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Naden Franciscus edited comment on SPARK-10474 at 10/1/15 1:30 PM: ------------------------------------------------------------------- We are executing 20 Spark SQL jobs in parallel using Spark Job Server and hitting this issue pretty routinely. 40GB heaps x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> 0.1 with no difference. I am using the HEAD of the Spark 1.5 branch so it definitely includes all the commits above. Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106) at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68) at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82) at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33) at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278) at scala.collection.immutable.List.foreach(List.scala:318) at scala.collection.TraversableLike$class.collect(TraversableLike.scala:278) at scala.collection.AbstractTraversable.collect(Traversable.scala:105) at org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82) was (Author: nadenf): We are executing 20 Spark SQL jobs in parallel using Spark Job Server and hitting this issue pretty routinely. 40GB heaps x 6 nodes. Have tried adjusting shuffle.memoryFraction from 0.2 -> 0.1 with no difference. Caused by: java.io.IOException: Unable to acquire 16777216 bytes of memory at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138) at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106) at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68) at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169) at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83) at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82) at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33) at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278) at scala.collection.immutable.List.foreach(List.scala:318) at scala.collection.TraversableLike$class.collect(TraversableLike.scala:278) at scala.collection.AbstractTraversable.collect(Traversable.scala:105) at org.apache.spark.rdd.ZippedPartitionsBaseRDD.tryPrepareParents(ZippedPartitionsRDD.scala:82) > TungstenAggregation cannot acquire memory for pointer array after switching > to sort-based > ----------------------------------------------------------------------------------------- > > Key: SPARK-10474 > URL: https://issues.apache.org/jira/browse/SPARK-10474 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.5.0 > Reporter: Yi Zhou > Assignee: Andrew Or > Priority: Blocker > Fix For: 1.5.1, 1.6.0 > > > In aggregation case, a Lost task happened with below error. > {code} > java.io.IOException: Could not acquire 65536 bytes of memory > at > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.initializeForWriting(UnsafeExternalSorter.java:169) > at > org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill(UnsafeExternalSorter.java:220) > at > org.apache.spark.sql.execution.UnsafeKVExternalSorter.<init>(UnsafeKVExternalSorter.java:126) > at > org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap.destructAndCreateExternalSorter(UnsafeFixedWidthAggregationMap.java:257) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:435) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119) > at > org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119) > at > org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:88) > at > org.apache.spark.executor.Executor$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) > {code} > Key SQL Query > {code:sql} > INSERT INTO TABLE test_table > SELECT > ss.ss_customer_sk AS cid, > count(CASE WHEN i.i_class_id=1 THEN 1 ELSE NULL END) AS id1, > count(CASE WHEN i.i_class_id=3 THEN 1 ELSE NULL END) AS id3, > count(CASE WHEN i.i_class_id=5 THEN 1 ELSE NULL END) AS id5, > count(CASE WHEN i.i_class_id=7 THEN 1 ELSE NULL END) AS id7, > count(CASE WHEN i.i_class_id=9 THEN 1 ELSE NULL END) AS id9, > count(CASE WHEN i.i_class_id=11 THEN 1 ELSE NULL END) AS id11, > count(CASE WHEN i.i_class_id=13 THEN 1 ELSE NULL END) AS id13, > count(CASE WHEN i.i_class_id=15 THEN 1 ELSE NULL END) AS id15, > count(CASE WHEN i.i_class_id=2 THEN 1 ELSE NULL END) AS id2, > count(CASE WHEN i.i_class_id=4 THEN 1 ELSE NULL END) AS id4, > count(CASE WHEN i.i_class_id=6 THEN 1 ELSE NULL END) AS id6, > count(CASE WHEN i.i_class_id=8 THEN 1 ELSE NULL END) AS id8, > count(CASE WHEN i.i_class_id=10 THEN 1 ELSE NULL END) AS id10, > count(CASE WHEN i.i_class_id=14 THEN 1 ELSE NULL END) AS id14, > count(CASE WHEN i.i_class_id=16 THEN 1 ELSE NULL END) AS id16 > FROM store_sales ss > INNER JOIN item i ON ss.ss_item_sk = i.i_item_sk > WHERE i.i_category IN ('Books') > AND ss.ss_customer_sk IS NOT NULL > GROUP BY ss.ss_customer_sk > HAVING count(ss.ss_item_sk) > 5 > {code} > Note: > the store_sales is a big fact table and item is a small dimension table. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org