srielau commented on code in PR #58549:
URL: https://github.com/apache/spark/pull/58549#discussion_r3951662998
##########
python/pyspark/sql/pandas/types.py:
##########
@@ -133,7 +135,7 @@ def to_arrow_type(
arrow_type = pa.float64()
elif isinstance(dt, DecimalType):
arrow_type = pa.decimal128(dt.precision, dt.scale)
- elif isinstance(dt, StringType):
+ elif isinstance(dt, (StringType, CharType, VarcharType)):
Review Comment:
Follow-up in b4582cf0532: the same recursive Arrow UDTF return-type check
now runs in Spark Connect for both invocation and registration, including
parsed DDL schemas. This also keeps the shared classic/Connect parity test
valid.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvalPythonEvaluatorFactory.scala:
##########
@@ -36,6 +38,16 @@ abstract class EvalPythonEvaluatorFactory(
output: Seq[Attribute])
extends PartitionEvaluatorFactory[InternalRow, InternalRow] {
+ private val applyCharVarcharChecks =
+ CharVarcharUtils.shouldApplyWriteSideLengthCheck(SQLConf.get)
+ private val checkedOutput = if (applyCharVarcharChecks) {
+ childOutput ++ output.drop(childOutput.length).map { attr =>
Review Comment:
Follow-up in b4582cf0532: moved the `useArrow=True` chained-UDF execution
regression into the pandas/pyarrow-gated Arrow suite. The base UDF suite now
tests only the pickled path, while the plan-shape suite continues to verify
both Batch and Arrow executor splitting and legacy fusion.
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