I had similar problems to this (reduce side failures for large joins (25bn rows with 9bn)), and found the answer was to further up the spark.sql.shuffle.partitions=1000. In my case, 16k partitions worked for me, but your tables look a little denser, so you may want to go even higher.
On Thu, Aug 27, 2015 at 6:04 PM Thomas Dudziak <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$HashMap1 > 2: 251085327 8034730464 scala.Tuple2 > 3: 243694737 5848673688 java.lang.Float > 4: 231198778 5548770672 java.lang.Integer > 5: 72191585 4298521576 [Lscala.collection.immutable.HashMap; > 6: 72191582 2310130624 > scala.collection.immutable.HashMap$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 >
