Spark（十五）SparkCore的源码解读

一、启动脚本分析

独立部署模式下，主要由master和slaves组成，master可以利用zk实现高可用性，其driver，work，app等信息可以持久化到zk上；slaves由一台至多台主机构成。Driver通过向Master申请资源获取运行环境。

启动master和slaves主要是执行/usr/dahua/spark/sbin目录下的start-master.sh和start-slaves.sh，或者执行

start-all.sh，其中star-all.sh本质上就是调用start-master.sh和start-slaves.sh

1.1　start-all.sh 

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

#2.执行${SPARK_HOME}/sbin/spark-config.sh，见以下分析
. "${SPARK_HOME}/sbin/spark-config.sh"

#3.执行"${SPARK_HOME}/sbin"/start-master.sh，见以下分析
"${SPARK_HOME}/sbin"/start-master.sh

#4.执行"${SPARK_HOME}/sbin"/start-slaves.sh，见以下分析
"${SPARK_HOME}/sbin"/start-slaves.sh

其中start-master.sh和start-slave.sh分别调用的是

org.apache.spark.deploy.master.Master和org.apache.spark.deploy.worker.Worker

1.2　start-master.sh

start-master.sh调用了spark-daemon.sh，注意这里指定了启动的类

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

# NOTE: This exact class name is matched downstream by SparkSubmit.
# Any changes need to be reflected there.
#2.设置CLASS="org.apache.spark.deploy.master.Master"
CLASS="org.apache.spark.deploy.master.Master"

#3.如果参数结尾包含--help或者-h则打印帮助信息，并退出
if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
  echo "Usage: ./sbin/start-master.sh [options]"
  pattern="Usage:"
  pattern+="\|Using Spark's default log4j profile:"
  pattern+="\|Registered signal handlers for"

  "${SPARK_HOME}"/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
  exit 1
fi

#4.设置ORIGINAL_ARGS为所有参数
ORIGINAL_ARGS="$@"
#5.执行${SPARK_HOME}/sbin/spark-config.sh
. "${SPARK_HOME}/sbin/spark-config.sh"
#6.执行${SPARK_HOME}/bin/load-spark-env.sh
. "${SPARK_HOME}/bin/load-spark-env.sh"
#7.SPARK_MASTER_PORT为空则赋值7077
if [ "$SPARK_MASTER_PORT" = "" ]; then
  SPARK_MASTER_PORT=7077
fi
#8.SPARK_MASTER_HOST为空则赋值本主机名(hostname)
if [ "$SPARK_MASTER_HOST" = "" ]; then
  case `uname` in
      (SunOS)
      SPARK_MASTER_HOST="`/usr/sbin/check-hostname | awk '{print $NF}'`"
      ;;
      (*)
      SPARK_MASTER_HOST="`hostname -f`"
      ;;
  esac
fi
#9.SPARK_MASTER_WEBUI_PORT为空则赋值8080
if [ "$SPARK_MASTER_WEBUI_PORT" = "" ]; then
  SPARK_MASTER_WEBUI_PORT=8080
fi
#10.执行脚本
"${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS 1 \
  --host $SPARK_MASTER_HOST --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT \
  $ORIGINAL_ARGS

其中10肯定是重点，分析之前我们看看5，6都干了些啥，最后直译出最后一个脚本

1.3　spark-config.sh(1.2的第5步)

#判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi
#SPARK_CONF_DIR存在就用此目录，不存在用${SPARK_HOME}/conf
export SPARK_CONF_DIR="${SPARK_CONF_DIR:-"${SPARK_HOME}/conf"}"
# Add the PySpark classes to the PYTHONPATH:
if [ -z "${PYSPARK_PYTHONPATH_SET}" ]; then
  export PYTHONPATH="${SPARK_HOME}/python:${PYTHONPATH}"
  export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.6-src.zip:${PYTHONPATH}"
  export PYSPARK_PYTHONPATH_SET=1
fi

1.4　load-spark-env.sh(1.2的第6步)

