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?
Thanks
src/contrib/hbase/src/java/org/apache/hadoop/hbase/rest/ScannerHandler.java
public void doDelete(HttpServletRequest request, HttpServletResponse
response,
String[] pathSegments)
throws ServletException, IOException {
doMethodNotAllowed(response, "Not hooked back up yet!");
}
"Bryan Duxbury" <[EMAIL PROTECTED]> wrote in
message news:[EMAIL PROTECTED]
> 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" <[EMAIL PROTECTED]> wrote in message
>> news:[EMAIL PROTECTED]
>>> 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" <[EMAIL PROTECTED]> wrote
>>>> in message news:[EMAIL PROTECTED]
>>>>
>>>>> 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.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>
>
