这篇文章主要介绍“WordCount On Hadoop怎么实现”，在日常操作中，相信很多人在WordCount On Hadoop怎么实现问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”WordCount On Hadoop怎么实现”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
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官方例子：
WordCount2.java
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
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.mapreduce.Counter;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.StringUtils;
public class WordCount2 {
public static class TokenizerMapper extends
Mapper {
static enum CountersEnum {
INPUT_WORDS
}
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private boolean caseSensitive;
private Set patternsToSkip = new HashSet();
private Configuration conf;
private BufferedReader fis;
@Override
public void setup(Context context) throws IOException,
InterruptedException {
conf = context.getConfiguration();
caseSensitive = conf.getBoolean("wordcount.case.sensitive", true);
if (conf.getBoolean("wordcount.skip.patterns", false)) {//官方例子为true，若无配置文件将报错，改为false正常。参见：https://issues.apache.org/jira/browse/MAPREDUCE-6038
URI[] patternsURIs = Job.getInstance(conf).getCacheFiles();
for (URI patternsURI : patternsURIs) {
Path patternsPath = new Path(patternsURI.getPath());
String patternsFileName = patternsPath.getName().toString();
parseSkipFile(patternsFileName);
}
}
}
private void parseSkipFile(String fileName) {
try {
fis = new BufferedReader(new FileReader(fileName));
String pattern = null;
while ((pattern = fis.readLine()) != null) {
patternsToSkip.add(pattern);
}
} catch (IOException ioe) {
System.err
.println("Caught exception while parsing the cached file '"
+ StringUtils.stringifyException(ioe));
}
}
@Override
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
String line = (caseSensitive) ? value.toString() : value.toString()
.toLowerCase();
for (String pattern : patternsToSkip) {
line = line.replaceAll(pattern, "");
}
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
Counter counter = context.getCounter(
CountersEnum.class.getName(),
CountersEnum.INPUT_WORDS.toString());
counter.increment(1);
}
}
}
public static class IntSumReducer extends
Reducer {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable 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();
GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
String[] remainingArgs = optionParser.getRemainingArgs();
if (!(remainingArgs.length != 2 || remainingArgs.length != 4)) {
System.err
.println("Usage: wordcount [-skip skipPatternFile]");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount2.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
List otherArgs = new ArrayList();
for (int i = 0; i < remainingArgs.length; ++i) {
if ("-skip".equals(remainingArgs[i])) {
job.addCacheFile(new Path(remainingArgs[++i]).toUri());
job.getConfiguration().setBoolean("wordcount.skip.patterns",
true);
} else {
otherArgs.add(remainingArgs[i]);
}
}
FileInputFormat.addInputPath(job, new Path(otherArgs.get(0)));
FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1)));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
} cd /data/program javac -classpath /home/hadoop/hadoop-2.7.1/share/hadoop/common/hadoop-common-2.7.1.jar:/home/hadoop/hadoop-2.7.1/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.7.1.jar:/home/hadoop/hadoop-2.7.1/share/hadoop/common/lib/commons-cli-1.2.jar WordCount2.java jar cf wc.jar WordCount*.class cd /home/hadoop/hadoop-2.7.1/ bin/hadoop jar wc.jar WordCount2 /program/input /program/output
到此，关于“WordCount On Hadoop怎么实现”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注创新互联网站，小编会继续努力为大家带来更多实用的文章！
