hadoop学习之----------IntelliJ IDEA上实现MapReduce中最简单的单词统计的程序(本地和 hadoop 两种实现方式)

idea上的maven中的pom.xml文件<?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.it18zhang</groupId>
    <artifactId>HdfsDemo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <packaging>jar</packaging>

    <dependencies>
　　　　<dependency>

    　　　　<groupId>org.apache.hadoop</groupId>
    　　　　<artifactId>hadoop-core</artifactId>
   　　　　 <version>1.2.1</version>
　　　　</dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-common</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-server-resourcemanager</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.anarres.lzo</groupId>
            <artifactId>lzo-hadoop</artifactId>
            <version>1.0.0</version>
            <scope>compile</scope>
        </dependency>

        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
        </dependency>

    </dependencies>
</project>

mapper

package com.it18zhang.hdfs.mr.mr;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/*
 *
 * Mapper
 * */

public class WorldMapper extends Mapper<LongWritable,Text,Text,IntWritable> {
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        Text keyOut = new Text();
        IntWritable valueOut = new IntWritable();
        String[] arr = value.toString().split(" ");
        for (String s : arr){
            keyOut.set(s);
            valueOut.set(1);
            context.write(keyOut,valueOut);

        }
    }
}

reducer

package com.it18zhang.hdfs.mr.mr;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;


/*
* Reducer
*
*/

public class Worldreducer extends Reducer<Text,IntWritable,Text,IntWritable> {

    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int count = 0 ;
        for (IntWritable iw : values){
            count = count + iw.get() ;
        }
    context.write(key,new IntWritable(count));






    }
}

worldcount

package com.it18zhang.hdfs.mr.mr;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class WorldApp {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration() ;
        //conf.set("fs.defaultFS","file:///");
        Job job = Job.getInstance(conf);

        if(args.length > 1) {
            FileSystem.get(conf).delete(new Path(args[1]));
        }

        //设置job的各种属性
        job.setJobName("WorldApp"); //作业名称
        job.setJarByClass(WorldApp.class);//搜索类
        job.setInputFormatClass(TextInputFormat.class);//设置输入格式

        //添加输入路径
        FileInputFormat.addInputPath(job,new Path(args[0]));
        //添加输出路径
        FileOutputFormat.setOutputPath(job,new Path(args[1]));


        job.setMapperClass(WorldMapper.class);     //Mapper类
        job.setReducerClass(Worldreducer.class);   //Reduce类

        job.setNumReduceTasks(1); //reduce个数
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.waitForCompletion(true);
    }
}

本地模式需要将

 //conf.set("fs.defaultFS","file:///");  
前面的//去掉 需要设置文件位置
我的文件输入输出位置是在

words.txt里面的内容

hello world tom
hello tom world
tom hello world
how are you

开始设置文件输入输出位置,先运行一下WorldApp.java
再点击Run--->edit Configurations

左侧applications下是WorldApp ,在Program arguments 输入你的文件位置,格式见下图 (注意不应该出现中文字符)

开始两个bug的调试

第一个bug是指在windows上出现 ,具体如下为防止链接丢失粘贴点关键信息

https://www.linuxidc.com/wap.aspx?cid=9&cp=2&nid=96531&p=4&sp=753

2013-9-3019:25:02 org.apache.hadoop.security.UserGroupInformation doAs
严重: PriviledgedActionExceptionas:Administrator cause:java.io.IOException: Failed to set permissions of path:\tmp\hadoop-Administrator\mapred\staging\Administrator1702422322\.staging to0700
Exception inthread "main" java.io.IOException: Failed to set permissions of path:\tmp\hadoop-Administrator\mapred\staging\Administrator1702422322\.staging to0700
atorg.apache.hadoop.fs.FileUtil.checkReturnValue(FileUtil.java:689)
atorg.apache.hadoop.fs.FileUtil.setPermission(FileUtil.java:662)
at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:509)
atorg.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:344)
atorg.apache.hadoop.fs.FilterFileSystem.mkdirs(FilterFileSystem.java:189)
at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:116)
atorg.apache.hadoop.mapred.JobClient$2.run(JobClient.java:856)
atorg.apache.hadoop.mapred.JobClient$2.run(JobClient.java:850)
atjava.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
atorg.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1121)
atorg.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:850)
at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:824)
atorg.apache.hadoop.mapred.JobClient.runJob(JobClient.java:1261)
atorg.conan.myhadoop.mr.WordCount.main(WordCount.java:78)

