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Posted to issues@spark.apache.org by "Hans van den Bogert (JIRA)" <ji...@apache.org> on 2015/10/06 23:25:27 UTC
[jira] [Issue Comment Deleted] (SPARK-10474) TungstenAggregation
cannot acquire memory for pointer array after switching to sort-based
[ https://issues.apache.org/jira/browse/SPARK-10474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hans van den Bogert updated SPARK-10474:
----------------------------------------
Comment: was deleted
(was: One more debug println for the calculated cores (in contrast to numCores):
https://gist.github.com/hansbogert/cc2baf3995d4e37270a2
Relevant output (output is the same for fine-grained as well as coarse-grained mesos):
{noformat}
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.5.1
/_/
Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0)
Type in expressions to have them evaluated.
Type :help for more information.
15/10/06 10:25:04 WARN SparkConf: In Spark 1.0 and later spark.local.dir will be overridden by the value set by the cluster manager (via SPARK_LOCAL_DIRS in mesos/standalone and LOCAL_DIRS in YARN).
numCores:0
cores:12
1048576
15/10/06 10:25:05 WARN MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
...
{noformat}
The calculated 'cores' is 12, which the amount of cores of the local driver node, however the total mesos cluster has more than 40 cores. Either way, there is no difference between fine-grained and coarse grained mode, for this method at least.)
> 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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