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https://issues.apache.org/jira/browse/SPARK-10474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14943865#comment-14943865
]
Reynold Xin commented on SPARK-10474:
-------------------------------------
I'm fairly sure you are hitting a different issue.
[~nadenf] can you please create a new JIRA ticket, and put the latest
stacktrace there? Also please try setting spark.buffer.pageSize to 1MB, and
dump the stacktrace there. It would be great to be as precise as possible to
help debugging.
Thanks.
> 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.
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