这个错误是win中开发特有的错误，文件权限问题，在Linux下可以正常运行。

解决方法是，修改/hadoop-1.2.1/src/core/org/apache/hadoop/fs/FileUtil.java文件

688-692行注释，然后重新编译源代码，重新打一个hadoop.jar的包。


685private static void checkReturnValue(boolean rv, File p,
686 FsPermissionpermission
687 )throws IOException {
688 /*if (!rv) {
689 throw new IOException("Failed toset permissions of path: " + p +
690 " to " +
691 String.format("%04o",permission.toShort()));
692 }*/
693 }

注：为了方便，我直接在网上下载的已经编译好的hadoop-core-1.2.1.jar包


我们还要替换maven中的hadoop类库。
~cp lib/hadoop-core-1.2.1.jarC:\Users\licz\.m2\repository\org\apache\hadoop\hadoop-core\1.2.1\hadoop-core-1.2.1.jar

至于修改好的hadoop-core-1.2.1.jar ,我用的是https://download.csdn.net/download/yunlong34574/7079951

我分享一下

链接：https://pan.baidu.com/s/1pswB27oOnlWCXR5iRn_qKA
提取码：p1v6

第二个bug是没有设置VM options 类似以下

http://mail-archives.apache.org/mod_mbox/hadoop-mapreduce-user/201406.mbox/%3C1530609F-0168-4933-AC87-8F78395C83BB%40gmail.com%3E

解决办法,看以下VM options 的配置 hadoop-2.7.3.tar.gz解压后的文件在里面去找native

我在运行时就出现这两个问题比较难解决

运行结果如下

运行会出现的警告忽视类似于

六月 30, 2019 11:01:08 上午 org.apache.hadoop.util.NativeCodeLoader <clinit>
警告: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
六月 30, 2019 11:01:08 上午 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
六月 30, 2019 11:01:08 上午 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
六月 30, 2019 11:01:08 上午 org.apache.hadoop.mapreduce.lib.input.FileInputFormat listStatus
信息: Total input paths to process : 1
六月 30, 2019 11:01:08 上午 org.apache.hadoop.io.compress.snappy.LoadSnappy <clinit>
警告: Snappy native library not loaded
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Running job: job_local425271598_0001
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job run
信息: Waiting for map tasks
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable run
信息: Starting task: attempt_local425271598_0001_m_000000_0
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask runNewMapper
信息: Processing split: file:/d:/mr/words.txt:0+64
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: io.sort.mb = 100
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: data buffer = 79691776/99614720
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: record buffer = 262144/327680
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local425271598_0001_m_000000_0 is done. And is in the process of commiting
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local425271598_0001_m_000000_0' done.
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable run
信息: Finishing task: attempt_local425271598_0001_m_000000_0
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job run
信息: Map task executor complete.
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Merging 1 sorted segments
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Down to the last merge-pass, with 1 segments left of total size: 134 bytes
六月 30, 2019 11:01:09 上午 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local425271598_0001_r_000000_0 is done. And is in the process of commiting
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Task commit
信息: Task attempt_local425271598_0001_r_000000_0 is allowed to commit now
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter commitTask
信息: Saved output of task 'attempt_local425271598_0001_r_000000_0' to file:/d:/mr/out
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: reduce > reduce
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local425271598_0001_r_000000_0' done.
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 100%
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Job complete: job_local425271598_0001
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Counters: 17
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Map-Reduce Framework
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Spilled Records=24
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Map output materialized bytes=138
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Reduce input records=12
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Map input records=4
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: SPLIT_RAW_BYTES=86
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Map output bytes=108
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Reduce shuffle bytes=0
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Reduce input groups=6
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Combine output records=0
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Reduce output records=6
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Map output records=12
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Combine input records=0
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Total committed heap usage (bytes)=385875968
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: File Input Format Counters
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Bytes Read=64
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: FileSystemCounters
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_WRITTEN=103142
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_READ=546
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: File Output Format Counters
六月 30, 2019 11:01:10 上午 org.apache.hadoop.mapred.Counters log
信息: Bytes Written=52

