Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15508664
--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,49 @@ private[python] object SerDeUtil extends Logging {
* by PySpark. By default, if serialization fails, toString is called
and the string
* representation is serialized
*/
- def rddToPython(rdd: RDD[(Any, Any)]): RDD[Array[Byte]] = {
+ def pairRDDToPython(rdd: RDD[(Any, Any)], batchSize: Int):
RDD[Array[Byte]] = {
val (keyFailed, valueFailed) = checkPickle(rdd.first())
rdd.mapPartitions { iter =>
val pickle = new Pickler
- iter.map { case (k, v) =>
- if (keyFailed && valueFailed) {
- pickle.dumps(Array(k.toString, v.toString))
- } else if (keyFailed) {
- pickle.dumps(Array(k.toString, v))
- } else if (!keyFailed && valueFailed) {
- pickle.dumps(Array(k, v.toString))
- } else {
- pickle.dumps(Array(k, v))
+ val cleaned = iter.map { case (k, v) =>
+ val key = if (keyFailed) k.toString else k
+ val value = if (valueFailed) v.toString else v
+ Array[Any](key, value)
+ }
+ if (batchSize > 1) {
+ cleaned.grouped(batchSize).map(batched =>
pickle.dumps(seqAsJavaList(batched)))
+ } else {
+ cleaned.map(pickle.dumps(_))
+ }
+ }
+ }
+
+ /**
+ * Convert an RDD of serialized Python tuple (K, V) to RDD[(K, V)].
+ */
+ def pythonToPairRDD[K, V](pyRDD: RDD[Array[Byte]]): RDD[(K, V)] = {
+ def isPair(obj: Any): Boolean = {
+
Option(obj.getClass.getComponentType).map(!_.isPrimitive).getOrElse(false) &&
+ obj.asInstanceOf[Array[_]].length == 2
+ }
+ pyRDD.mapPartitions { iter =>
+ val unpickle = new Unpickler
+ iter.flatMap { row =>
+ unpickle.loads(row) match {
+ // batch serialized Python RDDs
+ case objs: java.util.List[_] => objs
--- End diff --
What if I disabled batching and serialized an RDD of Python lists? In that
case, I think this would end up inadvertently flattening the RDD of lists. To
handle this case, I think you need to store an additional field in PythonRDD to
record whether its pickled objects were batch serialized.
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