Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/23055#discussion_r236512196
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala ---
@@ -74,8 +74,13 @@ private[spark] abstract class BasePythonRunner[IN, OUT](
private val reuseWorker = conf.getBoolean("spark.python.worker.reuse",
true)
// each python worker gets an equal part of the allocation. the worker
pool will grow to the
// number of concurrent tasks, which is determined by the number of
cores in this executor.
- private val memoryMb = conf.get(PYSPARK_EXECUTOR_MEMORY)
+ private val memoryMb = if (Utils.isWindows) {
--- End diff --
I mean, even if it succeeds to allocate in Yarn and Python worker doesn't
have the control on that, what's the point?
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