@ shashank's ответ правильный, это количество ядер, которые будут использоваться Spark при работе в локальном режиме.
Для получения подробной информации вы можете проверить код SparkContext .numDriverCores :
/**
* The number of cores available to the driver to use for tasks such as I/O with Netty
*/
private[spark] def numDriverCores(master: String, conf: SparkConf): Int = {
def convertToInt(threads: String): Int = {
if (threads == "*") Runtime.getRuntime.availableProcessors() else threads.toInt
}
master match {
case "local" => 1
case SparkMasterRegex.LOCAL_N_REGEX(threads) => convertToInt(threads)
case SparkMasterRegex.LOCAL_N_FAILURES_REGEX(threads, _) => convertToInt(threads)
case "yarn" =>
if (conf != null && conf.getOption("spark.submit.deployMode").contains("cluster")) {
conf.getInt("spark.driver.cores", 0)
} else {
0
}
case _ => 0 // Either driver is not being used, or its core count will be interpolated later
}
}
с
val LOCAL_N_REGEX = """local\[([0-9]+|\*)\]""".r