cloud-fan commented on code in PR #58581:
URL: https://github.com/apache/spark/pull/58581#discussion_r3992303766
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sql/connect/common/src/main/scala/org/apache/spark/sql/connect/SparkSession.scala:
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@@ -223,7 +223,9 @@ class SparkSession private[sql] (
/** @inheritdoc */
def createDataFrame(rows: java.util.List[Row], schema: StructType):
DataFrame = {
- createDataset(RowEncoder.encoderFor(schema),
rows.iterator().asScala).toDF()
+ // The client cannot observe the server's CHAR/VARCHAR configuration.
Encode an explicitly
+ // provided schema independently of the client's local configuration, as
for result schemas.
+ createDataset(RowEncoder.encoderForResultSchema(schema),
rows.iterator().asScala).toDF()
Review Comment:
The empty/non-empty reconciliation is now shared for standard and default
semantics, but the legacy-as-string configuration is still bypassed:
encoderForResultSchema pads CHAR values client-side, and the server rebuilds
the relation with the original CHAR/VARCHAR attributes. Please also honor and
cover spark.sql.legacy.charVarcharAsString=true.
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