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
fi
#2.判断SPARK_ENV_LOADED是否有值，没有将其设置为1
if [ -z "$SPARK_ENV_LOADED" ]; then
  export SPARK_ENV_LOADED=1
#3.设置user_conf_dir为SPARK_CONF_DIR或SPARK_HOME/conf
  export SPARK_CONF_DIR="${SPARK_CONF_DIR:-"${SPARK_HOME}"/conf}"
#4.执行"${user_conf_dir}/spark-env.sh" [注：set -/+a含义再做研究]
  if [ -f "${SPARK_CONF_DIR}/spark-env.sh" ]; then
    # Promote all variable declarations to environment (exported) variables
    set -a
    . "${SPARK_CONF_DIR}/spark-env.sh"
    set +a
  fi
fi

# Setting SPARK_SCALA_VERSION if not already set.
#5.选择scala版本，2.11和2.12都存在的情况下，优先选择2.11
if [ -z "$SPARK_SCALA_VERSION" ]; then

  ASSEMBLY_DIR2="${SPARK_HOME}/assembly/target/scala-2.11"
  ASSEMBLY_DIR1="${SPARK_HOME}/assembly/target/scala-2.12"

  if [[ -d "$ASSEMBLY_DIR2" && -d "$ASSEMBLY_DIR1" ]]; then
    echo -e "Presence of build for multiple Scala versions detected." 1>&2
    echo -e 'Either clean one of them or, export SPARK_SCALA_VERSION in spark-env.sh.' 1>&2
    exit 1
  fi

  if [ -d "$ASSEMBLY_DIR2" ]; then
    export SPARK_SCALA_VERSION="2.11"
  else
    export SPARK_SCALA_VERSION="2.12"
  fi
fi

1.5　spark-env.sh

列举很多种模式的选项配置

1.6　spark-daemon.sh

回过头来看看1.2第10步中需要直译出的最后一个脚本,如下：

sbin/spark-daemon.sh start org.apache.spark.deploy.master.Master 1 --host hostname --port 7077 --webui-port 8080

上面搞了半天只是设置了变量，最终才进入主角，继续分析spark-daemon.sh脚本

#1.参数个数小于等于1，打印帮助
if [ $# -le 1 ]; then
  echo $usage
  exit 1
fi
#2.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi
#3.执行${SPARK_HOME}/sbin/spark-config.sh，见上述分析 [类似脚本是否有重复？原因是有的人是直接用spark-daemon.sh启动的服务，反正重复设置下变量不需要什么代价]
. "${SPARK_HOME}/sbin/spark-config.sh"

# get arguments

# Check if --config is passed as an argument. It is an optional parameter.
# Exit if the argument is not a directory.

#4.判断第一个参数是否是--config,如果是取空格后一个字符串，然后判断该目录是否存在，不存在则打印错误信息并退出，存在设置SPARK_CONF_DIR为该目录,shift到下一个参数
#[注：--config只能用在第一参数上]
if [ "$1" == "--config" ]
then
  shift
  conf_dir="$1"
  if [ ! -d "$conf_dir" ]
  then
    echo "ERROR : $conf_dir is not a directory"
    echo $usage
    exit 1
  else
    export SPARK_CONF_DIR="$conf_dir"
  fi
  shift
fi
#5.分别设置option、command、instance为后面的三个参数(如：option=start,command=org.apache.spark.deploy.master.Master,instance=1)
#[注：很多人用spark-daemon.sh启动服务不成功的原因是名字不全]
option=$1
shift
command=$1
shift
instance=$1
shift
#6.日志回滚函数，主要用于更改日志名，如log-->log.1等，略过
spark_rotate_log ()
{
    log=$1;
    num=5;
    if [ -n "$2" ]; then
    num=$2
    fi
    if [ -f "$log" ]; then # rotate logs
    while [ $num -gt 1 ]; do
        prev=`expr $num - 1`
        [ -f "$log.$prev" ] && mv "$log.$prev" "$log.$num"
        num=$prev
    done
    mv "$log" "$log.$num";
    fi
}
#7.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}/bin/load-spark-env.sh"

