On 09/05/2015 11:22 AM, Reynold Xin wrote: > Try increase the shuffle memory fraction (by default it is only 16%). > Again, if you run Spark 1.5, this will probably run a lot faster, > especially if you increase the shuffle memory fraction ... Hi Reynold,
Does the 1.5 has better join/cogroup performance for RDD case too or only for SQL. - Gurvinder > > On Tue, Sep 1, 2015 at 8:13 AM, Thomas Dudziak <tom...@gmail.com > <mailto:tom...@gmail.com>> wrote: > > While it works with sort-merge-join, it takes about 12h to finish > (with 10000 shuffle partitions). My hunch is that the reason for > that is this: > > INFO ExternalSorter: Thread 3733 spilling in-memory map of 174.9 MB > to disk (62 times so far) > > (and lots more where this comes from). > > On Sat, Aug 29, 2015 at 7:17 PM, Reynold Xin <r...@databricks.com > <mailto:r...@databricks.com>> wrote: > > Can you try 1.5? This should work much, much better in 1.5 out > of the box. > > For 1.4, I think you'd want to turn on sort-merge-join, which is > off by default. However, the sort-merge join in 1.4 can still > trigger a lot of garbage, making it slower. SMJ performance is > probably 5x - 1000x better in 1.5 for your case. > > > On Thu, Aug 27, 2015 at 6:03 PM, Thomas Dudziak > <tom...@gmail.com <mailto:tom...@gmail.com>> wrote: > > I'm getting errors like "Removing executor with no recent > heartbeats" & "Missing an output location for shuffle" > errors for a large SparkSql join (1bn rows/2.5TB joined with > 1bn rows/30GB) and I'm not sure how to configure the job to > avoid them. > > The initial stage completes fine with some 30k tasks on a > cluster with 70 machines/10TB memory, generating about 6.5TB > of shuffle writes, but then the shuffle stage first waits > 30min in the scheduling phase according to the UI, and then > dies with the mentioned errors. > > I can see in the GC logs that the executors reach their > memory limits (32g per executor, 2 workers per machine) and > can't allocate any more stuff in the heap. Fwiw, the top 10 > in the memory use histogram are: > > num #instances #bytes class name > ---------------------------------------------- > 1: 249139595 11958700560 > scala.collection.immutable.HashMap$HashMap1 > 2: 251085327 8034730464 <tel:8034730464> > scala.Tuple2 > 3: 243694737 5848673688 java.lang.Float > 4: 231198778 5548770672 java.lang.Integer > 5: 72191585 4298521576 > [Lscala.collection.immutable.HashMap; > 6: 72191582 2310130624 > scala.collection.immutable.HashMap$HashTrieMap > 7: 74114058 1778737392 java.lang.Long > 8: 6059103 779203840 [Ljava.lang.Object; > 9: 5461096 174755072 > scala.collection.mutable.ArrayBuffer > 10: 34749 70122104 [B > > Relevant settings are (Spark 1.4.1, Java 8 with G1 GC): > > spark.core.connection.ack.wait.timeout 600 > spark.executor.heartbeatInterval 60s > spark.executor.memory 32g > spark.mesos.coarse false > spark.network.timeout 600s > spark.shuffle.blockTransferService netty > spark.shuffle.consolidateFiles true > spark.shuffle.file.buffer 1m > spark.shuffle.io.maxRetries 6 > spark.shuffle.manager sort > > The join is currently configured with > spark.sql.shuffle.partitions=1000 but that doesn't seem to > help. Would increasing the partitions help ? Is there a > formula to determine an approximate partitions number value > for a join ? > Any help with this job would be appreciated ! > > cheers, > Tom > > > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
