred.JobClient: Reduce shuffle bytes=014/05/29 15:14:00 INFO mapred.JobClient: Reduce input groups=2014/05/29 15:14:00 INFO mapred.JobClient: Combine output records=2014/05/29 15:14:00 INFO mapred.JobClient: Reduce output records=2014/05/29 15:14:00 INFO mapred.JobClient: Map output records=2214/05/29 15:14:00 INFO mapred.JobClient: Combine input records=2214/05/29 15:14:00 INFO mapred.JobClient: Total committed heap usage (bytes)=29045555214/05/29 15:14:00 INFO mapred.JobClient: File Input Format Counters 14/05/29 15:14:00 INFO mapred.JobClient: Bytes Read=10914/05/29 15:14:00 INFO mapred.JobClient: FileSystemCounters14/05/29 15:14:00 INFO mapred.JobClient: HDFS_BYTES_READ=21814/05/29 15:14:00 INFO mapred.JobClient: FILE_BYTES_WRITTEN=13772614/05/29 15:14:00 INFO mapred.JobClient: FILE_BYTES_READ=55714/05/29 15:14:00 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=13714/05/29 15:14:00 INFO mapred.JobClient: File Output Format Counters 14/05/29 15:14:00 INFO mapred.JobClient: Bytes Written=137
查看在HDFS文件系统中新生成的out文件夹,可以看见生成的part-r-00000,其结果为:
Mary 2a 1and 1as 1everywhere 1fleece 1go 1had 1its 1lamb 2little 1snow 1sure 1that 1the 1to 1very 1was 1went 1white 1
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