Hi,
Thanks Andrew and Daniel for the response.
Setting spark.shuffle.spill to false didnt make any difference. 5 days
completed in 6 min and 10 days was stuck after around 1hr.
Daniel,in my current use case I cant read all the files to a single RDD.But I
have another use case where I did it in that way,ie I read all the files to a
single RDD and joined with with the RDD of 9 million rows and it worked fine
and took only 3 minutes.
Thanks & Regards,
Meethu M
On Wednesday, 18 June 2014 12:11 AM, Daniel Darabos
<daniel.dara...@lynxanalytics.com> wrote:
I've been wondering about this. Is there a difference in performance between
these two?
valrdd1 =sc.textFile(files.mkString(","))valrdd2
=sc.union(files.map(sc.textFile(_)))
I don't know about your use-case, Meethu, but it may be worth trying to see if
reading all the files into one RDD (like rdd1) would perform better in the
join. (If this is possible in your situation.)
On Tue, Jun 17, 2014 at 6:45 PM, Andrew Or <and...@databricks.com> wrote:
How long does it get stuck for? This is a common sign for the OS thrashing due
to out of memory exceptions. If you keep it running longer, does it throw an
error?
>
>
>Depending on how large your other RDD is (and your join operation), memory
>pressure may or may not be the problem at all. It could be that spilling your
>shuffles
>to disk is slowing you down (but probably shouldn't hang your application).
>For the 5 RDDs case, what happens if you set spark.shuffle.spill to false?
>
>
>
>2014-06-17 5:59 GMT-07:00 MEETHU MATHEW <meethu2...@yahoo.co.in>:
>
>
>
>>
>> Hi all,
>>
>>
>>I want to do a recursive leftOuterJoin between an RDD (created from file)
>>with 9 million rows(size of the file is 100MB) and 30 other RDDs(created from
>>30 diff files in each iteration of a loop) varying from 1 to 6 million rows.
>>When I run it for 5 RDDs,its running successfully in 5 minutes.But when I
>>increase it to 10 or 30 RDDs its gradually slowing down and finally getting
>>stuck without showing any warning or error.
>>
>>
>>I am running in standalone mode with 2 workers of 4GB each and a total of 16
>>cores .
>>
>>
>>Any of you facing similar problems with JOIN or is it a problem with my
>>configuration.
>>
>>
>>Thanks & Regards,
>>Meethu M
>
