Thanks Chris for pointing out the issue. I think I was able to get over this issue by:
- repartitioning to increase the number of partitions (about 6k partitions) - apply sort() on the resulting dataframe to coalesce into single sorted partition file - read the sorted file and then adding just limit() to get the desired number of rows seem to have worked Thanks everyone for the input! On Tue, May 10, 2016 at 1:20 AM, Christophe Préaud < christophe.pre...@kelkoo.com> wrote: > Hi, > > You may be hitting this bug: SPARK-9879 > <https://issues.apache.org/jira/browse/SPARK-9879> > > In other words: did you try without the LIMIT clause? > > Regards, > Christophe. > > > On 02/05/16 20:02, Gourav Sengupta wrote: > > Hi, > > I have worked on 300GB data by querying it from CSV (using SPARK CSV) > and writing it to Parquet format and then querying parquet format to query > it and partition the data and write out individual csv files without any > issues on a single node SPARK cluster installation. > > Are you trying to cache in the entire data? What is that you are trying to > achieve in your used case? > > Regards, > Gourav > > On Mon, May 2, 2016 at 5:59 PM, Ted Yu <yuzhih...@gmail.com> wrote: > >> That's my interpretation. >> >> On Mon, May 2, 2016 at 9:45 AM, Buntu Dev < <buntu...@gmail.com> >> buntu...@gmail.com> wrote: >> >>> Thanks Ted, I thought the avg. block size was already low and less than >>> the usual 128mb. If I need to reduce it further via parquet.block.size, it >>> would mean an increase in the number of blocks and that should increase the >>> number of tasks/executors. Is that the correct way to interpret this? >>> >>> On Mon, May 2, 2016 at 6:21 AM, Ted Yu < <yuzhih...@gmail.com> >>> yuzhih...@gmail.com> wrote: >>> >>>> Please consider decreasing block size. >>>> >>>> Thanks >>>> >>>> > On May 1, 2016, at 9:19 PM, Buntu Dev < <buntu...@gmail.com> >>>> buntu...@gmail.com> wrote: >>>> > >>>> > I got a 10g limitation on the executors and operating on parquet >>>> dataset with block size 70M with 200 blocks. I keep hitting the memory >>>> limits when doing a 'select * from t1 order by c1 limit 1000000' (ie, 1M). >>>> It works if I limit to say 100k. What are the options to save a large >>>> dataset without running into memory issues? >>>> > >>>> > Thanks! >>>> >>> >>> >> > > > ------------------------------ > Kelkoo SAS > Société par Actions Simplifiée > Au capital de € 4.168.964,30 > Siège social : 158 Ter Rue du Temple 75003 Paris > 425 093 069 RCS Paris > > Ce message et les pièces jointes sont confidentiels et établis à > l'attention exclusive de leurs destinataires. Si vous n'êtes pas le > destinataire de ce message, merci de le détruire et d'en avertir > l'expéditeur. >
