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Hadoop >> mail # user >> Re: Application errors with one disk on datanode getting filled up to 100%


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Re: Application errors with one disk on datanode getting filled up to 100%
Thanks Mayank, Any clue on why was only one disk was getting all writes.

Rahul
On Thu, Jun 13, 2013 at 11:47 AM, Mayank <[EMAIL PROTECTED]> wrote:

> So we did a manual rebalance (followed instructions at:
> http://wiki.apache.org/hadoop/FAQ#On_an_individual_data_node.2C_how_do_you_balance_the_blocks_on_the_disk.3F)
> and also reserved 30 GB of space for non dfs usage via
> dfs.datanode.du.reserved and restarted our apps.
>
> Things have been going fine till now.
>
> Keeping fingers crossed :)
>
>
> On Wed, Jun 12, 2013 at 12:58 PM, Rahul Bhattacharjee <
> [EMAIL PROTECTED]> wrote:
>
>> I have a few points to make , these may not be very helpful for the said
>> problem.
>>
>> +All data nodes are bad exception is kind of not pointing to the problem
>> related to disk space full.
>> +hadoop.tmp.dir acts as base location of other hadoop related properties
>> , not sure if any particular directory is created specifically.
>> +Only one disk getting filled looks strange.The other disk are part while
>> formatting the NN.
>>
>> Would be interesting to know the reason for this.
>> Please keep posted.
>>
>> Thanks,
>> Rahul
>>
>>
>> On Mon, Jun 10, 2013 at 3:39 PM, Nitin Pawar <[EMAIL PROTECTED]>wrote:
>>
>>> From the snapshot, you got around 3TB for writing data.
>>>
>>> Can you check individual datanode's storage health.
>>> As you said you got 80 servers writing parallely to hdfs, I am not sure
>>> can that be an issue.
>>> As suggested in past threads, you can do a rebalance of the blocks but
>>> that will take some time to finish and will not solve your issue right
>>> away.
>>>
>>> You can wait for others to reply. I am sure there will be far better
>>> solutions from experts for this.
>>>
>>>
>>> On Mon, Jun 10, 2013 at 3:18 PM, Mayank <[EMAIL PROTECTED]> wrote:
>>>
>>>> No it's not a map-reduce job. We've a java app running on around 80
>>>> machines which writes to hdfs. The error that I'd mentioned is being thrown
>>>> by the application and yes we've replication factor set to 3 and following
>>>> is status of hdfs:
>>>>
>>>> Configured Capacity : 16.15 TB DFS Used : 11.84 TB Non DFS Used :872.66 GB DFS
>>>> Remaining : 3.46 TB DFS Used% : 73.3 % DFS Remaining% : 21.42 % Live
>>>> Nodes<http://hmaster.production.indix.tv:50070/dfsnodelist.jsp?whatNodes=LIVE> :10 Dead
>>>> Nodes<http://hmaster.production.indix.tv:50070/dfsnodelist.jsp?whatNodes=DEAD>
>>>> : 0  Decommissioning Nodes<http://hmaster.production.indix.tv:50070/dfsnodelist.jsp?whatNodes=DECOMMISSIONING>
>>>> : 0 Number of Under-Replicated Blocks : 0
>>>>
>>>>
>>>> On Mon, Jun 10, 2013 at 3:11 PM, Nitin Pawar <[EMAIL PROTECTED]>wrote:
>>>>
>>>>> when you say application errors out .. does that mean your mapreduce
>>>>> job is erroring? In that case apart from hdfs space you will need to look
>>>>> at mapred tmp directory space as well.
>>>>>
>>>>> you got 400GB * 4 * 10 = 16TB of disk and lets assume that you have a
>>>>> replication factor of 3 so at max you will have datasize of 5TB with you.
>>>>> I am also assuming you are not scheduling your program to run on
>>>>> entire 5TB with just 10 nodes.
>>>>>
>>>>> i suspect your clusters mapred tmp space is getting filled in while
>>>>> the job is running.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Mon, Jun 10, 2013 at 3:06 PM, Mayank <[EMAIL PROTECTED]> wrote:
>>>>>
>>>>>> We are running a hadoop cluster with 10 datanodes and a namenode.
>>>>>> Each datanode is setup with 4 disks (/data1, /data2, /data3, /data4), which
>>>>>> each disk having a capacity 414GB.
>>>>>>
>>>>>>
>>>>>> hdfs-site.xml has following property set:
>>>>>>
>>>>>> <property>
>>>>>>         <name>dfs.data.dir</name>
>>>>>>
>>>>>> <value>/data1/hadoopfs,/data2/hadoopfs,/data3/hadoopfs,/data4/hadoopfs</value>
>>>>>>         <description>Data dirs for DFS.</description>
>>>>>> </property>
>>>>>>
>>>>>> Now we are facing a issue where in we find /data1 getting filled up
>>>>>> quickly and many a times we see it's usage running at 100% with just few