Hi Mori Bellamy,
 I did this twice.  and still the same problem is persisting. I don't know
how to solve this issue. If any one know the answer, please let me know.

Thanks

Mori Bellamy wrote:
> 
> That's bizarre. I'm not sure why your DFS would have magically gotten  
> full. Whenever hadoop gives me trouble, i try the following sequence  
> of commands
> 
> stop-all.sh
> rm -Rf /path/to/my/hadoop/dfs/data
> hadoop namenode -format
> start-all.sh
> 
> maybe you would get some luck if you ran that on all of the machines?  
> (of course, don't run it if you don't want to lose all of that "data")
> On Jun 19, 2008, at 4:32 AM, novice user wrote:
> 
>>
>> 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
>>
>>
>>
>> -- 
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>>
> 
> 
> 

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