Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2378#discussion_r17630886
--- Diff: core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
---
@@ -775,17 +775,38 @@ private[spark] object PythonRDD extends Logging {
}.toJavaRDD()
}
+ private class AutoBatchedPickler(iter: Iterator[Any]) extends
Iterator[Array[Byte]] {
+ private val pickle = new Pickler()
+ private var batch = 1
+ private val buffer = new mutable.ArrayBuffer[Any]
+
+ override def hasNext(): Boolean = iter.hasNext
+
+ override def next(): Array[Byte] = {
+ while (iter.hasNext && buffer.length < batch) {
+ buffer += iter.next()
+ }
+ val bytes = pickle.dumps(buffer.toArray)
+ val size = bytes.length
+ // let 1M < size < 10M
+ if (size < 1024 * 100) {
+ batch = (1024 * 100) / size // fast grow
--- End diff --
If the first record is small, e.g., a SparseVector with a single nonzero,
and the records followed are large vectors, line 789 may cause memory problems.
Does it give significant performance gain? under what circumstances?
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