Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/16030#discussion_r90694615
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -189,7 +189,15 @@ case class DataSource(
throw new AnalysisException(
s"Unable to infer schema for $format. It must be specified
manually.")
}
- (dataSchema, partitionSchema)
+
+ // Override the fields of the partition schema if the data schema has
the same field
+ val resolvedPartitionSchema = partitionSchema.map { partitionField =>
--- End diff --
I don't think we need this. Otherwise, the value you return when `if
(justPartitioning)` is inconsistent.
In a real world setting, as you provided in your example, for:
`case class A(a: Long, b: Int)`
if `a` is in fact a `Long` and is also the partitioning column, there will
be examples of `a` being a `Long` among the partition columns, therefore,
things should work. No-one should ever have more than 2 billion partition
columns anyway. Spark wouldn't be able to resolve that many columns all
in-memory right now anyway
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