Github user felixcheung commented on a diff in the pull request: https://github.com/apache/spark/pull/20151#discussion_r159819670 --- Diff: core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala --- @@ -34,17 +34,25 @@ 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 } + // This configuration indicates the module to run the daemon to execute its Python workers. + val daemonModule = SparkEnv.get.conf.get("spark.python.daemon.module", "pyspark.daemon") --- End diff -- generally, I thought we use the name "command" as what we call the thing to execute
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