#8.判断SPARK_IDENT_STRING是否有值，没有将其设置为$USER(linux用户)
if [ "$SPARK_IDENT_STRING" = "" ]; then
  export SPARK_IDENT_STRING="$USER"
fi

#9.设置SPARK_PRINT_LAUNCH_COMMAND=1
export SPARK_PRINT_LAUNCH_COMMAND="1"

# get log directory
#10.判断SPARK_LOG_DIR是否有值，没有将其设置为${SPARK_HOME}/logs，并创建改目录，测试创建文件，修改权限
if [ "$SPARK_LOG_DIR" = "" ]; then
  export SPARK_LOG_DIR="${SPARK_HOME}/logs"
fi
mkdir -p "$SPARK_LOG_DIR"
touch "$SPARK_LOG_DIR"/.spark_test > /dev/null 2>&1
TEST_LOG_DIR=$?
if [ "${TEST_LOG_DIR}" = "0" ]; then
  rm -f "$SPARK_LOG_DIR"/.spark_test
else
  chown "$SPARK_IDENT_STRING" "$SPARK_LOG_DIR"
fi

#11.判断SPARK_PID_DIR是否有值，没有将其设置为/tmp
if [ "$SPARK_PID_DIR" = "" ]; then
  SPARK_PID_DIR=/tmp
fi

# some variables
#12.设置log和pid
log="$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.out"
pid="$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command-$instance.pid"

# Set default scheduling priority
#13.判断SPARK_NICENESS是否有值，没有将其设置为0 [注：调度优先级，见后面]
if [ "$SPARK_NICENESS" = "" ]; then
    export SPARK_NICENESS=0
fi

#14.execute_command()函数，暂且略过，调用时再作分析
execute_command() {
  if [ -z ${SPARK_NO_DAEMONIZE+set} ]; then
      nohup -- "$@" >> $log 2>&1 < /dev/null &
      newpid="$!"

      echo "$newpid" > "$pid"

      # Poll for up to 5 seconds for the java process to start
      for i in {1..10}
      do
        if [[ $(ps -p "$newpid" -o comm=) =~ "java" ]]; then
           break
        fi
        sleep 0.5
      done

      sleep 2
      # Check if the process has died; in that case we'll tail the log so the user can see
      if [[ ! $(ps -p "$newpid" -o comm=) =~ "java" ]]; then
        echo "failed to launch: $@"
        tail -10 "$log" | sed 's/^/  /'
        echo "full log in $log"
      fi
  else
      "$@"
  fi
}
#15.进入case语句，判断option值，进入该分支，我们以start为例
#   执行run_command class "$@"，其中$@此时为空，经验证，启动带上此参数后，关闭也需，不然关闭不了，后面再分析此参数作用
#   我们正式进入run_command()函数，分析
#   I.设置mode=class,创建SPARK_PID_DIR，上面的pid文件是否存在，
#   II.SPARK_MASTER不为空，同步删除某些文件
#   III.回滚log日志
#   IV.进入case，command=org.apache.spark.deploy.master.Master，最终执行
#       nohup nice -n "$SPARK_NICENESS" "${SPARK_HOME}"/bin/spark-class $command "$@" >> "$log" 2>&1 < /dev/null &
#       newpid="$!"
#       echo "$newpid" > "$pid"
#   重点转向bin/spark-class org.apache.spark.deploy.master.Master
run_command() {
  mode="$1"
  shift

  mkdir -p "$SPARK_PID_DIR"

  if [ -f "$pid" ]; then
    TARGET_ID="$(cat "$pid")"
    if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then
      echo "$command running as process $TARGET_ID.  Stop it first."
      exit 1
    fi
  fi

  if [ "$SPARK_MASTER" != "" ]; then
    echo rsync from "$SPARK_MASTER"
    rsync -a -e ssh --delete --exclude=.svn --exclude='logs/*' --exclude='contrib/hod/logs/*' "$SPARK_MASTER/" "${SPARK_HOME}"
  fi

  spark_rotate_log "$log"
  echo "starting $command, logging to $log"

  case "$mode" in
    (class)
      execute_command nice -n "$SPARK_NICENESS" "${SPARK_HOME}"/bin/spark-class "$command" "$@"
      ;;

