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From "Cheng Lian (JIRA)" <>
Subject [jira] [Created] (SPARK-6444) Sum expression should remain unresolved if the data type isn't a numeric type
Date Sat, 21 Mar 2015 04:04:38 GMT
Cheng Lian created SPARK-6444:

             Summary: Sum expression should remain unresolved if the data type isn't a numeric
                 Key: SPARK-6444
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 1.2.1, 1.1.1, 1.0.2, 1.3.0
            Reporter: Cheng Lian
            Assignee: Cheng Lian

Spark shell session for reproducing this issue:
import sqlContext._

        c1 STRING,
        c2 STRING

        SELECT SUM(c1) AS c3, 0 AS c4 FROM ut     -- (1)
        UNION ALL
        SELECT 0 AS c3, COUNT(c2) AS c4 FROM ut   -- (2)
    ) t
Exception thrown:
java.util.NoSuchElementException: key not found: c3#10
        at scala.collection.MapLike$class.default(MapLike.scala:228)
        at org.apache.spark.sql.catalyst.expressions.AttributeMap.default(AttributeMap.scala:29)
        at scala.collection.MapLike$class.apply(MapLike.scala:141)
        at org.apache.spark.sql.catalyst.expressions.AttributeMap.apply(AttributeMap.scala:29)
        at org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$1.applyOrElse(Optimizer.scala:80)
        at org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$1.applyOrElse(Optimizer.scala:79)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:187)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:187)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:50)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:186)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:177)
        at org.apache.spark.sql.catalyst.optimizer.UnionPushdown$.pushToRight(Optimizer.scala:79)
        at org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$apply$1$$anonfun$applyOrElse$6.apply(Optimizer.scala:101)
        at org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$apply$1$$anonfun$applyOrElse$6.apply(Optimizer.scala:101)
The analyzed plan of the query is:
== Analyzed Logical Plan ==
!Aggregate [], [SUM(CAST(c3#153, DoubleType)) AS _c0#157]                   (c)
  Project [CAST(c3#153, StringType) AS c3#164,c4#163L]                      (d)
   Project [c3#153,CAST(c4#154, LongType) AS c4#163L]
    Aggregate [], [SUM(CAST(c1#158, DoubleType)) AS c3#153,0 AS c4#154]     (b)
     MetastoreRelation default, ut, None
  Project [CAST(c3#155, StringType) AS c3#162,c4#156L]                      (a)
   Aggregate [], [0 AS c3#155,COUNT(c2#161) AS c4#156L]
    MetastoreRelation default, ut, None
This case is very interesting. It involves 2 analysis rules, {{WidenTypes}} and {{PropagateStrings}},
and 1 optimizer rule, {{UnionPushdown}}. To see the details, we can turn on TRACE level log
and check detailed rule execution process. The TL;DR is:
# Since {{c1}} is STRING, {{SUM(c1)}} is also STRING (which is the root cause of the whole
# {{c3}} in {{(1)}} is STRING, while the one in {{(2)}} is INT. Thus {{WidenTypes}} casts
the latter to STRING to ensure both sides of the UNION have the same schema.  See {{(a)}}.
# {{PropagateStrings}} casts {{c1}} in {{SUM(c1)}} to DOUBLE, which consequently changes data
type of {{SUM(c1)}} and {{c3}} to DOUBLE.  See {{(b)}}.
# {{c3}} in the top level {{Aggregate}} is resolved as DOUBLE (c)
# Since schemas of the two sides of the UNION are different again, {{WidenTypes}} casts {{SUM(c1)
AS c3}} to STRING.  See {{(d)}}.
# The top level {{Aggregate}} becomes unresolved since {{c3#153}} is hidden by {{(d)}} now.
# In the optimizing phase, {{UnionPushdown}} throws because the top level {{Aggregate}} is

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