Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/11105#discussion_r86486890
--- 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 --
the idea is that if you use an accumulator in a foreach, you get the same
behavior whether you use a data-property accumulator or not, right? that makes
sense. before this last change, would data-property accumulators just get
totally ignored in a foreach?
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