Hi Deepak Not 100% sure , but please try increasing (--executor-cores ) to twice the number of your physical cores on your machine.
Thanks and Regards Aniruddh On Tue, Jul 14, 2015 at 9:49 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote: > Its been 30 minutes and still the partitioner has not completed yet, its > ever. > > Without repartition, i see this error > https://issues.apache.org/jira/browse/SPARK-5928 > > > FetchFailed(BlockManagerId(1, imran-2.ent.cloudera.com, 55028), shuffleId=1, > mapId=0, reduceId=0, message= > org.apache.spark.shuffle.FetchFailedException: Adjusted frame length exceeds > 2147483647: 3021252889 - discarded > at > org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67) > at > org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83) > at > org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83) > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) > at > org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) > > > > > On Mon, Jul 13, 2015 at 8:34 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> > wrote: > >> I have 100 MB of Avro data. and i do repartition(307) is taking forever. >> >> 2. val x = input.repartition(7907).map( {k1,k2,k3,k4}, {inputRecord} ) >> 3. val quantiles = x.map( {k1,k2,k3,k4}, TDigest(inputRecord).asBytes >> ).reduceByKey() [ This was groupBy earlier ] >> 4. x.join(quantiles).coalesce(100).writeInAvro >> >> >> Attached is full Scala code. >> >> I have 340 Yarn node cluster with 14G Ram on each node and have input >> data of just just 100 MB. (Hadoop takes 2.5 hours on 1 TB dataset) >> >> >> ./bin/spark-submit -v --master yarn-cluster --jars >> /apache/hadoop-2.4.1-2.1.3.0-2-EBAY/share/hadoop/hdfs/hadoop-hdfs-2.4.1-EBAY-2.jar,/home/dvasthimal/spark1.4/lib/spark_reporting_dep_only-1.0-SNAPSHOT.jar >> --num-executors 330 --driver-memory 14g --driver-java-options >> "-XX:MaxPermSize=512M -Xmx4096M -Xms4096M -verbose:gc -XX:+PrintGCDetails >> -XX:+PrintGCTimeStamps" --executor-memory 14g --executor-cores 1 --queue >> hdmi-others --class com.ebay.ep.poc.spark.reporting.SparkApp >> /home/dvasthimal/spark1.4/lib/spark_reporting-1.0-SNAPSHOT.jar >> startDate=2015-06-20 endDate=2015-06-21 >> input=/apps/hdmi-prod/b_um/epdatasets/exptsession subcommand=ppwmasterprime >> output=/user/dvasthimal/epdatasets/ppwmasterprime buffersize=128 >> maxbuffersize=1068 maxResultSize=200G >> >> >> I see this in stdout of the task on that executor >> >> 15/07/13 19:58:48 WARN hdfs.BlockReaderLocal: The short-circuit local reads >> feature cannot be used because libhadoop cannot be loaded. >> 15/07/13 20:00:08 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (1 time so far) >> 15/07/13 20:01:31 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (2 times so far) >> 15/07/13 20:03:07 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (3 times so far) >> 15/07/13 20:04:32 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (4 times so far) >> 15/07/13 20:06:21 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (5 times so far) >> 15/07/13 20:08:09 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (6 times so far) >> 15/07/13 20:09:51 INFO collection.ExternalSorter: Thread 47 spilling >> in-memory map of 2.2 GB to disk (7 times so far) >> >> >> >> Also attached is the thread dump >> >> >> -- >> Deepak >> >> > > > -- > Deepak > >
