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https://issues.apache.org/jira/browse/SPARK-22249?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16216634#comment-16216634
]
Andreas Maier commented on SPARK-22249:
---------------------------------------
Thank you for solving this issue so quickly. Not every open source project is
reacting so fast.
> UnsupportedOperationException: empty.reduceLeft when caching a dataframe
> ------------------------------------------------------------------------
>
> Key: SPARK-22249
> URL: https://issues.apache.org/jira/browse/SPARK-22249
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.1, 2.2.0
> Environment: $ uname -a
> Darwin MAC-UM-024.local 16.7.0 Darwin Kernel Version 16.7.0: Thu Jun 15
> 17:36:27 PDT 2017; root:xnu-3789.70.16~2/RELEASE_X86_64 x86_64
> $ pyspark --version
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /___/ .__/\_,_/_/ /_/\_\ version 2.2.0
> /_/
>
> Using Scala version 2.11.8, Java HotSpot(TM) 64-Bit Server VM, 1.8.0_92
> Branch
> Compiled by user jenkins on 2017-06-30T22:58:04Z
> Revision
> Url
> Reporter: Andreas Maier
> Assignee: Marco Gaido
> Fix For: 2.2.1, 2.3.0
>
>
> It seems that the {{isin()}} method with an empty list as argument only
> works, if the dataframe is not cached. If it is cached, it results in an
> exception. To reproduce
> {code:java}
> $ pyspark
> >>> df = spark.createDataFrame([pyspark.Row(KEY="value")])
> >>> df.where(df["KEY"].isin([])).show()
> +---+
> |KEY|
> +---+
> +---+
> >>> df.cache()
> DataFrame[KEY: string]
> >>> df.where(df["KEY"].isin([])).show()
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/usr/local/anaconda3/envs/<myenv>/lib/python3.6/site-packages/pyspark/sql/dataframe.py",
> line 336, in show
> print(self._jdf.showString(n, 20))
> File
> "/usr/local/anaconda3/envs/<myenv>/lib/python3.6/site-packages/pyspark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py",
> line 1133, in __call__
> File
> "/usr/local/anaconda3/envs/<myenv>/lib/python3.6/site-packages/pyspark/sql/utils.py",
> line 63, in deco
> return f(*a, **kw)
> File
> "/usr/local/anaconda3/envs/<myenv>/lib/python3.6/site-packages/pyspark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py",
> line 319, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o302.showString.
> : java.lang.UnsupportedOperationException: empty.reduceLeft
> at
> scala.collection.TraversableOnce$class.reduceLeft(TraversableOnce.scala:180)
> at
> scala.collection.mutable.ArrayBuffer.scala$collection$IndexedSeqOptimized$$super$reduceLeft(ArrayBuffer.scala:48)
> at
> scala.collection.IndexedSeqOptimized$class.reduceLeft(IndexedSeqOptimized.scala:74)
> at scala.collection.mutable.ArrayBuffer.reduceLeft(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.reduce(TraversableOnce.scala:208)
> at scala.collection.AbstractTraversable.reduce(Traversable.scala:104)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$1.applyOrElse(InMemoryTableScanExec.scala:107)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$1.applyOrElse(InMemoryTableScanExec.scala:71)
> at scala.PartialFunction$Lifted.apply(PartialFunction.scala:223)
> at scala.PartialFunction$Lifted.apply(PartialFunction.scala:219)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$2.apply(InMemoryTableScanExec.scala:112)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$2.apply(InMemoryTableScanExec.scala:111)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
> at scala.collection.immutable.List.flatMap(List.scala:344)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec.<init>(InMemoryTableScanExec.scala:111)
> at
> org.apache.spark.sql.execution.SparkStrategies$InMemoryScans$$anonfun$3.apply(SparkStrategies.scala:307)
> at
> org.apache.spark.sql.execution.SparkStrategies$InMemoryScans$$anonfun$3.apply(SparkStrategies.scala:307)
> at
> org.apache.spark.sql.execution.SparkPlanner.pruneFilterProject(SparkPlanner.scala:99)
> at
> org.apache.spark.sql.execution.SparkStrategies$InMemoryScans$.apply(SparkStrategies.scala:303)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
> at
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
> at
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> at
> scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
> at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2832)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2153)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2366)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:245)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
> {code}
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