Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/14467#discussion_r75263947
--- Diff: core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
---
@@ -889,21 +892,42 @@ private class PythonAccumulatorParam(@transient
private val serverHost: String,
socket
}
- override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new
JArrayList
+ override def reset(): Unit = {
+ this._acc = Collections.synchronizedList(new JArrayList[Array[Byte]])
+ }
+
+ override def isZero: Boolean = {
+ this._acc.isEmpty
+ }
+
+ override def copyAndReset(): PythonAccumulatorV2 = new
PythonAccumulatorV2(serverHost, serverPort)
+
+ override def copy(): PythonAccumulatorV2 = {
+ val newAcc = new PythonAccumulatorV2(serverHost, serverPort)
+ newAcc._acc.addAll(this._acc)
+ newAcc
+ }
+
+ // This happens on the worker node, where we just want to remember all
the updates
+ override def add(val2: JList[Array[Byte]]): Unit = {
+ _acc.addAll(val2)
+ }
+
- override def addInPlace(val1: JList[Array[Byte]], val2:
JList[Array[Byte]])
- : JList[Array[Byte]] = synchronized {
+ override def merge(other: AccumulatorV2[JList[Array[Byte]],
JList[Array[Byte]]]): Unit = {
+ val otherPythonAccumulator = other.asInstanceOf[PythonAccumulatorV2]
+ // This conditional isn't strictly speaking needed - merging only
currently happens on the
+ // driver program - but that isn't gauranteed so incase this changes.
if (serverHost == null) {
- // This happens on the worker node, where we just want to remember
all the updates
- val1.addAll(val2)
- val1
+ // We are on the worker
+ add(otherPythonAccumulator._acc)
} else {
// This happens on the master, where we pass the updates to Python
through a socket
val socket = openSocket()
val in = socket.getInputStream
val out = new DataOutputStream(new
BufferedOutputStream(socket.getOutputStream, bufferSize))
- out.writeInt(val2.size)
- for (array <- val2.asScala) {
+ out.writeInt(otherPythonAccumulator._acc.size)
--- End diff --
So this code path is only taken during merging on the driver side - and
there is no reason to merge the same accumulated value into two different
accumulators at the same time. You can also see the merge logic inside of
DAGScheduler.scala & TaskMetrics (although not applicable here since the Python
accumulator isn't a task metric) and verify that the updates are merged in one
at a time.
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