Github user squito commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11105#discussion_r56421658
  
    --- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
    @@ -146,6 +212,32 @@ class Accumulable[R, T] private (
       def merge(term: R) { value_ = param.addInPlace(value_, term)}
     
       /**
    +   * Merge in pending updates for ac consistent accumulators or merge 
accumulated values for
    +   * regular accumulators. This is only called on the driver when merging 
task results together.
    +   */
    +  private[spark] def internalMerge(term: Any) {
    +    if (!consistent) {
    +      merge(term.asInstanceOf[R])
    +    } else {
    +      mergePending(term.asInstanceOf[mutable.HashMap[(Int, Int, Int), R]])
    +    }
    +  }
    +
    +  /**
    +   * Merge another Accumulable's pending updates, checks to make sure that 
each pending update has
    +   * not already been processed before updating.
    +   */
    +  private[spark] def mergePending(term: mutable.HashMap[(Int, Int, Int), 
R]) = {
    +    term.foreach{case ((rddId, shuffleId, splitId), v) =>
    +      val splits = processed.getOrElseUpdate((rddId, shuffleId), new 
mutable.BitSet())
    +      if (!splits.contains(splitId)) {
    +        splits += splitId
    +        value_ = param.addInPlace(value_, v)
    +      }
    --- End diff --
    
    I don't think you need both `completed` and `processed` -- they seem to be 
doing more or less the same thing.  You could change this to:
    
    ```scala
        term.foreach{case (partitionKey, v) =>
          if (!completed.contains(partitionKey)) {
            completed += partitionKey
            value_ = param.addInPlace(value_, v)
          }
        }
    ```
    
    If I understand correctly, there is a bit of logical distinction between 
the two -- `processed` was being used on the driver, that is across multiple 
tasks, to track what had been accumulated and what hadn't.  `completed`, OTOH, 
was being used on the executors, to track the updates coming from *one task*.  
Typically it would only contain a single entry, for the one `(rdd, shuffle, 
partition)` tuple that was being used, since tasks *usually* only compute one 
partition, but that isn't true when there is a coalesce involved.
    
    If that explanation sounds correct, its probably best to put it into a 
comment somewhere, and I'd still say that you can eliminate `processed` and 
just explain how `completed` will get used in two different ways on the 
executors and on the driver.


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