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https://issues.apache.org/jira/browse/SPARK-21550?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
peay closed SPARK-21550.
------------------------
Resolution: Duplicate
Fix Version/s: 2.2.0
> 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
> 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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