[SPARK-40433][SS][PYTHON] Add toJVMRow in PythonSQLUtils to convert p…

…ickled PySpark Row to JVM Row

### What changes were proposed in this pull request?

This PR adds toJVMRow in PythonSQLUtils to convert pickled PySpark Row to JVM Row.

Co-authored with HyukjinKwon .

This is a breakdown PR of apache#37863.

### Why are the changes needed?

This change will be leveraged in [SPARK-40434](https://issues.apache.org/jira/browse/SPARK-40434).

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

N/A. We will make sure test suites are constructed via E2E manner under [SPARK-40431](https://issues.apache.org/jira/browse/SPARK-40431).

Closes apache#37891 from HeartSaVioR/SPARK-40433.

Lead-authored-by: Jungtaek Lim <[email protected]>
Co-authored-by: Hyukjin Kwon <[email protected]>
Signed-off-by: Jungtaek Lim <[email protected]>

sql/core/src/main/scala/org/apache/spark/sql/api/python/PythonSQLUtils.scala

@@ -22,27 +22,45 @@ import java.net.Socket
 import java.nio.channels.Channels
 import java.util.Locale
 
-import net.razorvine.pickle.Pickler
+import net.razorvine.pickle.{Pickler, Unpickler}
 
 import org.apache.spark.api.python.DechunkedInputStream
 import org.apache.spark.internal.Logging
 import org.apache.spark.security.SocketAuthServer
 import org.apache.spark.sql.{Column, DataFrame, Row, SparkSession}
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
 import org.apache.spark.sql.execution.{ExplainMode, QueryExecution}
 import org.apache.spark.sql.execution.arrow.ArrowConverters
 import org.apache.spark.sql.execution.python.EvaluatePython
 import org.apache.spark.sql.internal.SQLConf
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.types.{DataType, StructType}
 
 private[sql] object PythonSQLUtils extends Logging {
-  private lazy val internalRowPickler = {
+  private def withInternalRowPickler(f: Pickler => Array[Byte]): Array[Byte] = {
     EvaluatePython.registerPicklers()
-    new Pickler(true, false)
+    val pickler = new Pickler(true, false)
+    val ret = try {
+        f(pickler)
+      } finally {
+        pickler.close()
+      }
+    ret
+  }
+
+  private def withInternalRowUnpickler(f: Unpickler => Any): Any = {
+    EvaluatePython.registerPicklers()
+    val unpickler = new Unpickler
+    val ret = try {
+        f(unpickler)
+      } finally {
+        unpickler.close()
+      }
+    ret
   }
 
   def parseDataType(typeText: String): DataType = CatalystSqlParser.parseDataType(typeText)
@@ -94,8 +112,18 @@ private[sql] object PythonSQLUtils extends Logging {
 
   def toPyRow(row: Row): Array[Byte] = {
     assert(row.isInstanceOf[GenericRowWithSchema])
-    internalRowPickler.dumps(EvaluatePython.toJava(
-      CatalystTypeConverters.convertToCatalyst(row), row.schema))
+    withInternalRowPickler(_.dumps(EvaluatePython.toJava(
+      CatalystTypeConverters.convertToCatalyst(row), row.schema)))
+  }
+
+  def toJVMRow(
+      arr: Array[Byte],
+      returnType: StructType,
+      deserializer: ExpressionEncoder.Deserializer[Row]): Row = {
+    val fromJava = EvaluatePython.makeFromJava(returnType)
+    val internalRow =
+        fromJava(withInternalRowUnpickler(_.loads(arr))).asInstanceOf[InternalRow]
+    deserializer(internalRow)
   }
 
   def castTimestampNTZToLong(c: Column): Column = Column(CastTimestampNTZToLong(c.expr))