Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/8835#discussion_r40131699
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala ---
@@ -342,51 +348,57 @@ case class BatchPythonEvaluation(udf: PythonUDF,
output: Seq[Attribute], child:
override def canProcessSafeRows: Boolean = true
protected override def doExecute(): RDD[InternalRow] = {
- val childResults = child.execute().map(_.copy())
+ 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 parent = childResults.mapPartitions { iter =>
+ inputRDD.mapPartitions { iter =>
EvaluatePython.registerPicklers() // register pickler for Row
+
+ // The queue used to buffer input rows so we can drain it to
+ // combine input with output from Python.
+ val queue = new
java.util.concurrent.ConcurrentLinkedQueue[InternalRow]()
+
val pickle = new Pickler
val currentRow = newMutableProjection(udf.children, child.output)()
val fields = udf.children.map(_.dataType)
val schema = new StructType(fields.map(t => new StructField("", t,
true)).toArray)
- iter.grouped(100).map { inputRows =>
+
+ // Input iterator to Python: input rows are grouped so we send them
in batches to Python.
+ // For each row, add it to the queue.
+ val inputIterator = iter.grouped(100).map { inputRows =>
val toBePickled = inputRows.map { row =>
+ queue.add(row)
EvaluatePython.toJava(currentRow(row), schema)
}.toArray
pickle.dumps(toBePickled)
}
- }
- val pyRDD = new PythonRDD(
- parent,
- udf.command,
- udf.envVars,
- udf.pythonIncludes,
- false,
- udf.pythonExec,
- udf.pythonVer,
- udf.broadcastVars,
- udf.accumulator
- ).mapPartitions { iter =>
- val pickle = new Unpickler
- iter.flatMap { pickedResult =>
- val unpickledBatch = pickle.loads(pickedResult)
- unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala
- }
- }.mapPartitions { iter =>
+ val context = TaskContext.get()
+
+ // Output iterator for results from Python.
+ val outputIterator = new PythonRunner(
+ udf.command,
+ udf.envVars,
+ udf.pythonIncludes,
+ udf.pythonExec,
+ udf.pythonVer,
+ udf.broadcastVars,
+ udf.accumulator,
+ bufferSize,
+ reuseWorker
+ ).compute(inputIterator, context.partitionId(), context)
+
+ val unpickle = new Unpickler
--- End diff --
In the old code, it looks like a new `Unpickler` was constructed for each
partition. Should we use `outputIterator.flatMapPartitions` and move the
construction of the unpickler to there in order to be safe / more conservative?
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