    (submit)
      execute_command nice -n "$SPARK_NICENESS" bash "${SPARK_HOME}"/bin/spark-submit --class "$command" "$@"
      ;;

    (*)
      echo "unknown mode: $mode"
      exit 1
      ;;
  esac

}

case $option in

  (submit)
    run_command submit "$@"
    ;;

  (start)
    run_command class "$@"
    ;;

  (stop)

    if [ -f $pid ]; then
      TARGET_ID="$(cat "$pid")"
      if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then
        echo "stopping $command"
        kill "$TARGET_ID" && rm -f "$pid"
      else
        echo "no $command to stop"
      fi
    else
      echo "no $command to stop"
    fi
    ;;

  (status)

    if [ -f $pid ]; then
      TARGET_ID="$(cat "$pid")"
      if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then
        echo $command is running.
        exit 0
      else
        echo $pid file is present but $command not running
        exit 1
      fi
    else
      echo $command not running.
      exit 2
    fi
    ;;

  (*)
    echo $usage
    exit 1
    ;;

esac

1.7　spark-class

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
fi

#2.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}"/bin/load-spark-env.sh

# Find the java binary
#3.判断JAVA_HOME是否为NULL，不是则设置RUNNER="${JAVA_HOME}/bin/java"，否则找系统自带，在没有则报未设置，并退出
if [ -n "${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

# Find Spark jars.
#4.查找SPARK_JARS_DIR，若${SPARK_HOME}/RELEASE文件存在，则SPARK_JARS_DIR="${SPARK_HOME}/jars"，否则
#SPARK_JARS_DIR="${SPARK_HOME}/assembly/target/scala-$SPARK_SCALA_VERSION/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

#5.若SPARK_JARS_DIR不存在且$SPARK_TESTING$SPARK_SQL_TESTING有值[注：一般我们不设置这两变量]，报错退出，否则LAUNCH_CLASSPATH="$SPARK_JARS_DIR/*"
if [ ! -d "$SPARK_JARS_DIR" ] && [ -z "$SPARK_TESTING$SPARK_SQL_TESTING" ]; then
  echo "Failed to find Spark jars directory ($SPARK_JARS_DIR)." 1>&2
  echo "You need to build Spark with the target \"package\" before running this program." 1>&2
  exit 1
else
  LAUNCH_CLASSPATH="$SPARK_JARS_DIR/*"
fi

# Add the launcher build dir to the classpath if requested.
#6.SPARK_PREPEND_CLASSES不是NULL，则LAUNCH_CLASSPATH="${SPARK_HOME}/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH"，
#添加编译相关至LAUNCH_CLASSPATH
if [ -n "$SPARK_PREPEND_CLASSES" ]; then
  LAUNCH_CLASSPATH="${SPARK_HOME}/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH"
fi

# For tests
#7.SPARK_TESTING不是NULL，则unset YARN_CONF_DIR和unset HADOOP_CONF_DIR，暂且当做是为了某种测试
if [[ -n "$SPARK_TESTING" ]]; then
  unset YARN_CONF_DIR
  unset HADOOP_CONF_DIR
fi

#8.build_command函数，略过
build_command() {
  "$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
  printf "%d\0" $?
}

# Turn off posix mode since it does not allow process substitution
set +o posix
CMD=()
while IFS= read -d '' -r ARG; do
  CMD+=("$ARG")
  #9.最终调用"$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"，
  #直译：java -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
  #转向java类org.apache.spark.launcher.Main，这就是java入口类
done < <(build_command "$@")

