本文以Hadoop2.7.3单机模式环境下的WordCount实例来介绍2.7.3版本中如何编辑自己的MapReduce程序
实例中至少有3个jar包:
通过命令hadoop classpath可以得到运行Hadoop程序所需的全部classpath信息
可以将Hadoop的classpath信息添加到CLASSPATH变量中,在/etc/profile中增加如下几行:
export HADOOP_HOME=/usr/local/hadoop
export CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath):$CLASSPATH
执行source /etc/profile使变量生效,接着就可以通过javac命令编辑WordCount.java
1.首先建立一个工作目录:
mkdir WordCount
cd WordCount
将WordCount源码放置该目录下,进行编译
javac WordCount.java
接着把.class文件打包成jar,才能在Hadoop中运行
jar -cvf WordCount.jar ./WordCount*.class
打包完成后,创建几个输入文件来测试一下:
mkdir input
echo "today is a good day" > ./input/test1
echo "today i am happy,so how about the other day" > ./input/test2
如果读者的Hadoop是单机模式,请跳过此步骤。如果读者的Hadoop环境已经配置成伪分布,那么需要执行以下命令,小编的是伪分布式
保证已经开启Hadoop服务
start-all.sh,相当于执行start-dfs.sh and start-yarn.sh
可以到hadoop安装目录下开启hadoop服务
# 把本地文件上传到伪分布式HDFS上
/usr/local/hadoop/bin/hadoop fs -put ./input input
开始执行,命令行输入
/usr/local/hadoop/bin/hadoop fs -put ./input input
伪分布正确的执行结果如下
如果读者已经将Hadoop的bin目录添加到环境变量,那么每一句命令的/usr/local/hadoop/bin/都可以不写
import java.io.IOException;
import java.util.Iterator;
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 WordCount() {
}
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> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCount.TokenizerMapper.class);
job.setCombinerClass(WordCount.IntSumReducer.class);
job.setReducerClass(WordCount.IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for(int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true)?0:1);
}
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public IntSumReducer() {
}
public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int sum = 0;
IntWritable val;
for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {
val = (IntWritable)i$.next();
}
this.result.set(sum);
context.write(key, this.result);
}
}
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
private static final IntWritable one = new IntWritable(1);
private Text word = new Text();
public TokenizerMapper() {
}
public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens()) {
this.word.set(itr.nextToken());
context.write(this.word, one);
}
}
}
}
作者:秦景坤
GitHub:https://github.com/Roc-J
CSDN博客:http://blog.csdn.net/qjk19940101
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