我们平常在使用Spark进行提交代码的时候,一般是直接在装有spark客户端的机器上提交jar包执行。运行命令如下:
/data/opt/spark-2.3.1-bin-hadoop2.7/bin/spark-submit \
--class com.tencent.th.dwd.t_dwd_evt_user_action_log_s \
--total-executor-cores 300 --conf spark.sql.shuffle.partitions=500 \
SparkV2-1.0.1.jar repartition_num=300
这里的执行入口spark-submit是什么呢?请看:
cat /data/opt/spark-2.3.1-bin-hadoop2.7/bin/spark-submit
if [ -z "${SPARK_HOME}" ]; then
source "$(dirname "$0")"/find-spark-home
fi
# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0
export SPARK_HOME=/data/opt/spark-2.3.1-bin-hadoop2.7/
exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"
这里首先是初始化SPARK_HOME目录,然后执行编译后的类:org.apache.spark.deploy.SparkSubmit,那么这个入口类做了哪些工作呢?请看源代码:
def main(args: Array[String]): Unit = {
//这里将传入的args参数进行初始化
val appArgs = new SparkSubmitArguments(args)
//判断参数是否有效合法
if (appArgs.verbose) {
// scalastyle:off println
printStream.println(appArgs)
// scalastyle:on println
}
//判断执行类别
appArgs.action match {
case SparkSubmitAction.SUBMIT => submit(appArgs)
case SparkSubmitAction.KILL => kill(appArgs)
case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
}
}
如果提交命令正确,开始执行spark:
/**
* 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.
*/
@tailrec
private def submit(args: SparkSubmitArguments): Unit = {
/**准备执行环境,这里主要得到了以下4个参数:
(1)childArgs: 子进程的参数
(2)childClasspath: 子进程的执行环境
(3)sysProps:系统参数
(4)childMainClass:子类名
**/
val (childArgs, childClasspath, sysProps, childMainClass) = prepareSubmitEnvironment(args)
//开始执行Spark任务
def doRunMain(): Unit = {
//是否需要创建代理用户
if (args.proxyUser != null) {
val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,
UserGroupInformation.getCurrentUser())
try {
proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {
override def run(): Unit = {
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
}
})
} catch {
case e: Exception =>
// Hadoop's AuthorizationException suppresses the exception's stack trace, which
// makes the message printed to the output by the JVM not very helpful. Instead,
// detect exceptions with empty stack traces here, and treat them differently.
if (e.getStackTrace().length == 0) {
// scalastyle:off println
printStream.println(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}")
// scalastyle:on println
exitFn(1)
} else {
throw e
}
}
} else {
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
}
}
执行的时候无论创建代理用户,最后都是调用 runMain方法开始执行,在runMain方法中,先是初始化判断参数是否verbose,然后是加载jar包:
for (jar <- childClasspath) {
addJarToClasspath(jar, loader)
}
接下来做了两件核心的事情,第一个:加载要执行的类:
mainClass = Utils.classForName(childMainClass)
第二个,判断要执行的任务的入口:
val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass)
最后一步,通过反射调用要执行类的任务:
mainMethod.invoke(null, childArgs.toArray)
整体来看,执行入口的代码还是比较清晰易懂的。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。