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hadoop2.5.0单节点下MR运行WordCount
参考:http://hadoop.apache.org/docs/r2.5.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html
Maven和WordCount代码:
<dependencies> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.4.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-core</artifactId> <version>2.4.1</version> </dependency> </dependencies>
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;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(); Job job = Job.getInstance(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(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); }}
export JAVA_HOME=/usr/java/defaultexport PATH=$JAVA_HOME/bin:$PATHexport HADOOP_CLASSPATH=$JAVA_HOME/lib/tools.jar
运行Shell:
$ bin/hadoop com.sun.tools.javac.Main WordCount.java$ jar cf wc.jar WordCount*.clas
假设:
/user/joe/wordcount/input - input directory in HDFS /user/joe/wordcount/output - output directory in HDFS
以下目录和文件没有的话先创建:
$ bin/hdfs dfs -ls /user/joe/wordcount/input//user/joe/wordcount/input/file01/user/joe/wordcount/input/file02
$ bin/hdfs dfs -cat /user/joe/wordcount/input/file01Hello World Bye World$ bin/hdfs dfs -cat /user/joe/wordcount/input/file02Hello Hadoop Goodbye Hadoop
运行:
$ bin/hadoop jar wc.jar WordCount /user/joe/wordcount/input /user/joe/wordcount/output
输出:
$ bin/hdfs dfs -cat /user/joe/wordcount/output/part-r-00000Bye 1Goodbye 1Hadoop 2Hello 2World 2
hadoop2.5.0单节点下MR运行WordCount
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