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From viirya <...@git.apache.org>
Subject [GitHub] spark pull request #18779: [SPARK-21580][SQL]Integers in aggregation express...
Date Tue, 01 Aug 2017 13:55:02 GMT
Github user viirya commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18779#discussion_r130613115
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
    @@ -1010,7 +1014,16 @@ class Analyzer(
                     s"(valid range is [1, ${aggs.size}])")
               case o => o
             }
    -        Aggregate(newGroups, aggs, child)
    +        // If an aggregateExpression is integer, after replaced with this aggregateExpression,
the
    +        // groupExpression will be considered as an ordinal still, so we should filter
it.
    +        var filterGroups: Seq[Expression] = Nil
    +        newGroups.foreach { ng =>
    +          ng match {
    +            case a: Alias => if (!isIntLiteral (a.child)) filterGroups :+= ng
    +            case _ => filterGroups :+= ng
    +          }
    +        }
    +        Aggregate(filterGroups, aggs, child)
    --- End diff --
    
    @10110346 Please see previous comment: https://github.com/apache/spark/pull/18779#issuecomment-319007812
    
    The basic idea is, instead of resolve `UnresolvedOrdinal` to actual aggregate expressions,
we resolve it to `ResolvedOrdinal`.
    
    Then in the beginning of optimization, we replace all `ResolvedOrdinal` to actual aggregate
expressions.
    
    What do you think?


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