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()
05-08 07:50