I see mapred.tasktracker.reduce.tasks.maximum and mapred.tasktracker.map.tasks.maximum, but I'm wondering if there isn't another tuning parameter I need to look at.
I can tune the task tracker so that when I have many jobs running, with many simultaneous maps and reduces I utilize 95% of cpu and memory. Inevitably though I end up with a huge final reduce task that only uses half of of my cluster because I have reserved the other half for Mapping. Is there a way around this problem? Seems like there should also be a maximum number of reducers conditional on no Map tasks running. -JD