COUNT=${#CMD[@]}
LAST=$((COUNT - 1))
LAUNCHER_EXIT_CODE=${CMD[$LAST]}

# Certain JVM failures result in errors being printed to stdout (instead of stderr), which causes
# the code that parses the output of the launcher to get confused. In those cases, check if the
# exit code is an integer, and if it's not, handle it as a special error case.
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[@]}"

1.8　start-slaves.sh

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

#2.执行${SPARK_HOME}/sbin/spark-config.sh，见上述分析
. "${SPARK_HOME}/sbin/spark-config.sh"

#3.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}/bin/load-spark-env.sh"

# Find the port number for the master
#4.SPARK_MASTER_PORT为空则设置为7077
if [ "$SPARK_MASTER_PORT" = "" ]; then
  SPARK_MASTER_PORT=7077
fi

#5.SPARK_MASTER_HOST为空则设置为`hostname`
if [ "$SPARK_MASTER_HOST" = "" ]; then
  case `uname` in
      (SunOS)
      SPARK_MASTER_HOST="`/usr/sbin/check-hostname | awk '{print $NF}'`"
      ;;
      (*)
      SPARK_MASTER_HOST="`hostname -f`"
      ;;
  esac
fi

# Launch the slaves
#6.启动slaves，
#   "${SPARK_HOME}/sbin/slaves.sh" cd "${SPARK_HOME}" \; "${SPARK_HOME}/sbin/start-slave.sh" "spark://$SPARK_MASTER_HOST:$SPARK_MASTER_PORT"
#   遍历conf/slaves中主机，其中有设置SPARK_SSH_OPTS，ssh每一台机器执行"${SPARK_HOME}/sbin/start-slave.sh" "spark://$SPARK_MASTER_HOST:$SPARK_MASTER_PORT"
"${SPARK_HOME}/sbin/slaves.sh" cd "${SPARK_HOME}" \; "${SPARK_HOME}/sbin/start-slave.sh" "spark://$SPARK_MASTER_HOST:$SPARK_MASTER_PORT"

1.9　转向start-slave.sh

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

#2.设置CLASS="org.apache.spark.deploy.worker.Worker"
CLASS="org.apache.spark.deploy.worker.Worker"

#3.如果参数结尾包含--help或者-h则打印帮助信息，并退出
if [[ $# -lt 1 ]] || [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
  echo "Usage: ./sbin/start-slave.sh [options] <master>"
  pattern="Usage:"
  pattern+="\|Using Spark's default log4j profile:"
  pattern+="\|Registered signal handlers for"

  "${SPARK_HOME}"/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
  exit 1
fi

#4.执行${SPARK_HOME}/sbin/spark-config.sh，见上述分析
. "${SPARK_HOME}/sbin/spark-config.sh"
#5.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}/bin/load-spark-env.sh"

#6.MASTER=$1,这里MASTER=spark://hostname:7077，然后shift，也就是说单独启动单个slave使用start-slave.sh spark://hostname:7077
MASTER=$1
shift

#7.SPARK_WORKER_WEBUI_PORT为空则设置为8081
if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then
  SPARK_WORKER_WEBUI_PORT=8081
fi

#8.函数start_instance，略过
function start_instance {
#设置WORKER_NUM=$1
  WORKER_NUM=$1
  shift

  if [ "$SPARK_WORKER_PORT" = "" ]; then
    PORT_FLAG=
    PORT_NUM=
  else
    PORT_FLAG="--port"
    PORT_NUM=$(( $SPARK_WORKER_PORT + $WORKER_NUM - 1 ))
  fi
  WEBUI_PORT=$(( $SPARK_WORKER_WEBUI_PORT + $WORKER_NUM - 1 ))

  #直译：spark-daemon.sh start org.apache.spark.deploy.worker.Worker 1 --webui-port 7077 spark://hostname:7077
  #代码再次转向spark-daemon.sh，见上诉分析
  "${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS $WORKER_NUM \
     --webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@"
}

