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https://issues.apache.org/jira/browse/SPARK-7937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14651232#comment-14651232
]
Apache Spark commented on SPARK-7937:
-------------------------------------
User 'rxin' has created a pull request for this issue:
https://github.com/apache/spark/pull/7877
> Cannot compare Hive named_struct. (when using argmax, argmin)
> -------------------------------------------------------------
>
> Key: SPARK-7937
> URL: https://issues.apache.org/jira/browse/SPARK-7937
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.0
> Reporter: Jianshi Huang
>
> Imagine the following SQL:
> Intention: get last used bank account country.
>
> {code:sql}
> select bank_account_id,
> max(named_struct(
> 'src_row_update_ts', unix_timestamp(src_row_update_ts,'yyyy/M/D
> HH:mm:ss'),
> 'bank_country', bank_country)).bank_country
> from bank_account_monthly
> where year_month='201502'
> group by bank_account_id
> {code}
> =>
> {noformat}
> Error: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 94 in stage 96.0 failed 4 times, most recent failure: Lost task 94.3 in
> stage 96.0 (TID 22281, xxxx): java.lang.RuntimeException: Type
> StructType(StructField(src_row_update_ts,LongType,true),
> StructField(bank_country,StringType,true)) does not support ordered operations
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.catalyst.expressions.LessThan.ordering$lzycompute(predicates.scala:222)
> at
> org.apache.spark.sql.catalyst.expressions.LessThan.ordering(predicates.scala:215)
> at
> org.apache.spark.sql.catalyst.expressions.LessThan.eval(predicates.scala:235)
> at
> org.apache.spark.sql.catalyst.expressions.MaxFunction.update(aggregates.scala:147)
> at
> org.apache.spark.sql.execution.Aggregate$$anonfun$doExecute$1$$anonfun$7.apply(Aggregate.scala:165)
> at
> org.apache.spark.sql.execution.Aggregate$$anonfun$doExecute$1$$anonfun$7.apply(Aggregate.scala:149)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:686)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:686)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:70)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:70)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> 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:724)
> {noformat}
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