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https://issues.apache.org/jira/browse/SPARK-10474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14944166#comment-14944166
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Hans van den Bogert edited comment on SPARK-10474 at 10/5/15 10:39 PM:
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Had this patch against tag 1.5.1:
https://gist.github.com/f110f64887f4739b7dd8
Output of the added println()'s are near the beginning in:
{noformat}
Using Spark's repl log4j profile:
org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
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 00:16:00 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
1048576
15/10/06 00:16:02 WARN MetricsSystem: Using default name DAGScheduler for
source because spark.app.id is not set.
I1006 00:16:02.414851 25123 sched.cpp:137] Version: 0.21.0
I1006 00:16:02.423246 25115 sched.cpp:234] New master detected at
master@10.149.3.5:5050
I1006 00:16:02.423482 25115 sched.cpp:242] No credentials provided. Attempting
to register without authentication
I1006 00:16:02.427250 25121 sched.cpp:408] Framework registered with
20151006-001500-84120842-5050-6847-0000
15/10/06 00:16:02 WARN NativeCodeLoader: Unable to load native-hadoop library
for your platform... using builtin-java classes where applicable
Spark context available as sc.
15/10/06 00:16:04 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:04 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:09 WARN ObjectStore: Version information not found in metastore.
hive.metastore.schema.verification is not enabled so recording the schema
version 1.2.0
15/10/06 00:16:09 WARN ObjectStore: Failed to get database default, returning
NoSuchObjectException
15/10/06 00:16:11 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:11 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:14 WARN NativeCodeLoader: Unable to load native-hadoop library
for your platform... using builtin-java classes where applicable
SQL context available as sqlContext.
{noformat}
was (Author: hbogert):
Had this patch against tag 1.5.1:
https://gist.github.com/f110f64887f4739b7dd8.git
Output of the added println()'s are near the beginning in:
{noformat}
Using Spark's repl log4j profile:
org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
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 00:16:00 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
1048576
15/10/06 00:16:02 WARN MetricsSystem: Using default name DAGScheduler for
source because spark.app.id is not set.
I1006 00:16:02.414851 25123 sched.cpp:137] Version: 0.21.0
I1006 00:16:02.423246 25115 sched.cpp:234] New master detected at
master@10.149.3.5:5050
I1006 00:16:02.423482 25115 sched.cpp:242] No credentials provided. Attempting
to register without authentication
I1006 00:16:02.427250 25121 sched.cpp:408] Framework registered with
20151006-001500-84120842-5050-6847-0000
15/10/06 00:16:02 WARN NativeCodeLoader: Unable to load native-hadoop library
for your platform... using builtin-java classes where applicable
Spark context available as sc.
15/10/06 00:16:04 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:04 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:09 WARN ObjectStore: Version information not found in metastore.
hive.metastore.schema.verification is not enabled so recording the schema
version 1.2.0
15/10/06 00:16:09 WARN ObjectStore: Failed to get database default, returning
NoSuchObjectException
15/10/06 00:16:11 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:11 WARN Connection: BoneCP specified but not present in
CLASSPATH (or one of dependencies)
15/10/06 00:16:14 WARN NativeCodeLoader: Unable to load native-hadoop library
for your platform... using builtin-java classes where applicable
SQL context available as sqlContext.
{noformat}
> 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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