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https://issues.apache.org/jira/browse/SPARK-19039?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16852732#comment-16852732
]
Junichi Koizumi commented on SPARK-19039:
-------------------------------------------
It seems to be working fine on my console .
{quote} scala> df.where(rowHasMyNumber).show()
19/05/30 18:02:52 INFO SparkContext: Starting job: show at <console>:41
19/05/30 18:02:52 INFO DAGScheduler: Got job 0 (show at <console>:41) with 1
output pa rtitions
19/05/30 18:02:52 INFO DAGScheduler: Final stage: ResultStage 0 (show at
<console>:41)
19/05/30 18:02:52 INFO DAGScheduler: Parents of final stage: List()
19/05/30 18:02:52 INFO DAGScheduler: Missing parents: List()
19/05/30 18:02:52 INFO DAGScheduler: Submitting ResultStage 0
(MapPartitionsRDD[10] at show at <console>:41), which has no missing parents
19/05/30 18:02:52 INFO MemoryStore: Block broadcast_2 stored as values in
memory (estimated size 11.1 KB, free 11.1 KB)
19/05/30 18:02:52 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes
in memory (estimated size 4.9 KB, free 16.0 KB)
19/05/30 18:02:52 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on
localhost:50755 (size: 4.9 KB, free: 511.1 MB)
19/05/30 18:02:52 INFO SparkContext: Created broadcast 2 from broadcast at
DAGSchedule r.scala:1008
19/05/30 18:02:52 INFO DAGScheduler: Submitting 1 missing tasks from
ResultStage 0 (Ma pPartitionsRDD[10] at show at <console>:41)
19/05/30 18:02:52 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
19/05/30 18:02:53 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0,
localhos t, partition 0,PROCESS_LOCAL, 2452 bytes)
19/05/30 18:02:53 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
19/05/30 18:02:57 INFO GeneratePredicate: Code generated in 3051.980036 ms
19/05/30 18:02:57 INFO GenerateUnsafeProjection: Code generated in 76.257507 ms
19/05/30 18:02:57 INFO GenerateSafeProjection: Code generated in 78.801312 ms
19/05/30 18:02:57 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1617
bytes result sent to driver
19/05/30 18:02:57 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0)
in 4249 ms on localhost (1/1)
19/05/30 18:02:57 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks
have all completed, from pool
19/05/30 18:02:57 INFO DAGScheduler: ResultStage 0 (show at <console>:41)
finished in 4.857 s
19/05/30 18:02:57 INFO DAGScheduler: Job 0 finished: show at <console>:41,
took 5.5939 30 s
19/05/30 18:02:58 INFO SparkContext: Starting job: show at <console>:41
19/05/30 18:02:58 INFO DAGScheduler: Got job 1 (show at <console>:41) with 1
output partitions
19/05/30 18:02:58 INFO DAGScheduler: Final stage: ResultStage 1 (show at
<console>:41)
19/05/30 18:02:58 INFO DAGScheduler: Parents of final stage: List()
19/05/30 18:02:58 INFO DAGScheduler: Missing parents: List()
19/05/30 18:02:58 INFO DAGScheduler: Submitting ResultStage 1
(MapPartitionsRDD[10] at show at <console>:41), which has no missing parents
19/05/30 18:02:58 INFO MemoryStore: Block broadcast_3 stored as values in
memory (estimated size 11.1 KB, free 27.0 KB)
19/05/30 18:02:58 INFO MemoryStore: Block broadcast_3_piece0 stored as bytes
in memory (estimated size 4.9 KB, free 31.9 KB)
19/05/30 18:02:58 INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on
localhost:50755 (size: 4.9 KB, free: 511.1 MB)
19/05/30 18:02:58 INFO SparkContext: Created broadcast 3 from broadcast at
DAGScheduler.scala:1008
19/05/30 18:02:58 INFO DAGScheduler: Submitting 1 missing tasks from
ResultStage 1 (MapPartitionsRDD[10] at show at <console>:41)
19/05/30 18:02:58 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
19/05/30 18:02:58 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1,
localhost, partition 1,PROCESS_LOCAL, 2451 bytes)
19/05/30 18:02:58 INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
19/05/30 18:02:58 INFO Executor: Finished task 0.0 in stage 1.0 (TID 1). 1574
bytes result sent to driver
19/05/30 18:02:58 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1)
in 42 ms on localhost (1/1)
19/05/30 18:02:58 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks
have all completed, from pool
19/05/30 18:02:58 INFO DAGScheduler: ResultStage 1 (show at <console>:41)
finished in 0.044 s
19/05/30 18:02:58 INFO DAGScheduler: Job 1 finished: show at <console>:41,
took 0.0870 83 s
+------+--+
{quote}
|a|b|
+------+--+
|hi|1|
|there|2|
|the|3|
+------+--+
> UDF ClosureCleaner bug when UDF, col applied in paste mode in REPL
> ------------------------------------------------------------------
>
> Key: SPARK-19039
> URL: https://issues.apache.org/jira/browse/SPARK-19039
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.3.0
> Reporter: Joseph K. Bradley
> Priority: Major
>
> When I try this:
> * Define UDF
> * Apply UDF to get Column
> * Use Column in a DataFrame
> I can find weird behavior in the spark-shell when using paste mode.
