viirya commented on code in PR #6130:
URL: https://github.com/apache/datafusion-comet/pull/6130#discussion_r4107073304


##########
spark/src/main/spark-4.1+/org/apache/spark/sql/comet/CometArrowEvalPythonExec.scala:
##########
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.spark.sql.comet
+
+import scala.jdk.CollectionConverters._
+
+import org.apache.spark.api.python.PythonEvalType
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, 
Expression, NamedArgumentExpression, NamedExpression, PythonUDF}
+import org.apache.spark.sql.execution.{PartitioningPreservingUnaryExecNode, 
SparkPlan}
+import org.apache.spark.sql.execution.python.ArrowEvalPythonExec
+import org.apache.spark.sql.types.{BinaryType, BooleanType, ByteType, 
DataType, DateType, DecimalType, DoubleType, FloatType, IntegerType, LongType, 
ShortType, StringType, TimestampNTZType}
+
+import com.google.protobuf.ByteString
+
+import org.apache.comet.{CometConf, ConfigEntry, NativeBase}
+import org.apache.comet.CometSparkSessionExtensions.withFallbackReason
+import org.apache.comet.serde.{CometOperatorSerde, Compatible, 
OperatorOuterClass, QueryPlanSerde, SupportLevel, Unsupported}
+import org.apache.comet.serde.OperatorOuterClass.Operator
+
+/** Native execution for Spark 4.1+ scalar `@arrow_udf` functions. */
+object CometArrowEvalPythonExec extends 
CometOperatorSerde[ArrowEvalPythonExec] {
+
+  // SparkContext adds this entry even when the user has not configured a 
Python
+  // environment. Keep other overrides on Spark's worker path.
+  private def hasUnsupportedEnvironment(env: java.util.Map[String, String]): 
Boolean =
+    env != null && env.asScala.exists { case (key, value) =>
+      key != "PYTHONHASHSEED" || value != "0"
+    }
+
+  private def hasCompatibleArrowSchema(dataType: DataType): Boolean = dataType 
match {
+    case _: BooleanType | _: ByteType | _: ShortType | _: IntegerType | _: 
LongType |
+        _: FloatType | _: DoubleType | _: BinaryType | _: DateType | _: 
DecimalType |
+        _: TimestampNTZType =>
+      true
+    // Spark's Arrow conversion accepts plain strings. Collated and 
constrained strings
+    // may carry semantics that are not represented by Comet's Utf8 Arrow type.
+    case s: StringType if s == StringType => true
+    case _ => false
+  }
+
+  override def enabledConfig: Option[ConfigEntry[Boolean]] =
+    Some(CometConf.COMET_NATIVE_ARROW_PYTHON_UDF_ENABLED)
+
+  override def getSupportLevel(op: ArrowEvalPythonExec): SupportLevel = {
+    if (!NativeBase.supportsPythonUdf()) {
+      return Unsupported(Some("Native library lacks the python-udf feature"))
+    }
+    if (op.evalType != PythonEvalType.SQL_SCALAR_ARROW_UDF) {
+      return Unsupported(Some("Only scalar @arrow_udf is supported"))
+    }
+    if (op.udfs.isEmpty || op.udfs.length != op.resultAttrs.length) {
+      return Unsupported(Some("Arrow UDF functions and result attributes do 
not match"))
+    }
+    if (op.conf.arrowUseLargeVarTypes) {
+      return Unsupported(Some("Arrow UDF large variable types are not 
supported in-process"))
+    }
+    if (op.conf.pythonUDFProfiler.nonEmpty) {
+      return Unsupported(Some("Arrow UDF profiling is not supported 
in-process"))
+    }
+    if (op.udfs.exists(_.children.exists(expr => 
!hasCompatibleArrowSchema(expr.dataType))) ||
+      op.resultAttrs.exists(attr => !hasCompatibleArrowSchema(attr.dataType))) 
{
+      return Unsupported(Some("Arrow UDF type is outside the verified native 
Arrow schema set"))
+    }
+    op.udfs.collectFirst {
+      case udf if udf.func.broadcastVars != null && 
!udf.func.broadcastVars.isEmpty =>
+        "Arrow UDF broadcast variables are not supported in-process"
+      case udf if udf.func.pythonIncludes != null && 
!udf.func.pythonIncludes.isEmpty =>
+        "Arrow UDF Python includes are not supported in-process"
+      case udf if hasUnsupportedEnvironment(udf.func.envVars) =>
+        "Arrow UDF Python environment overrides are not supported in-process"
+      case udf if 
udf.children.exists(_.find(_.isInstanceOf[PythonUDF]).nonEmpty) =>
+        "Chained Arrow UDFs are not supported in-process"
+    } match {
+      case Some(reason) => Unsupported(Some(reason))
+      case None => Compatible(None)
+    }
+  }
+
+  override def convert(
+      op: ArrowEvalPythonExec,
+      builder: Operator.Builder,
+      childOp: Operator*): Option[Operator] = {
+    if (childOp.length != 1) {
+      withFallbackReason(op, "Arrow UDF requires one native child")
+      return None
+    }
+
+    val functions = op.udfs.zip(op.resultAttrs).map { case (udf, attr) =>
+      val args: Seq[(Expression, String)] = udf.children.map {
+        case NamedArgumentExpression(key, value) => (value, key)
+        case other => (other, "")
+      }
+      val argProtos = args.map { case (expr, _) =>
+        QueryPlanSerde.exprToProto(expr, op.child.output)
+      }
+      val returnType = QueryPlanSerde.serializeDataType(attr.dataType)
+      if (argProtos.exists(_.isEmpty) || returnType.isEmpty) {
+        None
+      } else {
+        Some(
+          OperatorOuterClass.ArrowPythonFunction
+            .newBuilder()
+            .setCommand(ByteString.copyFrom(udf.func.command.toArray))
+            .addAllArgs(argProtos.map(_.get).asJava)
+            .addAllArgNames(args.map(_._2).asJava)
+            .setReturnType(returnType.get)
+            .setReturnName(attr.name)
+            .setPythonVersion(udf.func.pythonVer)
+            .build())
+      }
+    }
+    if (functions.exists(_.isEmpty)) {
+      withFallbackReason(op, "Arrow UDF argument or return type cannot be 
serialized")
+      None
+    } else {
+      val native = OperatorOuterClass.ArrowPythonUdf
+        .newBuilder()
+        .addAllFunctions(functions.map(_.get).asJava)
+        .setMaxRecordsPerBatch(op.conf.arrowMaxRecordsPerBatch)

