Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/13269#discussion_r65492813
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -1884,10 +1884,62 @@ class Analyzer(
} else {
inputAttributes
}
- val unbound = deserializer transform {
- case b: BoundReference => inputs(b.ordinal)
+
+ validateTupleColumns(deserializer, inputs)
+ val ordinalResolved = deserializer transform {
+ case GetColumnByOrdinal(ordinal, _) => inputs(ordinal)
+ }
+ val attrResolved = resolveExpression(
+ ordinalResolved, LocalRelation(inputs), throws = true)
+ validateInnerTupleFields(attrResolved)
+ attrResolved
+ }
+ }
+
+ private def fail(schema: StructType, maxOrdinal: Int): Unit = {
+ throw new AnalysisException(s"Try to map ${schema.simpleString} to
Tuple${maxOrdinal + 1}, " +
+ "but failed as the number of fields does not line up.")
+ }
+
+ /**
+ * For each Tuple field, we use [[GetColumnByOrdinal]] to get its
corresponding column by
+ * position. However, the actual number of columns may be different
from the number of Tuple
+ * fields. This method is used to check the number of columns and
fields, and throw an
+ * exception if they do not match.
+ */
+ private def validateTupleColumns(deserializer: Expression, inputs:
Seq[Attribute]): Unit = {
+ var maxOrdinal = -1
+ deserializer.foreach {
+ case GetColumnByOrdinal(ordinal, _) => if (ordinal > maxOrdinal)
maxOrdinal = ordinal
+ case _ =>
+ }
+ if (maxOrdinal >= 0 && maxOrdinal != inputs.length - 1) {
+ fail(inputs.toStructType, maxOrdinal)
+ }
--- End diff --
Actually we should also check that each ordinal from 0 to `inputs.length -
1` appears in deserializer expression:
```scala
val ordinals = deserializer.collect {
case GetColumnByOrdinal(ordinal, _) => ordinal
}.distinct.sorted
if (ordinals.nonEmpty && ordinals != (0 until inputs.length)) {
fail(inputs.toStructType, ordinals.max)
}
```
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