> To reproduce this, paste this into the spark-shell:
> {code}
> import org.apache.spark.sql.functions._
> val df = spark.createDataFrame(Seq(
> ("hi", 1),
> ("there", 2),
> ("the", 3),
> ("end", 4)
> )).toDF("a", "b")
> val myNumbers = Set(1,2,3)
> val tmpUDF = udf { (n: Int) => myNumbers.contains(n) }
> val rowHasMyNumber = tmpUDF($"b")
> df.where(rowHasMyNumber).show()
> {code}
> Stack trace for Spark 2.0 (similar for other versions):
> {code}
> org.apache.spark.SparkException: Task not serializable
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
> at
> org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
> at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
> at org.apache.spark.SparkContext.clean(SparkContext.scala:2057)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:817)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:816)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
> at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:816)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:364)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
> at
> org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:225)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:308)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
> at
> org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2193)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2551)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2192)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2199)
> at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1935)
> at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1934)
> at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2581)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:1934)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2149)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:526)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:486)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:495)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw.<init>(<console>:45)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw.<init>(<console>:57)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw.<init>(<console>:59)
> at linef732283eefe649f4877db916c5ad096f25.$read$$iw.<init>(<console>:61)
> at
> linef732283eefe649f4877db916c5ad096f25.$eval$.$print$lzycompute(<console>:7)
> at linef732283eefe649f4877db916c5ad096f25.$eval$.$print(<console>:6)
> Caused by: java.io.NotSerializableException: org.apache.spark.sql.Column
> Serialization stack:
> - object not serializable (class: org.apache.spark.sql.Column, value:
> UDF(b))
> - field (class:
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw, name:
> rowHasMyNumber, type: class org.apache.spark.sql.Column)
> - object (class
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw,
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw@6688375a)
> - field (class:
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw$$anonfun$1,
> name: $outer, type: class
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw)
> - object (class
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw$$anonfun$1,
> <function1>)
> - field (class:
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2, name: func$2,
> type: interface scala.Function1)
> - object (class
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2, <function1>)
> - field (class: org.apache.spark.sql.catalyst.expressions.ScalaUDF,
> name: f, type: interface scala.Function1)
> - object (class org.apache.spark.sql.catalyst.expressions.ScalaUDF,
> UDF(input[1, int, false]))
> - element of array (index: 1)
> - array (class [Ljava.lang.Object;, size 2)
> - field (class:
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8, name:
> references$1, type: class [Ljava.lang.Object;)
> - object (class
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8, <function2>)
> at
> org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:295)
> at
> org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
> at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
> at org.apache.spark.SparkContext.clean(SparkContext.scala:2057)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:817)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:816)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
> at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:816)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:364)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
> at
> org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:225)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:308)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
> at
> org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2193)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2551)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2192)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2199)
> at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1935)
> at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1934)
> at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2581)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:1934)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2149)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:526)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:486)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:495)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw$$iw.<init>(<console>:45)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw$$iw.<init>(<console>:57)
> at
> linef732283eefe649f4877db916c5ad096f25.$read$$iw$$iw.<init>(<console>:59)
> at linef732283eefe649f4877db916c5ad096f25.$read$$iw.<init>(<console>:61)
> at
> linef732283eefe649f4877db916c5ad096f25.$eval$.$print$lzycompute(<console>:7)
> at linef732283eefe649f4877db916c5ad096f25.$eval$.$print(<console>:6)
> {code}
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