sparkR在spark2.0里面，RDD后端代码位于org.apache.spark.rdd中，R语言相关的位于org.apache.spark.api.r中。
从入口开始，./bin/sparkR里面只有四句话，调用的是这个
exec "$SPARK_HOME"/bin/spark-submit sparkr-shell-main "$@"
spark-submit里面是个一句话的shell脚本
exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"
好了，入口是org.apache.spark.deploy.SparkSubmit这个类，该类中的main方法中调用具体方法
case SparkSubmitAction.SUBMIT => submit(appArgs) /**
* Submit the application using the provided parameters. *
* This runs in two steps. First, we prepare the launch environment by setting up
* the appropriate classpath, system properties, and application arguments for
* running the child main class based on the cluster manager and the deploy mode.
* Second, we use this launch environment to invoke the main method of the child * main class.
*/
private def submit(args: SparkSubmitArguments): Unit = {
submit方法准备classpath、系统属性、运行参数,然后按照这些调用下面的方法运行
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
该方法主要两步，第一步调用下面方法进行准备
val (childArgs, childClasspath, sysProps, childMainClass) = prepareSubmitEnvironment(args)
第二部会调用
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
进行执行。
在第一步中将sparkR的R相关代码打包成zip文件，然后设置将要运行的主类
如果是SPARKR-SHELL则调用org.apache.spark.api.r.RBackend
如果是纯粹client模式，则调用org.apache.spark.deploy.RRunner，其调用形式如下，例如
Usage: RRunner <main R file> [app arguments]
sun.java.command=com.aliyun.odps.cupid.runtime.Main --class org.apache.spark.deploy.RRunner --primary-r-file testOdpsRdd.R --arg testOdpsRdd.R
RBackend基于netty用来在R和java之间的通讯
Runner里面会调用启动RBackend，然后启动processBuilder去执行R脚本，也就是这句话:
new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs)
如何让spark worker识别sparkR代码呢？在R语言中变量R_PROFILE_USER ，用来初始化R运行环境，sparkR相关代码被打包提交到计算集群以后，在计算节点上面首先设置这个数值指向到初始化脚本${SPARK_HOME}/sparkr/SparkR/profile/general.R，这个脚本中识别路径，并且把解压后sparkR的代码安装到当前R环境中。下面是其代码
.First <- function() {
packageDir <- Sys.getenv("SPARKR_PACKAGE_DIR")
.libPaths(c(packageDir, .libPaths()))
Sys.setenv(NOAWT=1)
}下面的代码来自于prepareSubmitEnvironment
// In YARN mode for an R app, add the SparkR package archive to archives
// that can be distributed with the job
if (args.isR && clusterManager == YARN) {
val rPackagePath = RUtils.localSparkRPackagePath
if (rPackagePath.isEmpty) {
printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
}
val rPackageFile = RPackageUtils.zipRLibraries(new File(rPackagePath.get), SPARKR_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val localURI = Utils.resolveURI(rPackageFile.getAbsolutePath)
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, localURI.toString + "#sparkr")
}
// If we're running a R app, set the main class to our specific R runner if (args.isR && deployMode == CLIENT) {
if (args.primaryResource == SPARKR_SHELL) {
args.mainClass = "org.apache.spark.api.r.RBackend"
} else {
// If a R file is provided, add it to the child arguments and list of files to deploy. // Usage: RRunner <main R file> [app arguments]
args.mainClass = "org.apache.spark.deploy.RRunner"
args.childArgs = ArrayBuffer(args.primaryResource) ++ args.childArgs args.files = mergeFileLists(args.files, args.primaryResource)
}
}
if (isYarnCluster && args.isR) {
// In yarn-cluster mode for a R app, add primary resource to files
// that can be distributed with the job
args.files = mergeFileLists(args.files, args.primaryResource)
}
对于普通scala/java作业，standalone情况下直接调用下面类
// In legacy standalone cluster mode, use Client as a wrapper around the user
class childMainClass = "org.apache.spark.deploy.Client"
在client模式下直接提交用户应用主类运行，这里的主类如果是SPARKR_SHELL的话就是org.apache.spark.api.r.RBackend
直接提交文件执行则调用org.apache.spark.deploy.RRunner
// In client mode, launch the application main class directly
// In addition, add the main application jar and any added jars (if any) to the classpath
if (deployMode == CLIENT) {
childMainClass = args.mainClass
if (isUserJar(args.primaryResource)) {
childClasspath += args.primaryResource
} if (args.jars != null) {
childClasspath ++= args.jars.split(",")
} if (args.childArgs != null) {
childArgs ++= args.childArgs
}
}
在yarnCluster模式调度情况下，使用org.apache.spark.deploy.yarn.Client
这个类包装用户的类进行提交
// In yarn-cluster mode, use yarn.Client as a wrapper around the user class
if (isYarnCluster) {
childMainClass = "org.apache.spark.deploy.yarn.Client"
if (args.isPython) {
childArgs += ("--primary-py-file", args.primaryResource)
if (args.pyFiles != null) {
childArgs += ("--py-files", args.pyFiles)
}
childArgs += ("--class", "org.apache.spark.deploy.PythonRunner")
} else if (args.isR) {
val mainFile = new Path(args.primaryResource).getName
childArgs += ("--primary-r-file", mainFile)
childArgs += ("--class", "org.apache.spark.deploy.RRunner")
} else {
if (args.primaryResource != SPARK_INTERNAL) {
childArgs += ("--jar", args.primaryResource)
}
childArgs += ("--class", args.mainClass)
} if (args.childArgs != null) {
args.childArgs.foreach { arg => childArgs += ("--arg", arg)
}
}
}
org.apache.spark.deploy.yarn.Client
Py调用spark过程，在python/pyspark/context.py下面存在
class SparkContext(object)
其中的_jvm成员作为py4j的调用存在，其初始化
233 if not SparkContext._gateway:
234 SparkContext._gateway=gateway or launch_gateway()
235 SparkContext._jvm=SparkContext._gateway.jvm
其调用后端方法
207 # Create a temporary directory inside spark.local.dir:
208 local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf())
209 self._temp_dir = \
210 self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir, "pyspark") \
211 .getAbsolutePath()