Process finished with exit code 0

输出 out文件夹里面的内容如下

查看part-r-00000

hadoop模式

//conf.set("fs.defaultFS","file:///");  

将文件打包(右上角有个闪电图标,我点了,跳过Test文件夹)

左侧target下会发现你打包好的jar包

右击HdfsDemo-1.0-Snapshot.jar 点击 file path 点击HdfsDemo-1.0-Snapshot.ja

将这个jar包通过xshell 的下xftp传到虚拟机上

启动集群

创建输出输出的放置位置

写文件并传上去

执行WorldApp

19/07/01 10:31:36 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.72.111:8032
19/07/01 10:31:39 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your applica
tion with ToolRunner to remedy this.19/07/01 10:31:41 INFO input.FileInputFormat: Total input paths to process : 1
19/07/01 10:31:42 INFO mapreduce.JobSubmitter: number of splits:1
19/07/01 10:31:42 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1561947478859_0001
19/07/01 10:31:45 INFO impl.YarnClientImpl: Submitted application application_1561947478859_0001
19/07/01 10:31:45 INFO mapreduce.Job: The url to track the job: http://node1:8088/proxy/application_1561947478859_0001/
19/07/01 10:31:45 INFO mapreduce.Job: Running job: job_1561947478859_0001
19/07/01 10:32:33 INFO mapreduce.Job: Job job_1561947478859_0001 running in uber mode : false
19/07/01 10:32:33 INFO mapreduce.Job: map 0% reduce 0%
19/07/01 10:33:04 INFO mapreduce.Job: map 100% reduce 0%
19/07/01 10:33:27 INFO mapreduce.Job: map 100% reduce 100%
19/07/01 10:33:29 INFO mapreduce.Job: Job job_1561947478859_0001 completed successfully
19/07/01 10:33:29 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=94
FILE: Number of bytes written=237037
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=141
HDFS: Number of bytes written=40
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=25599
Total time spent by all reduces in occupied slots (ms)=19918
Total time spent by all map tasks (ms)=25599
Total time spent by all reduce tasks (ms)=19918
Total vcore-milliseconds taken by all map tasks=25599
Total vcore-milliseconds taken by all reduce tasks=19918
Total megabyte-milliseconds taken by all map tasks=26213376
Total megabyte-milliseconds taken by all reduce tasks=20396032
Map-Reduce Framework
Map input records=1
Map output records=8
Map output bytes=72
Map output materialized bytes=94
Input split bytes=101
Combine input records=0
Combine output records=0
Reduce input groups=6
Reduce shuffle bytes=94
Reduce input records=8
Reduce output records=6
Spilled Records=16
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=762
CPU time spent (ms)=5310
Physical memory (bytes) snapshot=297164800
Virtual memory (bytes) snapshot=4157005824
Total committed heap usage (bytes)=141647872
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=40
File Output Format Counters
Bytes Written=40

打开8088端口看一下

因为我执行了两次 ,所以出现两次记录正常的应该是

查看结果

发表于 2019-06-30 11:40 木木爱阿狸阅读(744) 评论(0) 编辑收藏举报

hadoop学习之----------IntelliJ IDEA上实现MapReduce中最简单的单词统计的程序(本地 和 hadoop 两种实现方式)

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