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https://issues.apache.org/jira/browse/SPARK-10504?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen updated SPARK-10504:
-------------------------------
Affects Version/s: 1.4.1
> aggregate where NULL is defined as the value expression aborts when SUM used
> ----------------------------------------------------------------------------
>
> Key: SPARK-10504
> URL: https://issues.apache.org/jira/browse/SPARK-10504
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.3.1, 1.4.1
> Reporter: N Campbell
> Priority: Minor
>
> In ISO-SQL the context would determine an implicit type for NULL or one might
> find that a vendor requires an explicit type via CAST ( NULL as INTEGER). It
> appears that SPARK presumes a long type i.e. select min(NULL), max(NULL) but
> a query such the following aborts.
>
> {{select sum ( null ) from tversion}}
> {code}
> Operation: execute
> Errors:
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 5232.0 failed 4 times, most recent failure: Lost task 0.3 in stage
> 5232.0 (TID 18531, sandbox.hortonworks.com): scala.MatchError: NullType (of
> class org.apache.spark.sql.types.NullType$)
> at
> org.apache.spark.sql.catalyst.expressions.Cast.org$apache$spark$sql$catalyst$expressions$Cast$$cast(Cast.scala:403)
> at
> org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:422)
> at org.apache.spark.sql.catalyst.expressions.Cast.cast(Cast.scala:422)
> at org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:426)
> at
> org.apache.spark.sql.catalyst.expressions.Coalesce.eval(nullFunctions.scala:51)
> at
> org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:119)
> at
> org.apache.spark.sql.catalyst.expressions.Coalesce.eval(nullFunctions.scala:51)
> at
> org.apache.spark.sql.catalyst.expressions.MutableLiteral.update(literals.scala:82)
> at
> org.apache.spark.sql.catalyst.expressions.SumFunction.update(aggregates.scala:581)
> at
> org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$6.apply(Aggregate.scala:133)
> at
> org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$6.apply(Aggregate.scala:126)
> at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:634)
> at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:634)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:64)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
> 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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