上一篇文章寫了如何藉助 docker 搭建一套可以簡單運行的 Hadoop 集羣，搭建好了就可以使用了。
在 hadoop 應用中，最簡單的例子應該就是 wordcount 這種類型的了，這次也來走一遍這個流程。
項目搭建
IDEA、Maven 項目
放下 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>org.example</groupId>
<artifactId>mavenusage</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>commons-beanutils</groupId>
<artifactId>commons-beanutils</artifactId>
<version>1.9.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.8.5</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.8.5</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.8.5</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.8.5</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
</configuration>
</plugin>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<archive>
<manifest>
<mainClass>com.myhadoop.WordCount</mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
有一個HDFSConnect.java，這個是用來測試是否可以用代碼鏈接搭建好的 hadoop 集羣。文件內容如下：
package com.myhadoop;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import java.io.IOException;
public class HDFSConnect {
public static void main(String[] args) throws IOException {
System.out.println("hello world");
Configuration conf=new Configuration();
conf.set("fs.defaultFS","hdfs://localhost:19000");
FileSystem hdfs = FileSystem.get(conf);
boolean is_success = hdfs.mkdirs(new Path("/guoruibiaonew"));
if(is_success){
System.out.println("success");
}else{
System.out.println("failure");
}
hdfs.close();
}
}
這裏需要注意的是端口部分，docker 映射到本地是19000，如果打開了防火牆設置，直連 docker 容器內部的話，應該是172.18.0.2:9000。
查看是否創建成功，就可以隨便找一臺節點，使用如下命令查看即可：
hdfs dfs -ls /
編寫代碼
編寫代碼遵循 map ➕ reduce模式即可。
MyMapper.java
package com.myhadoop;
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;
public class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private Text k = new Text();
private IntWritable v = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] words = value.toString().split(" ");
for (String word : words) {
k.set(word);
context.write(k, v);
}
}
}
MyReducer.java
package com.myhadoop;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable v = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
v.set(sum);
context.write(key,v);
}
}
WordCount.java
package com.myhadoop;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class WordCount {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//構建Configuration實例
Configuration configuration = new Configuration();
//其他配置信息
//獲得Job實例
Job job = Job.getInstance(configuration,"My WordCount Job");
job.setJarByClass(WordCount.class);
//設置Mapper和Reducer處理類
job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.class);
//設置Mapper和Reducer的輸入輸出格式
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
//設置輸出結果的數據格式
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//指定輸入和輸出路徑
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//提交任務，true爲提交成功，如果爲true打印0，爲false打印1
boolean b = job.waitForCompletion(true);
System.exit(b ? 0 : 1);
}
}
項目打包
因爲本項目使用的是 maven 構建，所以可以很容易的打包。
操作流程是
mvn clean
mvn package
對應到 IDE 裏面直接看下圖即可。
扔到 hadoop 中執行
在執行之前，先隨便寫點內容，放到 hdfs 上。比如寫一個 data.log，文件內容如下：
hello world
hello hadoop
hello tiger
this is a data file.
如果這個文件是在本地編寫的，那還需要把文件拷貝到 docker 的 container 中。具體命令爲
docker cp /Users/biao/IDEAProjects/mavenusage/data.log 7da3f0644f0f:/tmp
然後再 hadoop-node1 上使用 hdfs 命令將文件上傳到 hadoop 的 HDFS 上。
# 如果未創建 hdfs 上的文件目錄，需要創建一下，命令如下：
# hdfs dfs -mkdir /guoruibiao
hdfs dfs -put /tmp/data.log /guoruibiao
然後需要注意的是maven 打包好的 jar 文件，也是需要放到數據節點中的，否則執行就會失敗，命令如下：
docker cp /Users/biao/IDEAProjects/mavenusage/target/mavenusage-1.0-SNAPSHOT.jar 7da3f0644f0f:/tmp
docker cp /Users/biao/IDEAProjects/mavenusage/target/mavenusage-1.0-SNAPSHOT.jar fe846930210d:/tmp
路徑按照自己的來，這裏只是做下參考。
do it
萬事俱備了，下面正式將 jar 交給 hadoop 去執行。
[root@hadoop-node1 tmp]# hadoop jar /tmp/mavenusage-1.0-SNAPSHOT.jar com.myhadoop.WordCount /guoruibiao/data.log /wordcountoutput
20/04/11 06:52:08 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
20/04/11 06:52:09 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
20/04/11 06:52:10 INFO input.FileInputFormat: Total input files to process : 1
20/04/11 06:52:10 INFO mapreduce.JobSubmitter: number of splits:1
20/04/11 06:52:10 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1586586904355_0003
20/04/11 06:52:11 INFO impl.YarnClientImpl: Submitted application application_1586586904355_0003
20/04/11 06:52:11 INFO mapreduce.Job: The url to track the job: http://hadoop-node1:8088/proxy/application_1586586904355_0003/
20/04/11 06:52:11 INFO mapreduce.Job: Running job: job_1586586904355_0003
20/04/11 06:52:22 INFO mapreduce.Job: Job job_1586586904355_0003 running in uber mode : false
20/04/11 06:52:22 INFO mapreduce.Job: map 0% reduce 0%
20/04/11 06:52:33 INFO mapreduce.Job: map 100% reduce 0%
20/04/11 06:52:42 INFO mapreduce.Job: map 100% reduce 100%
20/04/11 06:52:43 INFO mapreduce.Job: Job job_1586586904355_0003 completed successfully
20/04/11 06:52:43 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=130
FILE: Number of bytes written=315797
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=167
HDFS: Number of bytes written=64
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)=8082
Total time spent by all reduces in occupied slots (ms)=6327
Total time spent by all map tasks (ms)=8082
Total time spent by all reduce tasks (ms)=6327
Total vcore-milliseconds taken by all map tasks=8082
Total vcore-milliseconds taken by all reduce tasks=6327
Total megabyte-milliseconds taken by all map tasks=8275968
Total megabyte-milliseconds taken by all reduce tasks=6478848
Map-Reduce Framework
Map input records=4
Map output records=11
Map output bytes=102
Map output materialized bytes=130
Input split bytes=109
Combine input records=0
Combine output records=0
Reduce input groups=9
Reduce shuffle bytes=130
Reduce input records=11
Reduce output records=9
Spilled Records=22
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=142
CPU time spent (ms)=1690
Physical memory (bytes) snapshot=412508160
Virtual memory (bytes) snapshot=3884216320
Total committed heap usage (bytes)=270008320
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=58
File Output Format Counters
Bytes Written=64
[root@hadoop-node1 tmp]# hdfs dfs -ls /wordcountoutput
hdfs dFound 2 items
-rw-r--r-- 2 root supergroup 0 2020-04-11 06:52 /wordcountoutput/_SUCCESS
-rw-r--r-- 2 root supergroup 64 2020-04-11 06:52 /wordcountoutput/part-r-00000
fs[root@hadoop-node1 tmp]# hdfs dfs -cat /wordcountoutput/part-r-00000
a 1
data 1
file. 1
hadoop 1
hello 3
is 1
this 1
tiger 1
world 1
[root@hadoop-node1 tmp]#
enjoy it.