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Posted to issues@spark.apache.org by "Javier (JIRA)" <ji...@apache.org> on 2018/02/23 10:58:00 UTC
[jira] [Commented] (SPARK-21550) approxQuantiles throws "next on
empty iterator" on empty data
[ https://issues.apache.org/jira/browse/SPARK-21550?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16374218#comment-16374218 ]
Javier commented on SPARK-21550:
--------------------------------
I still observe this behavior in 2.2.0 when approxQuantile is applied to a single column that only contains None's:
{{}}
{code:java}
...
File "/mnt/yarn/usercache/hadoop/appcache/application_1519290822117_0043/container_1519290822117_0043_01_000001/pyspark.zip/pyspark/sql/dataframe.py", line 1402, in approxQuantile
File "/mnt/yarn/usercache/hadoop/appcache/application_1519290822117_0043/container_1519290822117_0043_01_000001/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in _call_
File "/mnt/yarn/usercache/hadoop/appcache/application_1519290822117_0043/container_1519290822117_0043_01_000001/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/mnt/yarn/usercache/hadoop/appcache/application_1519290822117_0043/container_1519290822117_0043_01_000001/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o202.approxQuantile.
: java.util.NoSuchElementException: next on empty iterator
at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
at scala.collection.IterableLike$class.head(IterableLike.scala:107)
at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$$super$head(ArrayOps.scala:186)
at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
at scala.collection.mutable.ArrayOps$ofRef.head(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.last(TraversableLike.scala:431)
at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$$super$last(ArrayOps.scala:186)
at scala.collection.IndexedSeqOptimized$class.last(IndexedSeqOptimized.scala:132)
at scala.collection.mutable.ArrayOps$ofRef.last(ArrayOps.scala:186)
at org.apache.spark.sql.catalyst.util.QuantileSummaries.query(QuantileSummaries.scala:207)
at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply$mcDD$sp(StatFunctions.scala:92)
at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply(StatFunctions.scala:92)
at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply(StatFunctions.scala:92)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1.apply(StatFunctions.scala:92)
at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1.apply(StatFunctions.scala:92)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.sql.execution.stat.StatFunctions$.multipleApproxQuantiles(StatFunctions.scala:92)
at org.apache.spark.sql.DataFrameStatFunctions.approxQuantile(DataFrameStatFunctions.scala:73)
at org.apache.spark.sql.DataFrameStatFunctions.approxQuantile(DataFrameStatFunctions.scala:84)
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:748){code}
> approxQuantiles throws "next on empty iterator" on empty data
> -------------------------------------------------------------
>
> Key: SPARK-21550
> URL: https://issues.apache.org/jira/browse/SPARK-21550
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: peay
> Priority: Major
> Fix For: 2.2.0
>
>
> The documentation says:
> {code}
> null and NaN values will be removed from the numerical column before calculation. If
> the dataframe is empty or the column only contains null or NaN, an empty array is returned.
> {code}
> However, this small pyspark example
> {code}
> sql_context.range(10).filter(col("id") == 42).approxQuantile("id", [0.99], 0.001)
> {code}
> throws
> {code}
> Py4JJavaError: An error occurred while calling o3493.approxQuantile.
> : java.util.NoSuchElementException: next on empty iterator
> at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
> at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
> at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
> at scala.collection.IterableLike$class.head(IterableLike.scala:107)
> at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$$super$head(ArrayOps.scala:186)
> at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
> at scala.collection.mutable.ArrayOps$ofRef.head(ArrayOps.scala:186)
> at scala.collection.TraversableLike$class.last(TraversableLike.scala:431)
> at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$$super$last(ArrayOps.scala:186)
> at scala.collection.IndexedSeqOptimized$class.last(IndexedSeqOptimized.scala:132)
> at scala.collection.mutable.ArrayOps$ofRef.last(ArrayOps.scala:186)
> at org.apache.spark.sql.catalyst.util.QuantileSummaries.query(QuantileSummaries.scala:207)
> at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply$mcDD$sp(StatFunctions.scala:92)
> at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply(StatFunctions.scala:92)
> at org.apache.spark.sql.execution.stat.StatFunctions$$anonfun$multipleApproxQuantiles$1$$anonfun$apply$1.apply(StatFunctions.scala:92)
> {code}
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