Github user holdenk commented on a diff in the pull request: https://github.com/apache/spark/pull/11105#discussion_r86491369 --- Diff: core/src/main/scala/org/apache/spark/util/AccumulatorV2.scala --- @@ -136,15 +181,92 @@ abstract class AccumulatorV2[IN, OUT] extends Serializable { def reset(): Unit /** + * Takes the inputs and accumulates. e.g. it can be a simple `+=` for counter accumulator. + * Developers should extend addImpl to customize the adding functionality. + */ + final def add(v: IN): Unit = { + if (metadata != null && metadata.dataProperty) { + dataPropertyAdd(v) + } else { + addImpl(v) + } + } + + private def dataPropertyAdd(v: IN): Unit = { + // To allow the user to be able to access the current accumulated value from their process + // worker side then we need to perform a "normal" add as well as the data property add. + addImpl(v) + // Add to the pending updates for data property + val updateInfo = TaskContext.get().getRDDPartitionInfo() + val base = pending.getOrElse(updateInfo, copyAndReset()) + // Since we may have constructed a new accumulator, set atDriverSide to false as the default + // new accumulators will have atDriverSide equal to true. + base.atDriverSide = false + base.addImpl(v) + pending(updateInfo) = base + } + + /** + * Mark a specific rdd/shuffle/partition as completely processed. This is a noop for + * non-data property accumuables. + */ + private[spark] def markFullyProcessed(taskOutputId: TaskOutputId): Unit = { + if (metadata.dataProperty) { + completed += taskOutputId + } + } + + /** + * Takes the inputs and accumulates. e.g. it can be a simple `+=` for counter accumulator. + * Developers should extend addImpl to customize the adding functionality. * Takes the inputs and accumulates. */ - def add(v: IN): Unit + protected[spark] def addImpl(v: IN) + + /** + * Merges another same-type accumulator into this one and update its state, i.e. this should be + * merge-in-place. Developers should extend mergeImpl to customize the merge functionality. + */ + final private[spark] lazy val merge: (AccumulatorV2[IN, OUT] => Unit) = { + assert(isAtDriverSide) + // Handle data property accumulators + if (metadata != null && metadata.dataProperty) { + dataPropertyMerge _ + } else { + mergeImpl _ + } + } + + final private[spark] def dataPropertyMerge(other: AccumulatorV2[IN, OUT]) = { + // Apply all foreach partitions regardless - they can only be fully evaluated + val unprocessed = other.pending.filter{ + case (ForeachOutputId(), v) => mergeImpl(v); false --- End diff -- Yes they would.
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