[GitHub] spark pull request #13585: [SPARK-15859][SQL] Optimize the partition pruning...

[chenghao-intel <gi...@git.apache.org>]

Github user chenghao-intel commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13585#discussion_r72184424
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala ---
    @@ -92,6 +92,36 @@ object PhysicalOperation extends PredicateHelper {
               .map(Alias(_, a.name)(a.exprId, a.qualifier, isGenerated = a.isGenerated)).getOrElse(a)
         }
       }
    +
    +  /**
    +   * Drop the non-partition key expression from the given expression, to optimize the
    +   * partition pruning. For instances: (We assume part1 & part2 are the partition keys):
    +   * (part1 == 1 and a > 3) or (part2 == 2 and a < 5)  ==> (part1 == 1 or part1 == 2)
    +   * (part1 == 1 and a > 3) or (a < 100) => None
    +   * (a > 100 && b < 100) or (part1 = 10) => None
    +   * (a > 100 && b < 100 and part1 = 10) or (part1 == 2) => (part1 = 10 or part1 == 2)
    +   * @param predicate The given expression
    +   * @param partitionKeyIds partition keys in attribute set
    +   * @return
    +   */
    +  def extractPartitionKeyExpression(
    +    predicate: Expression, partitionKeyIds: AttributeSet): Option[Expression] = {
    +    // drop the non-partition key expression in conjunction of the expression tree
    +    val additionalPartPredicate = predicate transformUp {
    --- End diff --
    
    This PR may have critical bugs, when user implements a UDF which logically like the `NOT` operator in the partition filter expression. Probably we need a white list the built-in UDFs.
    
    @yhuai @liancheng @yangw1234 @clockfly any comments on this?


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