Github user holdenk commented on a diff in the pull request:
    --- Diff: core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala ---
    @@ -104,10 +105,26 @@ class ShuffledRDD[K: ClassTag, V: ClassTag, C: 
       override def compute(split: Partition, context: TaskContext): 
Iterator[(K, C)] = {
    +    // Use -1 for our Shuffle ID since we are on the read side of the 
    +    val shuffleWriteId = -1
    +    // If our task has data property accumulators we need to keep track of 
which partitions
    +    // we are processing.
    +    if (context.taskMetrics.hasDataPropertyAccumulators()) {
    +      context.setRDDPartitionInfo(id, shuffleWriteId, split.index)
    +    }
         val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
    -    SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, 
split.index + 1, context)
    +    val itr = SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, 
split.index, split.index + 1,
    +      context)
    --- End diff --
    So the short version is no, because the only user controlled code which 
happens underneath this is eagerly evaluated on the entire shuffle input - 
namely `combineValuesByKey` and `combineCombinersByKey` both consume the entire 
input before passing back control. I'll add this in a comment explaining this 

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