Github user rdblue commented on a diff in the pull request: https://github.com/apache/spark/pull/21977#discussion_r207631295 --- Diff: resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala --- @@ -333,7 +340,7 @@ private[spark] class Client( val maxMem = newAppResponse.getMaximumResourceCapability().getMemory() logInfo("Verifying our application has not requested more than the maximum " + s"memory capability of the cluster ($maxMem MB per container)") - val executorMem = executorMemory + executorMemoryOverhead + val executorMem = executorMemory + executorMemoryOverhead + pysparkWorkerMemory if (executorMem > maxMem) { throw new IllegalArgumentException(s"Required executor memory ($executorMemory" + s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " + --- End diff -- I like having it broken out so users can see where their allocation is going. Otherwise, users that only know about `spark.executor.memory` might not know how their allocation is 1gb higher when running PySpark. I've updated this to include the worker memory.
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