#9.判断SPARK_WORKER_INSTANCES(可以认为是单节点Worker进程数)是否为空
#   为空，则start_instance 1 "$@"
#   不为空，则循环
#         for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do
#           start_instance $(( 1 + $i )) "$@"
#         done
if [ "$SPARK_WORKER_INSTANCES" = "" ]; then
  start_instance 1 "$@"
else
  for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do
  #10.转向start_instance函数
    start_instance $(( 1 + $i )) "$@"
  done
fi

二、其他脚本

2.1　start-history-server.sh

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

#2.执行${SPARK_HOME}/sbin/spark-config.sh，见上述分析
. "${SPARK_HOME}/sbin/spark-config.sh"
#3.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}/bin/load-spark-env.sh"
#4.exec "${SPARK_HOME}/sbin"/spark-daemon.sh start org.apache.spark.deploy.history.HistoryServer 1 $@ ，见上诉分析
exec "${SPARK_HOME}/sbin"/spark-daemon.sh start org.apache.spark.deploy.history.HistoryServer 1 "$@"

2.2　start-shuffle-service.sh

#1.判断SPARK_HOME是否有值，没有将其设置为当前文件所在目录的上级目录
if [ -z "${SPARK_HOME}" ]; then
  export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
fi

#2.执行${SPARK_HOME}/sbin/spark-config.sh，见上述分析
. "${SPARK_HOME}/sbin/spark-config.sh"
#3.执行${SPARK_HOME}/bin/load-spark-env.sh，见上述分析
. "${SPARK_HOME}/bin/load-spark-env.sh"
#4.exec "${SPARK_HOME}/sbin"/spark-daemon.sh start org.apache.spark.deploy.ExternalShuffleService 1 ，见上诉分析
exec "${SPARK_HOME}/sbin"/spark-daemon.sh start org.apache.spark.deploy.ExternalShuffleService 1

2.3　start-thriftserver.sh

开启thriftserver，略

三、spark-submit处理逻辑分析

以上主要是介绍了spark启动的一些脚本，这里主要分析一下Spark源码中提交任务脚本的处理逻辑，从spark-submit一步步深入进去看看任务提交的整体流程,首先看一下整体的流程概要图：

3.1　spark-submit

# -z是检查后面变量是否为空（空则真） shell可以在双引号之内引用变量，单引号不可
#这一步作用是检查SPARK_HOME变量是否为空，为空则执行then后面程序
#source命令： source filename作用在当前bash环境下读取并执行filename中的命令
#$0代表shell脚本文件本身的文件名，这里即使spark-submit
#dirname用于取得脚本文件所在目录 dirname $0取得当前脚本文件所在目录
#$(命令)表示返回该命令的结果
#故整个if语句的含义是：如果SPARK_HOME变量没有设置值，则执行当前目录下的find-spark-home脚本文件，设置SPARK_HOME值
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
fi

# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0
#执行spark-class脚本，传递参数org.apache.spark.deploy.SparkSubmit 和"$@"
#这里$@表示之前spark-submit接收到的全部参数
exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"

所以spark-submit脚本的整体逻辑就是： 
首先 检查SPARK_HOME是否设置；if 已经设置 执行spark-class文件 否则加载执行find-spark-home文件 

3.2　find-spark-home

#定义一个变量用于后续判断是否存在定义SPARK_HOME的python脚本文件
FIND_SPARK_HOME_PYTHON_SCRIPT="$(cd "$(dirname "$0")"; pwd)/find_spark_home.py"

# Short cirtuit if the user already has this set.
##如果SPARK_HOME为不为空值，成功退出程序
if [ ! -z "${SPARK_HOME}" ]; then
   exit 0
# -f用于判断这个文件是否存在并且是否为常规文件，是的话为真，这里不存在为假，执行下面语句，给SPARK_HOME变量赋值
elif [ ! -f "$FIND_SPARK_HOME_PYTHON_SCRIPT" ]; then
  # If we are not in the same directory as find_spark_home.py we are not pip installed so we don't
  # need to search the different Python directories for a Spark installation.
  # Note only that, if the user has pip installed PySpark but is directly calling pyspark-shell or
  # spark-submit in another directory we want to use that version of PySpark rather than the
  # pip installed version of PySpark.
  export SPARK_HOME="$(cd "$(dirname "$0")"/..; pwd)"
else
  # We are pip installed, use the Python script to resolve a reasonable SPARK_HOME
  # Default to standard python interpreter unless told otherwise
  if [[ -z "$PYSPARK_DRIVER_PYTHON" ]]; then
     PYSPARK_DRIVER_PYTHON="${PYSPARK_PYTHON:-"python"}"
  fi
  export SPARK_HOME=$($PYSPARK_DRIVER_PYTHON "$FIND_SPARK_HOME_PYTHON_SCRIPT")
fi

