Hi Every one, I am running a simple map-red application similar to k-means. But, when I ran it in on single machine, it went fine with out any issues. But, when I ran the same on a hadoop cluster of 9 machines. It fails saying java.io.IOException: All datanodes are bad. Aborting...
Here is more explanation about the problem: I tried to upgrade my hadoop cluster to hadoop-17. During this process, I made a mistake of not installing hadoop on all machines. So, the upgrade failed. Nor I was able to roll back. So, I re-formatted the name node afresh. and then hadoop installation was successful. Later, when I ran my map-reduce job, it ran successfully,but the same job with zero reduce tasks is failing with the error as: java.io.IOException: All datanodes are bad. Aborting... When I looked into the data nodes, I figured out that file system is 100% full with different directories of name "subdir" in hadoop-username/dfs/data/current directory. I am wondering where I went wrong. Can some one please help me on this? The same job went fine on a single machine with same amount of input data. Thanks -- View this message in context: http://www.nabble.com/java.io.IOException%3A-All-datanodes-are-bad.-Aborting...-tp18006296p18006296.html Sent from the Hadoop core-user mailing list archive at Nabble.com.
