Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9305#discussion_r43421576
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
---
@@ -201,15 +200,24 @@ private[sql] object DataSourceStrategy extends
Strategy with Logging {
// Builds `AttributeReference`s for all partition columns so that we
can use them to project
// required partition columns. Note that if a partition column
appears in `requiredColumns`,
// we should use the `AttributeReference` in `requiredColumns`.
- val requiredColumnMap = requiredColumns.map(a => a.name -> a).toMap
- val partitionColumns = partitionColumnSchema.toAttributes.map { a =>
- requiredColumnMap.getOrElse(a.name, a)
+ val partitionColumns = {
+ val requiredColumnMap = requiredColumns.map(a => a.name -> a).toMap
+ partitionColumnSchema.toAttributes.map { a =>
+ requiredColumnMap.getOrElse(a.name, a)
+ }
}
val mapPartitionsFunc = (_: TaskContext, _: Int, iterator:
Iterator[InternalRow]) => {
- val projection = UnsafeProjection.create(requiredColumns,
dataColumns ++ partitionColumns)
+ // Note that we can't use an `UnsafeRowJoiner` to replace the
following `JoinedRow` and
+ // `UnsafeProjection`. Because the projection may also adjust
column order.
--- End diff --
I guess the problem is that the expected column lists can be `col1,
partCol1, col2, partCol2`. So, we need a projection anyway.
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