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https://issues.apache.org/jira/browse/SPARK-10507?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14952747#comment-14952747
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Adrian Wang commented on SPARK-10507:
-------------------------------------
It seems this bug has been fixed long long ago. I just checked 1.5.0 and
there's no such problem.
> reject temporal expressions such as timestamp - timestamp at parse time
> ------------------------------------------------------------------------
>
> Key: SPARK-10507
> URL: https://issues.apache.org/jira/browse/SPARK-10507
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.3.1
> Reporter: N Campbell
> Priority: Minor
>
> TIMESTAMP - TIMESTAMP in ISO-SQL should return an interval type which SPARK
> does not support..
> A similar expression in Hive 0.13 fails with Error: Could not create
> ResultSet: Required field 'type' is unset!
> Struct:TPrimitiveTypeEntry(type:null) and SPARK has similar "challenges".
> While Hive 1.2.1 has added some interval type support it is far from complete
> with respect to ISO-SQL.
> The ability to compute the period of time (years, days, weeks, hours, ...)
> between timestamps or add/substract intervals from a timestamp are extremely
> common in business applications.
> Currently, a value expression such as select timestampcol - timestampcol from
> t will fail during execution and not parse time. While the error thrown
> states that fact, it would better for those value expressions to be rejected
> at parse time along with indicating the expression that is causing the parser
> error.
> Operation: execute
> Errors:
> {code}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 6214.0 failed 4 times, most recent failure: Lost task 0.3 in stage
> 6214.0 (TID 21208, sandbox.hortonworks.com): java.lang.RuntimeException: Type
> TimestampType does not support numeric operations
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.catalyst.expressions.Subtract.numeric$lzycompute(arithmetic.scala:138)
> at
> org.apache.spark.sql.catalyst.expressions.Subtract.numeric(arithmetic.scala:136)
> at
> org.apache.spark.sql.catalyst.expressions.Subtract.eval(arithmetic.scala:150)
> at
> org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:113)
> at
> org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:68)
> at
> org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:52)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:813)
> at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:813)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1498)
> {code}
> {code}
> create table if not exists TTS ( RNUM int , CTS timestamp )TERMINATED BY
> '\n'
> STORED AS orc ;
> {code}
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