MapReduce WordCount程序实践（IDEA版）

环境

Linux：Hadoop2.x

Windows：jdk1.8、Maven3、IDEA2021

步骤

编程分析

编程分析包括：
1.数据过程分析：数据从输入到输出的过程分析。
2.数据类型分析：Map的输入输出类型，Reduce的输入输出类型；
编程分析决定了我们该如何编写代码。

新建Maven工程

打开IDEA–>点击File–>New–>Project

选择Maven–>点击Next

选择一个空目录作为项目目录，目录名称例如：wordcount，建议目录路径不包含中文和空格，点击Finish

添加依赖

修改pom.xml，添加如下依赖

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoopgroupId>
            <artifactId>hadoop-commonartifactId>
            <version>2.7.3version>
        dependency>
        <dependency>
            <groupId>org.apache.hadoopgroupId>
            <artifactId>hadoop-clientartifactId>
            <version>2.7.3version>
        dependency>
        <dependency>
            <groupId>org.apache.hadoopgroupId>
            <artifactId>hadoop-hdfsartifactId>
            <version>2.7.3version>
        dependency>
        <dependency>
            <groupId>org.apache.hadoopgroupId>
            <artifactId>hadoop-mapreduce-client-coreartifactId>
            <version>2.7.3version>
        dependency>
    dependencies>
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加载依赖

新建包

在src\main\java目录下，新建包:org.example

填入org.example，效果如下：

新建类

在org.example包下，新建出三个类，分别为:MyMapper、MyReducer、MyMain，效果如下：

编写Map程序

编辑MyMapper类，步骤如下：

1.继承Mapper
2.重写map()方法
3.编写Map逻辑代码：
	1.v1由Text类型转换为String
	2.按空格进行分词：split(" ")方法
	3.输出k2, v2
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编写Reduce程序

编辑MyReducer类，步骤如下：

1.继承Reducer
2.重写reduce()方法
3.编写Reduce逻辑代码：
    1.k4 = k3
    2.v4 = v3元素的和
    3.输出k4, v4
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编写Main程序（Driver程序）

编辑MyMain类，步骤如下：

1. 创建一个job和任务入口(指定主类)
2. 指定job的mapper和输出的类型
3. 指定job的reducer和输出的类型
4. 指定job的输入和输出路径
5. 执行job
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思考

代码编写完成后，可以先在Windows本地运行吗？

打包

看到BUILD SUCCESS为打包成功

打包后得到的jar包，在项目的target目录下

提交到Hadoop集群运行

1.将上一步打包得到的jar包，上传到linux

2.启动hadoop集群

start-all.sh
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3.运行jar包

从Linux本地上传一个文件到hdfs

hdfs dfs -put 1.txt /input/1.txt
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hdfs查看输入数据

运行jar包

hadoop jar wordcount-1.0-SNAPSHOT.jar org.example.MyMain /input/1.txt /output/wordcount
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正常运行过程输出如下：

[hadoop@node1 ~]$ hadoop jar wordcount-1.0-SNAPSHOT.jar org.example.MyMain /input/1.txt /output/wordcount
22/03/29 00:23:59 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.193.140:8032
22/03/29 00:23:59 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
22/03/29 00:24:00 INFO input.FileInputFormat: Total input paths to process : 1
22/03/29 00:24:00 INFO mapreduce.JobSubmitter: number of splits:1
22/03/29 00:24:01 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1648484275192_0001
22/03/29 00:24:01 INFO impl.YarnClientImpl: Submitted application application_1648484275192_0001
22/03/29 00:24:01 INFO mapreduce.Job: The url to track the job: http://node1:8088/proxy/application_1648484275192_0001/
22/03/29 00:24:01 INFO mapreduce.Job: Running job: job_1648484275192_0001
22/03/29 00:24:08 INFO mapreduce.Job: Job job_1648484275192_0001 running in uber mode : false
22/03/29 00:24:08 INFO mapreduce.Job:  map 0% reduce 0%
22/03/29 00:24:12 INFO mapreduce.Job:  map 100% reduce 0%
22/03/29 00:24:17 INFO mapreduce.Job:  map 100% reduce 100%
22/03/29 00:24:19 INFO mapreduce.Job: Job job_1648484275192_0001 completed successfully
22/03/29 00:24:19 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=55
		FILE: Number of bytes written=237261
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=119
		HDFS: Number of bytes written=25
		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)=2290
		Total time spent by all reduces in occupied slots (ms)=2516
		Total time spent by all map tasks (ms)=2290
		Total time spent by all reduce tasks (ms)=2516
		Total vcore-milliseconds taken by all map tasks=2290
		Total vcore-milliseconds taken by all reduce tasks=2516
		Total megabyte-milliseconds taken by all map tasks=2344960
		Total megabyte-milliseconds taken by all reduce tasks=2576384
	Map-Reduce Framework
		Map input records=2
		Map output records=4
		Map output bytes=41
		Map output materialized bytes=55
		Input split bytes=94
		Combine input records=0
		Combine output records=0
		Reduce input groups=3
		Reduce shuffle bytes=55
		Reduce input records=4
		Reduce output records=3
		Spilled Records=8
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=103
		CPU time spent (ms)=1200
		Physical memory (bytes) snapshot=425283584
		Virtual memory (bytes) snapshot=4223356928
		Total committed heap usage (bytes)=277348352
	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=25
	File Output Format Counters 
		Bytes Written=25
[hadoop@node1 ~]$ 

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查看输出结果

思考

• 如果运行过程报如下错误，该如何解决？
  

• 代码还可以优化吗？如何优化？

完成！enjoy it!