Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1124#discussion_r13955317
--- Diff: core/src/main/scala/org/apache/spark/executor/Executor.scala ---
@@ -212,7 +208,12 @@ private[spark] class Executor(
val serializedDirectResult = ser.serialize(directResult)
logInfo("Serialized size of result for " + taskId + " is " +
serializedDirectResult.limit)
val serializedResult = {
- if (serializedDirectResult.limit >= akkaFrameSize - 1024) {
+ // TODO: [SPARK-1112] We use the min frame size to determine
whether to use Akka to send
+ // the task result or block manager. Since this is via the
backend, whose actor system is
+ // initialized before receiving the Spark conf, and hence it
does not know
+ // `spark.akka.frameSize`. A temporary solution is using the min
frame size.
+ // [SPARK-2156] We subtract 200K to leave some space for other
data in the Akka message.
+ if (serializedDirectResult.limit >= AkkaUtils.minFrameSizeBytes
- 200 * 1024) {
--- End diff --
@kayousterhout I saw the line in `CoarseGrainedSchedulerBackend`. Should
the overhead be bounded by a fixed size or proportional to the message size?
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