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From "Billy" <sa...@pearsonwholesale.com>
Subject Re: hbase master heap space
Date Sat, 29 Dec 2007 23:36:59 GMT
I checked and added the delete option to my code for the scanner based on 
the api from wiki but it looks like its not working at this time basedo nthe 
code and responce I got form the rest interfase. i get a "Not hooked back up 
yet" responce any idea on when this will be fixed?



public void doDelete(HttpServletRequest request, HttpServletResponse 
String[] pathSegments)
throws ServletException, IOException {
doMethodNotAllowed(response, "Not hooked back up yet!");

"Bryan Duxbury" <bryan@rapleaf.com> wrote in 
message news:0459216F-F3F0-46C1-B7DB-57A6479BD809@rapleaf.com...
> Are you closing the scanners when you're done? If not, those might be 
> hanging around for a long time. I don't think we've built in the  proper 
> timeout logic to make that work by itself.
> -Bryan
> On Dec 21, 2007, at 5:10 PM, Billy wrote:
>> I was thanking the same thing and been running REST outside of the 
>> Master on
>> each server for about 5 hours now and used the master as a backup  if 
>> local
>> rest interface failed. You are right I seen a little faster  processing 
>> time
>> from doing this vs. using just the master.
>> Seams the problem is not with the master its self looks like REST  is 
>> using
>> up more and more memory not sure but I thank its to do with inserts 
>> maybe
>> not but the memory usage is going up I an doing a scanner 2 threads 
>> reading
>> rows and processing the data and inserting it in to a separate table
>> building a inverted index.
>> I will restart everything when this job is done and try to do just 
>> inserts
>> and see if its the scanner or inserts.
>> The master is holding at about 75mb and the rest interfaces are up  to 
>> 400MB
>> and slowly going up on the ones running the jobs.
>> I am still testing I will see what else I can come up with.
>> Billy
>> "stack" <stack@duboce.net> wrote in message
>> news:476C1AA8.3030306@duboce.net...
>>> Hey Billy:
>>> Master itself should use little memory and though it is not out of  the
>>> realm of possibiliites, it should not have a leak.
>>> Are you running with the default heap size?  You might want to  give it
>>> more memory if you are (See
>>> http://wiki.apache.org/lucene-hadoop/Hbase/FAQ#3 for how).
>>> If you are uploading all via the REST server running on the  master, the
>>> problem as you speculate, could be in the REST servlet itself  (though 
>>> it
>>> looks like it shouldn't be holding on to anything having given it a
>>> cursory glance).  You could try running the REST server  independent of 
>>> the
>>> master.  Grep for 'Starting the REST Server' in this page,
>>> http://wiki.apache.org/lucene-hadoop/Hbase/HbaseRest, for how (If  you 
>>> are
>>> only running one REST instance, your upload might go faster if you  run
>>> multiple).
>>> St.Ack
>>> Billy wrote:
>>>> I forgot to say that once restart the master only uses about 70mb of
>>>> memory
>>>> Billy
>>>> "Billy" <sales@pearsonwholesale.com> wrote
>>>> in message news:fkejpo$u8c$1@ger.gmane.org...
>>>>> I not sure of this but why does the master server use up so much 
>>>>> memory.
>>>>> I been running an script that been inserting data into a table  for a
>>>>> little over 24 hours and the master crashed because of
>>>>> java.lang.OutOfMemoryError: Java heap space.
>>>>> So my question is why does the master use up so much memory at  most

>>>>> it
>>>>> should store the -ROOT-,.META. tables in memory and block to table
>>>>> mapping.
>>>>> Is it cache or a memory leak?
>>>>> I am using the rest interface so could that be the reason?
>>>>> I inserted according to the high edit ids on all the region servers
>>>>> about
>>>>> 51,932,760 edits and the master ran out of memory with a heap of 
>>>>> about
>>>>> 1GB.
>>>>> The other side to this is the data I inserted is only taking up 
>>>>> 886.61
>>>>> MB and that's with
>>>>> dfs.replication set to 2 so half that is only 440MB of data 
>>>>> compressed
>>>>> at the block level.
>>>>> From what I understand the master should have lower memory and  cpu 
>>>>> usage
>>>>> and the namenode on hadoop should be the memory hog it has to  keep up
>>>>> with all the data about the blocks.

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