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From Sandeep L <sandeepvre...@outlook.com>
Subject RE: Application errors with one disk on datanode getting filled up to 100%
Date Fri, 14 Jun 2013 11:15:13 GMT
Rahul,
In general this issue happens some times in Hadoop. There is no exact reason for this.To mitigate
this you need to run balancer in regular intervals.
Thanks,Sandeep.
Date: Fri, 14 Jun 2013 16:39:02 +0530
Subject: Re: Application errors with one disk on datanode getting filled up to 100%
From: mail2mayank@gmail.com
To: user@hadoop.apache.org

No, as of this moment we've no ideas about the reasons for that behavior. 


On Fri, Jun 14, 2013 at 4:04 PM, Rahul Bhattacharjee <rahul.rec.dgp@gmail.com> wrote:

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 <mail2mayank@gmail.com> 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 <rahul.rec.dgp@gmail.com> 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 <nitinpawar432@gmail.com> 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 <mail2mayank@gmail.com> 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 







 : 10  Dead Nodes  : 0
  Decommissioning Nodes  : 0 
 Number of Under-Replicated Blocks : 0


On Mon, Jun 10, 2013 at 3:11 PM, Nitin Pawar <nitinpawar432@gmail.com> 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 <mail2mayank@gmail.com> 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 megabytes of free space. This issue is visible
on 7 out of 10 datanodes at present.











We've some java applications which are writing to hdfs and many a times we are seeing foloowing
errors in our application logs:



java.io.IOException: All datanodes xxx.xxx.xxx.xxx:50010 are bad. Aborting...
	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:3093)
	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2200(DFSClient.java:2586)
	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2790)


I went through some old discussions and looks like manual rebalancing is what is required
in this case and we should also have dfs.datanode.du.reserved set up.

However I'd like to understand if this issue, with one disk getting filled up to 100% can
result into the issue which we are seeing in our application. 











Also, are there any other peformance implications due to some of the disks running at 100%
usage on a datanode.

-- 
Mayank Joshi

Skype: mail2mayank 
Mb.:  +91 8690625808

Blog: http://www.techynfreesouls.co.nr











PhotoStream: http://picasaweb.google.com/mail2mayank

Today is tommorrow I was so worried about yesterday ...



-- 
Nitin Pawar




-- 
Mayank Joshi

Skype: mail2mayank 
Mb.:  +91 8690625808

Blog: http://www.techynfreesouls.co.nr









PhotoStream: http://picasaweb.google.com/mail2mayank

Today is tommorrow I was so worried about yesterday ...



-- 
Nitin Pawar






-- 
Mayank Joshi

Skype: mail2mayank 
Mb.:  +91 8690625808

Blog: http://www.techynfreesouls.co.nr





PhotoStream: http://picasaweb.google.com/mail2mayank

Today is tommorrow I was so worried about yesterday ...





-- 
Mayank Joshi

Skype: mail2mayank 
Mb.:  +91 8690625808

Blog: http://www.techynfreesouls.co.nr


PhotoStream: http://picasaweb.google.com/mail2mayank

Today is tommorrow I was so worried about yesterday ...
 		 	   		  
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