tomvanbussel commented on code in PR #38979:
URL: https://github.com/apache/spark/pull/38979#discussion_r1044406797
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/SparkConnectPlanner.scala:
##########
@@ -380,7 +396,26 @@ class SparkConnectPlanner(session: SparkSession) {
}
val attributes = structType.toAttributes
val proj = UnsafeProjection.create(attributes, attributes)
- new logical.LocalRelation(attributes, rows.map(r => proj(r).copy()).toSeq)
+ val relation = logical.LocalRelation(attributes, rows.map(r =>
proj(r).copy()).toSeq)
+
+ if (rel.hasDatatype || rel.hasDatatypeStr) {
+ // rename columns and update datatypes
+ val schema = if (rel.hasDatatype) {
+ DataTypeProtoConverter
+ .toCatalystType(rel.getDatatype)
+ .asInstanceOf[StructType]
+ } else {
+ parseDatatypeString(rel.getDatatypeStr)
+ .asInstanceOf[StructType]
+ }
+ Dataset
+ .ofRows(session, logicalPlan = relation)
+ .toDF(schema.names: _*)
Review Comment:
Hmmm, this is going to more difficult than I anticipated. We will have to
check if the inferred type and the provided type are compatible, and then we'll
have to use something similar to `EvaluatePython.makeFromJava` to perform the
conversion. Let's do this in a follow-up :)
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
