Hadoop S3 No Space Left On Device - amazon-s3

Question

I am running a map reduce job that takes a small input (~3MB, list of integers of size z),
with a sparse matrix cache of size n x m, and basically outputs z sparse vectors of dimension (n x 1). The output here is pretty big (~2TB). I am running 20 m1.small nodes on Amazon EC2 with S3 storage as inputs and output.
However, I am getting a IOException: No space left on device.
It seems like there are s3 bytes written on Hadoop logs, but no files are created.
When I used a smaller input (smaller z), the output is correctly there after the job is done.
Thus, I believe that it runs out on a temporary storage.
Is there way to check where this temporary storage is?
Also, funny thing is that the log is saying that all the bytes are written to s3, but I see no files and don't know where these bytes are being written.
Thank you for your help.
Example code (Have also tried to split into map and reduce job with same error)
public void map(LongWritable key, Text value,
Mapper<LongWritable, Text, LongWritable, VectorWritable>.Context context)
throws IOException, InterruptedException
{
// Assume the input is id \t number
String[] input = value.toString().split("\t");
int idx = Integer.parseInt(input[0]) - 1;
// Some operations to do, but basically outputting a vector
// Collect the output
context.write(new LongWritable(idx), new VectorWritable(matrix.getColumn(idx)));
};

Answer 1

Amazon EMR supports a couple of versions. These are the default values 0.20.205
hadoop.tmp.dir - /tmp/hadoop-${user.name} - A base for other temporary directories.
mapred.local.dir - ${hadoop.tmp.dir}/mapred/local - The local directory where MapReduce stores intermediate data files. May be a comma-separated list of directories on different devices in order to spread disk i/o. Directories that do not exist are ignored.
mapred.temp.dir - ${hadoop.tmp.dir}/mapred/temp - A shared directory for temporary files.
Run the du --max-depth=7 /home/xyz | sort -n command on the hadoop.tmp.dir and check which directory is occupying the most space. Although hadoop.tmp.dir says temporary, it stores system and data files also.