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From kai-zeng <...@git.apache.org>
Subject [GitHub] spark pull request: [SQL] Checking data types when resolving types
Date Thu, 19 Mar 2015 17:46:46 GMT
Github user kai-zeng commented on a diff in the pull request:

    https://github.com/apache/spark/pull/4685#discussion_r26779326
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
---
    @@ -18,21 +18,28 @@
     package org.apache.spark.sql.catalyst.expressions
     
     import org.apache.spark.sql.catalyst.analysis.UnresolvedException
    -import org.apache.spark.sql.catalyst.errors.TreeNodeException
     import org.apache.spark.sql.types._
     
     case class UnaryMinus(child: Expression) extends UnaryExpression {
       type EvaluatedType = Any
     
    +  override lazy val resolved = child.resolved &&
    +    (child.dataType.isInstanceOf[NumericType] || child.dataType.isInstanceOf[NullType])
    +
       def dataType = child.dataType
       override def foldable = child.foldable
       def nullable = child.nullable
       override def toString = s"-$child"
     
    -  lazy val numeric = dataType match {
    -    case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]]
    -    case other => sys.error(s"Type $other does not support numeric operations")
    -  }
    +  val numeric =
    +    if (resolved) {
    +      dataType match {
    +        case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]]
    +        case n: NullType => UnresolvedNumeric
    +      }
    +    } else {
    +      UnresolvedNumeric
    +    }
    --- End diff --
    
    This is merely to avoid using lazy val, as accessing lazy val has an overhead compared
to accessing plain val. 
    But we cannot materialize numeric if the expression is not resolved


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