Github user squito 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 --
    don't you need to wrap this iterator to reset the partitionInfo per record, 
like you do in `MapPartitionsRDD`?  what if there is 
`rdd.reduceByKey(...).map(...)` with accumulator updates in both?
    The more I think about this, I am wondering if dataproperty accumulators 
should only be supported in map partitions rdd.  (sorry I think long ago I 
argued for putting this in ... I am still not certain ...)

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