Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/14467#discussion_r74857555
--- Diff: python/pyspark/context.py ---
@@ -173,9 +173,8 @@ def _do_init(self, master, appName, sparkHome, pyFiles,
environment, batchSize,
# they will be passed back to us through a TCP server
self._accumulatorServer = accumulators._start_update_server()
(host, port) = self._accumulatorServer.server_address
- self._javaAccumulator = self._jsc.accumulator(
- self._jvm.java.util.ArrayList(),
- self._jvm.PythonAccumulatorParam(host, port))
+ self._javaAccumulator = self._jvm.PythonAccumulatorV2(host, port)
+ self._jsc.sc().register(self._javaAccumulator)
--- End diff --
So in general you would have one SparkContext and many RDDs. The
accumulator here doesn't represent a specific accumulator rather a general
mechanism for all of the Python accumulators are built on top of. The design is
certainly a bit confusing if you try and think of it as a regular accumulator -
I found it helped to look at how the scala side "merge" is implemented.
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