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Posted to issues@spark.apache.org by "Michael Armbrust (JIRA)" <ji...@apache.org> on 2015/08/14 02:28:45 UTC
[jira] [Updated] (SPARK-9627) SQL job failed if the dataframe with
string columns is cached
[ https://issues.apache.org/jira/browse/SPARK-9627?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Armbrust updated SPARK-9627:
------------------------------------
Summary: SQL job failed if the dataframe with string columns is cached (was: SQL job failed if the dataframe is cached)
> SQL job failed if the dataframe with string columns is cached
> -------------------------------------------------------------
>
> Key: SPARK-9627
> URL: https://issues.apache.org/jira/browse/SPARK-9627
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Davies Liu
> Assignee: Cheng Lian
> Priority: Blocker
>
> {code}
> r = random.Random()
> def gen(i):
> d = date.today() - timedelta(r.randint(0, 5000))
> cat = str(r.randint(0, 20)) * 5
> c = r.randint(0, 1000)
> price = decimal.Decimal(r.randint(0, 100000)) / 100
> return (d, cat, c, price)
> schema = StructType().add('date', DateType()).add('cat', StringType()).add('count', ShortType()).add('price', DecimalType(5, 2))
> #df = sqlContext.createDataFrame(sc.range(1<<24).map(gen), schema)
> #df.show()
> #df.write.parquet('sales4')
> df = sqlContext.read.parquet('sales4')
> df.cache()
> df.count()
> df.show()
> print df.schema
> raw_input()
> r = df.groupBy(df.date, df.cat).agg(sum(df['count'] * df.price))
> print r.explain(True)
> r.show()
> {code}
> {code}
> StructType(List(StructField(date,DateType,true),StructField(cat,StringType,true),StructField(count,ShortType,true),StructField(price,DecimalType(5,2),true)))
> == Parsed Logical Plan ==
> 'Aggregate [date#0,cat#1], [date#0,cat#1,sum((count#2 * price#3)) AS sum((count * price))#70]
> Relation[date#0,cat#1,count#2,price#3] org.apache.spark.sql.parquet.ParquetRelation@5ec8f315
> == Analyzed Logical Plan ==
> date: date, cat: string, sum((count * price)): decimal(21,2)
> Aggregate [date#0,cat#1], [date#0,cat#1,sum((change_decimal_precision(CAST(CAST(count#2, DecimalType(5,0)), DecimalType(11,2))) * change_decimal_precision(CAST(price#3, DecimalType(11,2))))) AS sum((count * price))#70]
> Relation[date#0,cat#1,count#2,price#3] org.apache.spark.sql.parquet.ParquetRelation@5ec8f315
> == Optimized Logical Plan ==
> Aggregate [date#0,cat#1], [date#0,cat#1,sum((change_decimal_precision(CAST(CAST(count#2, DecimalType(5,0)), DecimalType(11,2))) * change_decimal_precision(CAST(price#3, DecimalType(11,2))))) AS sum((count * price))#70]
> InMemoryRelation [date#0,cat#1,count#2,price#3], true, 10000, StorageLevel(true, true, false, true, 1), (PhysicalRDD [date#0,cat#1,count#2,price#3], MapPartitionsRDD[3] at), None
> == Physical Plan ==
> NewAggregate with SortBasedAggregationIterator List(date#0, cat#1) ArrayBuffer((sum((change_decimal_precision(CAST(CAST(count#2, DecimalType(5,0)), DecimalType(11,2))) * change_decimal_precision(CAST(price#3, DecimalType(11,2)))))2,mode=Final,isDistinct=false))
> TungstenSort [date#0 ASC,cat#1 ASC], false, 0
> ConvertToUnsafe
> Exchange hashpartitioning(date#0,cat#1)
> NewAggregate with SortBasedAggregationIterator List(date#0, cat#1) ArrayBuffer((sum((change_decimal_precision(CAST(CAST(count#2, DecimalType(5,0)), DecimalType(11,2))) * change_decimal_precision(CAST(price#3, DecimalType(11,2)))))2,mode=Partial,isDistinct=false))
> TungstenSort [date#0 ASC,cat#1 ASC], false, 0
> ConvertToUnsafe
