cloud-fan commented on a change in pull request #29045:
URL: https://github.com/apache/spark/pull/29045#discussion_r454396181
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
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@@ -116,47 +116,53 @@ object OrcUtils extends Logging {
}
/**
- * Returns the requested column ids from the given ORC file. Column id can
be -1, which means the
- * requested column doesn't exist in the ORC file. Returns None if the given
ORC file is empty.
+ * @return Returns the requested column ids from the given ORC file and
Boolean flag to use actual
+ * schema or result schema. Column id can be -1, which means the requested
column doesn't
+ * exist in the ORC file. Returns None if the given ORC file is empty.
*/
def requestedColumnIds(
isCaseSensitive: Boolean,
dataSchema: StructType,
requiredSchema: StructType,
reader: Reader,
- conf: Configuration): Option[Array[Int]] = {
+ conf: Configuration): (Option[Array[Int]], Boolean) = {
+ var sendActualSchema = false
val orcFieldNames = reader.getSchema.getFieldNames.asScala
Review comment:
following your previous explanation in
https://github.com/apache/spark/pull/29045#discussion_r454234413
1. the code is creating a wrap VectorizedRowBatchWrap using the result
schema which is `<d_year, int>`
2. `orcVectorWrappers[i] = new OrcColumnVector(dt,
wrap.batch().cols[colId]);` accesses the 6th column of the orc batch.
Why it doesn't fail when the physical orc file schema matches the table
schema in metastore?
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