Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/11117#discussion_r52868615
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala ---
@@ -67,6 +74,72 @@ case class MapPartitions(
}
}
+case class PythonMapPartitions(
+ func: PythonFunction,
+ output: Seq[Attribute],
+ child: SparkPlan) extends UnaryNode {
+
+ override def expressions: Seq[Expression] = Nil
+
+ private def isPickled(schema: StructType): Boolean = {
+ schema.length == 1 && schema.head.dataType == BinaryType &&
+ schema.head.metadata.contains("pickled")
+ }
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ val inputRDD = child.execute().map(_.copy())
+ val bufferSize = inputRDD.conf.getInt("spark.buffer.size", 65536)
+ val reuseWorker =
inputRDD.conf.getBoolean("spark.python.worker.reuse", defaultValue = true)
+ val childIsPickled = isPickled(child.schema)
+ val outputIsPickled = isPickled(schema)
+
+ inputRDD.mapPartitions { iter =>
+ val inputIterator = if (childIsPickled) {
+ iter.map(_.getBinary(0))
+ } else {
+ EvaluatePython.registerPicklers() // register pickler for Row
+
+ val pickle = new Pickler
+
+ // Input iterator to Python: input rows are grouped so we send
them in batches to Python.
+ // For each row, add it to the queue.
+ iter.grouped(100).map { inputRows =>
+ val toBePickled = inputRows.map { row =>
+ EvaluatePython.toJava(row, child.schema)
+ }.toArray
+ pickle.dumps(toBePickled)
+ }
+ }
+
+ val context = TaskContext.get()
+
+ // Output iterator for results from Python.
+ val outputIterator =
+ new PythonRunner(
+ func.command,
+ func.envVars,
+ func.pythonIncludes,
+ func.pythonExec,
+ func.pythonVer,
+ func.broadcastVars,
+ func.accumulator,
+ bufferSize,
+ reuseWorker
+ ).compute(inputIterator, context.partitionId(), context)
+
+ if (outputIsPickled) {
+ outputIterator.map(bytes => InternalRow(bytes))
--- End diff --
BatchPythonEvaluation will produce UnsafeRow.
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