> InMemoryColumnarTableScan [date#0,cat#1,count#2,price#3], (InMemoryRelation [date#0,cat#1,count#2,price#3], true, 10000, StorageLevel(true, true, false, true, 1), (PhysicalRDD [date#0,cat#1,count#2,price#3], MapPartitionsRDD[3] at), None)
> Code Generation: true
> == RDD ==
> None
> 15/08/04 23:21:53 ERROR TaskSetManager: Task 0 in stage 4.0 failed 1 times; aborting job
> Traceback (most recent call last):
> File "t.py", line 34, in <module>
> r.show()
> File "/Users/davies/work/spark/python/pyspark/sql/dataframe.py", line 258, in show
> print(self._jdf.showString(n, truncate))
> File "/Users/davies/work/spark/python/lib/py4j/java_gateway.py", line 538, in __call__
> self.target_id, self.name)
> File "/Users/davies/work/spark/python/pyspark/sql/utils.py", line 36, in deco
> return f(*a, **kw)
> File "/Users/davies/work/spark/python/lib/py4j/protocol.py", line 300, in get_return_value
> format(target_id, '.', name), value)
> py4j.protocol.Py4JJavaError: An error occurred while calling o36.showString.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0 (TID 10, localhost): java.lang.UnsupportedOperationException: tail of empty list
> at scala.collection.immutable.Nil$.tail(List.scala:339)
> at scala.collection.immutable.Nil$.tail(List.scala:334)
> at scala.reflect.internal.SymbolTable.popPhase(SymbolTable.scala:172)
> at scala.reflect.internal.Symbols$Symbol.typeParams(Symbols.scala:1491)
> at scala.reflect.internal.Types$NoArgsTypeRef.typeParams(Types.scala:2144)
> at scala.reflect.internal.Types$TypeRef.initializedTypeParams(Types.scala:2408)
> at scala.reflect.internal.Types$TypeRef.typeParamsMatchArgs(Types.scala:2409)
> at scala.reflect.internal.Types$AliasTypeRef$class.dealias(Types.scala:2232)
> at scala.reflect.internal.Types$TypeRef$$anon$3.dealias(Types.scala:2539)
> at scala.reflect.runtime.JavaMirrors$JavaMirror.typeToJavaClass(JavaMirrors.scala:1256)
> at scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:202)
> at scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:65)
> at org.apache.spark.sql.columnar.compression.DictionaryEncoding$Decoder.<init>(compressionSchemes.scala:277)
> at org.apache.spark.sql.columnar.compression.DictionaryEncoding$.decoder(compressionSchemes.scala:185)
> at org.apache.spark.sql.columnar.compression.DictionaryEncoding$.decoder(compressionSchemes.scala:177)
> at org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor$class.initialize(CompressibleColumnAccessor.scala:31)
> at org.apache.spark.sql.columnar.NativeColumnAccessor.initialize(ColumnAccessor.scala:64)
> at org.apache.spark.sql.columnar.ColumnAccessor$class.$init$(ColumnAccessor.scala:33)
> at org.apache.spark.sql.columnar.BasicColumnAccessor.<init>(ColumnAccessor.scala:44)
> at org.apache.spark.sql.columnar.NativeColumnAccessor.<init>(ColumnAccessor.scala:64)
> at org.apache.spark.sql.columnar.StringColumnAccessor.<init>(ColumnAccessor.scala:92)
> at org.apache.spark.sql.columnar.ColumnAccessor$.apply(ColumnAccessor.scala:130)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anonfun$15.apply(InMemoryColumnarTableScan.scala:300)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anonfun$15.apply(InMemoryColumnarTableScan.scala:299)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14.apply(InMemoryColumnarTableScan.scala:299)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14.apply(InMemoryColumnarTableScan.scala:297)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:173)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:146)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:126)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$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}
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