I ran into the same issue. In the end I gave up and went back to 0.20 where I can specify the number of mappers and reducers per node (6 and 4 in your case). You can try increasing the memory.mb parameters which should force fewer map/reduce tasks per node, but then you won't be able to run your desired number of both kinds of tasks at the same time. If you find a solution please let the list know!
Jeff > -----Original Message----- > From: Trevor Robinson [mailto:tre...@scurrilous.com] > Sent: Tuesday, May 29, 2012 2:34 PM > To: mapreduce-user@hadoop.apache.org > Subject: Idle nodes with terasort and MRv2/YARN (0.23.1) > > Hello, > > I'm trying to tune terasort on a small cluster (4 identical slave > nodes w/ 4 disks and 16GB RAM each), but I'm having problems with very > uneven load. > > For teragen, I specify 24 mappers, but for some reason, only 2 nodes > out of 4 run them all, even though the web UI (for both YARN and HDFS) > shows all 4 nodes available. Similarly, I specify 16 reducers for > terasort, but the reducers seem to run on 3 nodes out of 4. Do I have > something configured wrong, or does the scheduler not attempt to > spread out the load? In addition to performing sub-optimally, this > also causes me to run out of disk space for large jobs, since the data > is not being spread out evenly. > > Currently, I'm using these settings (not shown as XML for brevity): > > yarn-site.xml: > yarn.nodemanager.resource.memory-mb=13824 > > mapred-site.xml: > mapreduce.map.memory.mb=768 > mapreduce.map.java.opts=-Xmx512M > mapreduce.reduce.memory.mb=2304 > mapreduce.reduce.java.opts=-Xmx2048M > mapreduce.task.io.sort.mb=512 > > In case it's significant, I've scripted the cluster setup and terasort > jobs, so everything runs back-to-back instantly, except that I poll to > ensure that HDFS is up and has active data nodes before running > teragen. I've also tried adding delays, but they didn't seem to have > any effect, so I don't *think* it's a start-up race issue. > > Thanks for any advice, > Trevor
