Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/10750#discussion_r49680909
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
---
@@ -480,10 +480,120 @@ private[sql] object DataSourceStrategy extends
Strategy with Logging {
case expressions.Contains(a: Attribute, Literal(v: UTF8String,
StringType)) =>
Some(sources.StringContains(a.name, v.toString))
+ case expressions.BinaryComparison(BinaryArithmetic(left, right),
Literal(v, t)) =>
+ translateArithemiticOPFilter (predicate)
+ case expressions.BinaryComparison(Literal(v, t),
BinaryArithmetic(left, right)) =>
+ translateArithemiticOPFilter (predicate)
+
case _ => None
}
}
+ private def translateArithemiticOPFilter(predicate: Expression):
Option[Filter] = {
+ predicate match {
+ case expressions.EqualTo(Add(left, right), Literal(v, t)) =>
+ Some(sources.ArithmeticOPEqualTo(Add(left, right),
convertToScala(v, t)))
--- End diff --
I took a look of SPARK-10195. Looks like it deals with the issue of
exposing internal data types. It uses `convertToScala` to convert these
internal data types to scala version. Since here `convertToScala` is used to
convert the values. I think it should not be the same problem.
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