我正在使用hadoop1.2.1,出于某种原因 Word Count
输出看起来很奇怪:
输入文件:
this is sparta this was sparta hello world goodbye world
hdfs输出:
goodbye 1
hello 1
is 1
sparta 1
sparta 1
this 1
this 1
was 1
world 1
world 1
代码:
public class WordCount {
public static class Map extends Mapper<LongWritable, Text, Text, 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 {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
context.write(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "wordcount");
job.setJarByClass(WordCount.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}
下面是一些相关的控制台输出:
14/01/04 16:17:37 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
14/01/04 16:17:37 INFO input.FileInputFormat: Total input paths to process : 1
14/01/04 16:17:37 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/01/04 16:17:37 WARN snappy.LoadSnappy: Snappy native library not loaded
14/01/04 16:17:38 INFO mapred.JobClient: Running job: job_201401041506_0013
14/01/04 16:17:39 INFO mapred.JobClient: map 0% reduce 0%
14/01/04 16:17:45 INFO mapred.JobClient: map 100% reduce 0%
14/01/04 16:17:52 INFO mapred.JobClient: map 100% reduce 33%
14/01/04 16:17:54 INFO mapred.JobClient: map 100% reduce 100%
14/01/04 16:17:55 INFO mapred.JobClient: Job complete: job_201401041506_0013
14/01/04 16:17:55 INFO mapred.JobClient: Counters: 26
14/01/04 16:17:55 INFO mapred.JobClient: Job Counters
14/01/04 16:17:55 INFO mapred.JobClient: Launched reduce tasks=1
14/01/04 16:17:55 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=6007
14/01/04 16:17:55 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
14/01/04 16:17:55 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
14/01/04 16:17:55 INFO mapred.JobClient: Launched map tasks=1
14/01/04 16:17:55 INFO mapred.JobClient: Data-local map tasks=1
14/01/04 16:17:55 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=9167
14/01/04 16:17:55 INFO mapred.JobClient: File Output Format Counters
14/01/04 16:17:55 INFO mapred.JobClient: Bytes Written=77
14/01/04 16:17:55 INFO mapred.JobClient: FileSystemCounters
14/01/04 16:17:55 INFO mapred.JobClient: FILE_BYTES_READ=123
14/01/04 16:17:55 INFO mapred.JobClient: HDFS_BYTES_READ=169
14/01/04 16:17:55 INFO mapred.JobClient: FILE_BYTES_WRITTEN=122037
14/01/04 16:17:55 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=77
14/01/04 16:17:55 INFO mapred.JobClient: File Input Format Counters
14/01/04 16:17:55 INFO mapred.JobClient: Bytes Read=57
14/01/04 16:17:55 INFO mapred.JobClient: Map-Reduce Framework
14/01/04 16:17:55 INFO mapred.JobClient: Map output materialized bytes=123
14/01/04 16:17:55 INFO mapred.JobClient: Map input records=10
14/01/04 16:17:55 INFO mapred.JobClient: Reduce shuffle bytes=123
14/01/04 16:17:55 INFO mapred.JobClient: Spilled Records=20
14/01/04 16:17:55 INFO mapred.JobClient: Map output bytes=97
14/01/04 16:17:55 INFO mapred.JobClient: Total committed heap usage (bytes)=269619200
14/01/04 16:17:55 INFO mapred.JobClient: Combine input records=0
14/01/04 16:17:55 INFO mapred.JobClient: SPLIT_RAW_BYTES=112
14/01/04 16:17:55 INFO mapred.JobClient: Reduce input records=10
14/01/04 16:17:55 INFO mapred.JobClient: Reduce input groups=7
14/01/04 16:17:55 INFO mapred.JobClient: Combine output records=0
14/01/04 16:17:55 INFO mapred.JobClient: Reduce output records=10
14/01/04 16:17:55 INFO mapred.JobClient: Map output records=10
什么会导致这种情况?我对hadoop非常陌生,所以我不知道该去哪里找。谢谢!
1条答案
按热度按时间vdzxcuhz1#
你使用的是旧的api签名。在1.x+中,reduce方法改为使用iterables而不是iterator(这是旧的0.xapi所使用的,因此您将在书籍和web上的许多示例中看到iterator)。
http://hadoop.apache.org/docs/r1.2.1/api/org/apache/hadoop/mapreduce/reducer.html#reduce%28keyin,%20java.lang.iterable,%20org.apache.hadoop.mapreduce.reducer.context%29
尝试
@override注解告诉编译器检查reduce方法是否重写父类中正确的方法签名。