参考这个页面,我遇到了类似的问题。我需要提供一个map和reduce方法来计算字长(1到n)的频率。 reference links我已经尝试了答案的方法来实现此实现。
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.LongWritable;
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 {
//Mapper which implement the mapper() function
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
//public static class TokenizerMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
//check whether word is start from a or b
String wordToCheck = itr.nextToken();
word.set(String.valueOf(wordToCheck.length()));
context.write(word, one);
//if (wordToCheck.startsWith("a")||wordToCheck.startsWith("b")){
// word.set(wordToCheck);
// context.write(word, one);
//}
//check for word length
//if (wordToCheck.length() > 8) {
// }
}
}
}
//Reducer which implement the reduce() function
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);
}
}
//Driver class to specific the Mapper and Reducer
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.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
我有以下例外情况。
17/02/25 17:02:34 INFO mapreduce.Job: map 0% reduce 0%
17/02/25 17:02:36 INFO mapreduce.Job: map 100% reduce 0%
17/02/25 17:02:36 INFO mapreduce.Job: Task Id : attempt_1488013180963_0001_m_000000_2, Status : FAILED
Error: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritable
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1069)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:712)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
at org.apache.hadoop.mapreduce.Mapper.map(Mapper.java:124)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:784)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:163)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1656)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
我在Eclipse Kepler中开发这个类,并在ubuntu LTXTerminal中使用hadoop 2.6.3将该类作为jar文件运行。问题是什么?我也尝试按照答案中的建议使用IntWritable,但是,它也有类似的反应。
答案 0 :(得分:1)
我不是100%肯定,但是当你使用文件作为输入时,mapper应该有LongWritable
类型的键(对应于文件中的行号)和Text
的值(文件)作为文字行。)
所以可能的解决方案是替换
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
与
public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {