Interesting. After experimenting with various parameters increasing spark.sql.shuffle.partitions and decreasing spark.buffer.pageSize helped my job go through. BTW I will be happy to help getting this issue fixed.
Nezih On Tue, Mar 22, 2016 at 1:07 AM james <[email protected]> wrote: Hi, > I also found 'Unable to acquire memory' issue using Spark 1.6.1 with > Dynamic > allocation on YARN. My case happened with setting > spark.sql.shuffle.partitions larger than 200. From error stack, it has a > diff with issue reported by Nezih and not sure if these has same root > cause. > > Thanks > James > > 16/03/17 16:02:11 INFO spark.MapOutputTrackerMaster: Size of output > statuses > for shuffle 0 is 1912805 bytes > 16/03/17 16:02:12 INFO spark.MapOutputTrackerMasterEndpoint: Asked to send > map output locations for shuffle 1 to hw-node3:55062 > 16/03/17 16:02:12 INFO spark.MapOutputTrackerMaster: Size of output > statuses > for shuffle 0 is 1912805 bytes > 16/03/17 16:02:16 INFO scheduler.TaskSetManager: Starting task 280.0 in > stage 153.0 (TID 9390, hw-node5, partition 280,PROCESS_LOCAL, 2432 bytes) > 16/03/17 16:02:16 WARN scheduler.TaskSetManager: Lost task 170.0 in stage > 153.0 (TID 9280, hw-node5): java.lang.OutOfMemoryError: Unable to acquire > 1073741824 bytes of memory, got 1060110796 > at > > org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:91) > at > > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.growPointerArrayIfNecessary(UnsafeExternalSorter.java:295) > at > > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:330) > at > > org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91) > at > > org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168) > at > org.apache.spark.sql.execution.Sort$anonfun$1.apply(Sort.scala:90) > at > org.apache.spark.sql.execution.Sort$anonfun$1.apply(Sort.scala:64) > at > > org.apache.spark.rdd.RDD$anonfun$mapPartitionsInternal$1$anonfun$apply$21.apply(RDD.scala:728) > at > > org.apache.spark.rdd.RDD$anonfun$mapPartitionsInternal$1$anonfun$apply$21.apply(RDD.scala:728) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at > > org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:89) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) > at > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > > > > -- > View this message in context: > http://apache-spark-developers-list.1001551.n3.nabble.com/java-lang-OutOfMemoryError-Unable-to-acquire-bytes-of-memory-tp16773p16787.html > Sent from the Apache Spark Developers List mailing list archive at > Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] > >
