最近精读Hadoop WordCount的示例,发现里面应该存在一个"可能的"Bug,现斗胆发出,希望有高人指点.
Bug描述:
WordCount数单词的时候,如果遇到大文件会对文件进行切分.但是切分是按照字节来进行的,完全有可能会将一个单词切分成2个单词,这样也就可能会创造2个不存在的单词.
相关代码:
- WordCount main 函数
( FileInputFormat.addInputPath(job, new Path(otherArgs[0]));)
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定义了大的输入文件,如何拆分
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
- 具体拆分代码:
public InputSplit[] getSplits(JobConf job, int numSplits)
throws IOException {
FileStatus[] files = listStatus(job);
// Save the number of input files in the job-conf
job.setLong(NUM_INPUT_FILES, files.length);
long totalSize = 0; // compute total size
for (FileStatus file: files) { // check we have valid files
if (file.isDir()) {
throw new IOException("Not a file: "+ file.getPath());
}
totalSize += file.getLen();
}
long goalSize = totalSize / (numSplits == 0 ? 1 : numSplits);
long minSize = Math.max(job.getLong("mapred.min.split.size", 1),
minSplitSize);
// generate splits
ArrayList<FileSplit> splits = new ArrayList<FileSplit>(numSplits);
NetworkTopology clusterMap = new NetworkTopology();
//可以很清楚的看出来,这里的拆分全部是基于字节的.
for (FileStatus file: files) {
Path path = file.getPath();
FileSystem fs = path.getFileSystem(job);
long length = file.getLen();
BlockLocation[] blkLocations = fs.getFileBlockLocations(file, 0, length);
if ((length != 0) && isSplitable(fs, path)) {
long blockSize = file.getBlockSize();
long splitSize = computeSplitSize(goalSize, minSize, blockSize);
long bytesRemaining = length;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
String[] splitHosts = getSplitHosts(blkLocations,
length-bytesRemaining, splitSize, clusterMap);
splits.add(new FileSplit(path, length-bytesRemaining, splitSize,
splitHosts));
bytesRemaining -= splitSize;
}
if (bytesRemaining != 0) {
splits.add(new FileSplit(path, length-bytesRemaining, bytesRemaining,
blkLocations[blkLocations.length-1].getHosts()));
}
} else if (length != 0) {
String[] splitHosts = getSplitHosts(blkLocations,0,length,clusterMap);
splits.add(new FileSplit(path, 0, length, splitHosts));
} else {
//Create empty hosts array for zero length files
splits.add(new FileSplit(path, 0, length, new String[0]));
}
}
LOG.debug("Total # of splits: " + splits.size());
return splits.toArray(new FileSplit[splits.size()]);
}
原因猜想:
Hadoop处理的是大数据,拆分中出现几个单词错误是可以接受的,所以Hadoop就没有对这个进行特别严格的处理.
解决方法:
也就是在后期读取文件时,做一些微调. 具体详情可以参考Blog: 大数据笔记--续谈WordCount的Bug