1. Mapper output, reduce shuffle/short(If data is less it will be inmemory) output is saved on to localfile system .
2. mapreduce.cluster.local.dir provides a list of dir where the temp data will be saved . Search for jobid inside dir to know exact location of the temp data at runtime
but you will not benefit anything in terms of debugging . What you should be actually looking for
1. Are your reducer starting after all mappers have finished. mapreduce.slow.start => 1
2. how many groups are being processed per reducer. try increasing number of reducer to increase parallelism
3. Is your job shuffering from skewed key problem ie for a given key one has lot of values.
4. disk failures where mappers and reducers are running. run dmessage to know about disk failure where the job is running