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);
}
}
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