Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/21977#discussion_r210271735
--- Diff:
resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
---
@@ -91,6 +91,13 @@ private[spark] class Client(
private val executorMemoryOverhead =
sparkConf.get(EXECUTOR_MEMORY_OVERHEAD).getOrElse(
math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toLong,
MEMORY_OVERHEAD_MIN)).toInt
+ private val isPython = sparkConf.get(IS_PYTHON_APP)
--- End diff --
It's true, creating mixed language pipelines is difficult and not
documented. But I do it, and some others do as well. Some cloud providers
(databricks is the most notable example) provide mixed language pipelines in
their notebook solutions I believe, and so I think that also reaches a larger
audience than the people who do it manually.
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