Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/20151#discussion_r160864049 --- Diff: core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala --- @@ -34,17 +34,39 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String import PythonWorkerFactory._ - // Because forking processes from Java is expensive, we prefer to launch a single Python daemon - // (pyspark/daemon.py) and tell it to fork new workers for our tasks. This daemon currently - // only works on UNIX-based systems now because it uses signals for child management, so we can - // also fall back to launching workers (pyspark/worker.py) directly. + // Because forking processes from Java is expensive, we prefer to launch a single Python daemon, + // pyspark/daemon.py (by default) and tell it to fork new workers for our tasks. This daemon + // currently only works on UNIX-based systems now because it uses signals for child management, + // so we can also fall back to launching workers, pyspark/worker.py (by default) directly. val useDaemon = { val useDaemonEnabled = SparkEnv.get.conf.getBoolean("spark.python.use.daemon", true) // This flag is ignored on Windows as it's unable to fork. !System.getProperty("os.name").startsWith("Windows") && useDaemonEnabled } + // WARN: Both configurations, 'spark.python.daemon.module' and 'spark.python.worker.module' are + // for very advanced users and they are experimental. This should be considered + // as expert-only option, and shouldn't be used before knowing what it means exactly. + + // This configuration indicates the module to run the daemon to execute its Python workers. + val daemonModule = SparkEnv.get.conf.getOption("spark.python.daemon.module").map { value => --- End diff -- Hm, actually we could check like .. if it's empty string too. I wrote "shouldn't be used before knowing what it means exactly." above. So, I think it's fine.
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