Review Comment:
   Thanks for the detailed reproduction. Fixed in `c73d8b599`: Scala now passes 
`arrowMaxBytesPerBatch` to the native operator. Rust splits all UDF arguments 
at the same boundary when either the row or byte limit is reached, counting 
Arrow values, offsets, and validity buffers. I added Rust tests and a real 
PySpark `@arrow_udf` test with a 16-byte limit and four `long` values. The 
Spark 4.1 and 4.2 PyArrow CI jobs pass.



##########
spark/src/test/resources/pyspark/test_pyarrow_udf.py:
##########
@@ -112,6 +112,50 @@ def _assert_plan_matches_mode(
         )
 
 
+def test_scalar_arrow_udf_uses_native_path_and_spark_batch_limit(spark):
+    # This must use PySpark's real UDF wrapper: it populates the default
+    # PYTHONHASHSEED entry that a hand-built SimplePythonFunction omits.
+    from pyspark.sql.pandas import functions as pandas_functions
+
+    if not hasattr(pandas_functions, "arrow_udf"):
+        pytest.skip("scalar arrow_udf requires Spark 4.1 or later")
+
+    @pandas_functions.arrow_udf("long")
+    def batch_length(values):
+        return pa.array([len(values)] * len(values), type=pa.int64())
+
+    @pandas_functions.arrow_udf("long")
+    def string_hash(values):
+        return pa.array([hash(value) for value in values.to_pylist()], 
type=pa.int64())
+
+    source = spark.range(1, 5, 1, 1)
+    spark.conf.set("spark.sql.adaptive.enabled", "false")
+    spark.conf.set("spark.sql.execution.arrow.maxRecordsPerBatch", "2")
+    spark.conf.set("spark.comet.sparkToColumnar.enabled", "true")
+    try:
+        spark.conf.set("spark.comet.exec.nativeArrowPythonUDF.enabled", 
"false")
+        spark_rows = source.select(batch_length("id")).collect()

Review Comment:
   Thanks, I confirmed the Spark 4.2 failure. The reference query now runs with 
`spark.comet.enabled=false` and checks that its plan contains no Comet 
operator. The test restores Comet before the native query and retains the 
native-plan and two-row batch assertions. The Spark 4.2 PyArrow CI job now 
passes.



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