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
xml
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.3</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-hdfs</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.3</version>
</dependency>
</dependencies>
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
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
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<k2 v2>
3. 指定job的reducer和输出的类型<k4 v4>
4. 指定job的输入和输出路径
5. 执行job
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
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
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
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 ~]$
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!