Hadoop MapReduce示例程序WordCount.java手动编译运行解析

WordCount.java

vi WordCount.java

import java.io.IOException;
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 static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

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> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

编译
建立保存生成的编译后的class文件的文件夹wordcount_classes

mkdir ~/wordcount_classes

需要指定编译依赖的jar包，中间用冒号隔开

javac -classpath /usr/lib/hadoop-0.20/hadoop-core-0.20.2-cdh3u6.jar:/usr/lib/hadoop-0.20/lib/commons-cli-1.2.jar -d wordcount_classes WordCount.java 

打包

jar -cvf WordCount.jar -C wordcount_classes/ .

运行

hadoop jar ~/WordCount.jar WordCount input output

input对应hdfs://user/root/input文件夹,output是结果输出的文件夹，必须是原来不存在的，否则将运行不成功，output将生成在/user/root/ouput位置。

结果

root@bjidss46:~# hadoop jar WordCount.jar WordCount input output
13/11/20 16:10:07 INFO input.FileInputFormat: Total input paths to process : 1
13/11/20 16:10:07 WARN snappy.LoadSnappy: Snappy native library is available
13/11/20 16:10:07 INFO util.NativeCodeLoader: Loaded the native-hadoop library
13/11/20 16:10:07 INFO snappy.LoadSnappy: Snappy native library loaded
13/11/20 16:10:07 INFO mapred.JobClient: Running job: job_201311201528_0008
13/11/20 16:10:08 INFO mapred.JobClient: map 0% reduce 0%
13/11/20 16:10:12 INFO mapred.JobClient: map 100% reduce 0%
13/11/20 16:10:16 INFO mapred.JobClient: map 100% reduce 100%
13/11/20 16:10:16 INFO mapred.JobClient: Job complete: job_201311201528_0008
13/11/20 16:10:16 INFO mapred.JobClient: Counters: 26
13/11/20 16:10:16 INFO mapred.JobClient: Job Counters
13/11/20 16:10:16 INFO mapred.JobClient: Launched reduce tasks=1
13/11/20 16:10:16 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=4473
13/11/20 16:10:16 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
13/11/20 16:10:16 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
13/11/20 16:10:16 INFO mapred.JobClient: Launched map tasks=1
13/11/20 16:10:16 INFO mapred.JobClient: Data-local map tasks=1
13/11/20 16:10:16 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=3523
13/11/20 16:10:16 INFO mapred.JobClient: FileSystemCounters
13/11/20 16:10:16 INFO mapred.JobClient: FILE_BYTES_READ=57
13/11/20 16:10:16 INFO mapred.JobClient: HDFS_BYTES_READ=138
13/11/20 16:10:16 INFO mapred.JobClient: FILE_BYTES_WRITTEN=105460
13/11/20 16:10:16 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=35
13/11/20 16:10:16 INFO mapred.JobClient: Map-Reduce Framework
13/11/20 16:10:16 INFO mapred.JobClient: Map input records=1
13/11/20 16:10:16 INFO mapred.JobClient: Reduce shuffle bytes=57
13/11/20 16:10:16 INFO mapred.JobClient: Spilled Records=8
13/11/20 16:10:16 INFO mapred.JobClient: Map output bytes=43
13/11/20 16:10:16 INFO mapred.JobClient: CPU time spent (ms)=1530
13/11/20 16:10:16 INFO mapred.JobClient: Total committed heap usage (bytes)=504758272
13/11/20 16:10:16 INFO mapred.JobClient: Combine input records=4
13/11/20 16:10:16 INFO mapred.JobClient: SPLIT_RAW_BYTES=111
13/11/20 16:10:16 INFO mapred.JobClient: Reduce input records=4
13/11/20 16:10:16 INFO mapred.JobClient: Reduce input groups=4
13/11/20 16:10:16 INFO mapred.JobClient: Combine output records=4
13/11/20 16:10:16 INFO mapred.JobClient: Physical memory (bytes) snapshot=334163968
13/11/20 16:10:16 INFO mapred.JobClient: Reduce output records=4
13/11/20 16:10:16 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2914021376
13/11/20 16:10:16 INFO mapred.JobClient: Map output records=4

总结

之所以使用原始的javac方式编译执行是为了更了解mapreduce的流程，使用eclipse的时候导出jar请不要将依赖的诸多jar包一起打包，只需要hadoop-core-0.20.2-cdh3u6.jar和/usr/lib/hadoop-0.20/lib/commons-cli-1.2.jar即可。

参考

Hadoop MapReduce教程（Apache官网）

第一个mapreduce程序

WordCount运行详解