摘要：本文次要是通过Spark代码走读来理解spark-submit的流程。
1.工作命令提交
咱们在进行Spark工作提交时，会应用“spark-submit -class …..”款式的命令来提交工作，该命令为Spark目录下的shell脚本。它的作用是查问spark-home，调用spark-class命令。
if [ -z "${SPARK_HOME}" ]; then
source "$(dirname "$0")"/find-spark-home
fi
# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0
exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"随后会执行spark-class命令，以SparkSubmit类为参数进行工作向Spark程序的提交，而Spark-class的shell脚本次要是执行以下几个步骤：
（1）加载spark环境参数，从conf中获取
if [ -z "${SPARK_HOME}" ]; then
source "$(dirname "$0")"/find-spark-home
fi
. "${SPARK_HOME}"/bin/load-spark-env.sh
# 寻找javahome
if [ -n "${JAVA_HOME}" ]; then
RUNNER="${JAVA_HOME}/bin/java"
else
if [ "$(command -v java)" ]; then
RUNNER="java"
else
echo "JAVA_HOME is not set" >&2
exit 1
fi
fi（2）载入java，jar包等
# Find Spark jars.
if [ -d "${SPARK_HOME}/jars" ]; then
SPARK_JARS_DIR="${SPARK_HOME}/jars"
else
SPARK_JARS_DIR="${SPARK_HOME}/assembly/target/scala-$SPARK_SCALA_VERSION/jars"
fi（3）调用org.apache.spark.launcher中的Main进行参数注入
build_command() {
"$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
printf "%d0" $?
}（4）shell脚本监测工作执行状态，是否实现或者退出工作，通过执行返回值，判断是否完结
if ! [[ $LAUNCHER_EXIT_CODE =~ ^[0-9]+$ ]]; then
echo "${CMD[@]}" | head -n-1 1>&2
exit 1
fi
if [ $LAUNCHER_EXIT_CODE != 0 ]; then
exit $LAUNCHER_EXIT_CODE
fi
CMD=("${CMD[@]:0:$LAST}")
exec "${CMD[@]}"2.工作检测及提交工作到Spark
检测执行模式（class or submit）构建cmd，在submit中进行参数的查看（SparkSubmitOptionParser），构建命令行并且打印回spark-class中，最初调用exec执行spark命令行提交工作。通过组装而成cmd内容如下所示：
/usr/local/java/jdk1.8.0_91/bin/java-cp
/data/spark-1.6.0-bin-hadoop2.6/conf/:/data/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar:/data/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/data/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/data/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/data/hadoop-2.6.5/etc/hadoop/
-Xms1g-Xmx1g -Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=1234
org.apache.spark.deploy.SparkSubmit
--classorg.apache.spark.repl.Main
--nameSpark shell
--masterspark://localhost:7077
--verbose/tool/jarDir/maven_scala-1.0-SNAPSHOT.jar3.SparkSubmit函数的执行
（1）Spark工作在提交之后会执行SparkSubmit中的main办法
def main(args: Array[String]): Unit = {
val submit = new SparkSubmit()
submit.doSubmit(args)
}（2）doSubmit（）对log进行初始化，增加spark工作参数，通过参数类型执行工作：
def doSubmit(args: Array[String]): Unit = {
// Initialize logging if it hasn't been done yet. Keep track of whether logging needs to
// be reset before the application starts.
val uninitLog = initializeLogIfNecessary(true, silent = true)
val appArgs = parseArguments(args)
if (appArgs.verbose) {
logInfo(appArgs.toString)
}
appArgs.action match {
case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)
case SparkSubmitAction.KILL => kill(appArgs)
case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
case SparkSubmitAction.PRINT_VERSION => printVersion()
}
}SUBMIT：应用提供的参数提交application
KILL（Standalone and Mesos cluster mode only）：通过REST协定终止工作
REQUEST_STATUS（Standalone and Mesos cluster mode only）：通过REST协定申请曾经提交工作的状态
PRINT_VERSION：对log输入版本信息
（3）调用submit函数：
def doRunMain(): Unit = {
if (args.proxyUser != null) {
val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,
UserGroupInformation.getCurrentUser())
try {
proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {
override def run(): Unit = {
runMain(args, uninitLog)
}
})
} 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) {
error(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}")
} else {
throw e
}
}
} else {
runMain(args, uninitLog)
}
}doRunMain为集群调用子main class筹备参数，而后调用runMain()执行工作invoke main
4.总结
Spark在作业提交中会采纳多种不同的参数及模式，都会依据不同的参数抉择不同的分支执行，因而在最初提交的runMain中会将所须要的参数传递给执行函数。
本文分享自华为云社区《Spark内核解析之Spark-submit》，原文作者：笨熊爱喝cola。
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