可以看到，如果事先用户没有设定SPARK_HOME的值，这里程序也会自动设置并且将其注册为环境变量，供后面程序使用

当SPARK_HOME的值设定完成之后，就会执行Spark-class文件，这也是我们分析的重要部分，源码如下：

3.3　spark-class

#!/usr/bin/env bash
#依旧是检查设置SPARK_HOME的值
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
fi
#执行load-spark-env.sh脚本文件，主要目的在于加载设定一些变量值
#设定spark-env.sh中的变量值到环境变量中，供后续使用
#设定scala版本变量值
. "${SPARK_HOME}"/bin/load-spark-env.sh

# Find the java binary
#检查设定java环境值
#-n代表检测变量长度是否为0，不为0时候为真
#如果已经安装Java没有设置JAVA_HOME,command -v java返回的值为${JAVA_HOME}/bin/java
if [ -n "${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

# Find Spark jars.
#-d检测文件是否为目录，若为目录则为真
#设置一些关联Class文件
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

if [ ! -d "$SPARK_JARS_DIR" ] && [ -z "$SPARK_TESTING$SPARK_SQL_TESTING" ]; then
  echo "Failed to find Spark jars directory ($SPARK_JARS_DIR)." 1>&2
  echo "You need to build Spark with the target \"package\" before running this program." 1>&2
  exit 1
else
  LAUNCH_CLASSPATH="$SPARK_JARS_DIR/*"
fi

# Add the launcher build dir to the classpath if requested.
if [ -n "$SPARK_PREPEND_CLASSES" ]; then
  LAUNCH_CLASSPATH="${SPARK_HOME}/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH"
fi

# For tests
if [[ -n "$SPARK_TESTING" ]]; then
  unset YARN_CONF_DIR
  unset HADOOP_CONF_DIR
fi

# The launcher library will print arguments separated by a NULL character, to allow arguments with
# characters that would be otherwise interpreted by the shell. Read that in a while loop, populating
# an array that will be used to exec the final command.
#
# The exit code of the launcher is appended to the output, so the parent shell removes it from the
# command array and checks the value to see if the launcher succeeded.
#执行类文件org.apache.spark.launcher.Main，返回解析后的参数
build_command() {
  "$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
  printf "%d\0" $?
}

# Turn off posix mode since it does not allow process substitution
#将build_command方法解析后的参数赋给CMD
set +o posix
CMD=()
while IFS= read -d '' -r ARG; do
  CMD+=("$ARG")
done < <(build_command "$@")

COUNT=${#CMD[@]}
LAST=$((COUNT - 1))
LAUNCHER_EXIT_CODE=${CMD[$LAST]}

# Certain JVM failures result in errors being printed to stdout (instead of stderr), which causes
# the code that parses the output of the launcher to get confused. In those cases, check if the
# exit code is an integer, and if it's not, handle it as a special error case.
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}")
#执行CMD中的某个参数类org.apache.spark.deploy.SparkSubmit
exec "${CMD[@]}"

spark-class文件的执行逻辑稍显复杂，总体上应该是这样的：

检查SPARK_HOME的值----》执行load-spark-env.sh文件，设定一些需要用到的环境变量，如scala环境值，这其中也加载了spark-env.sh文件-------》检查设定java的执行路径变量值-------》寻找spark jars,设定一些引用相关类的位置变量------》执行类文件org.apache.spark.launcher.Main，返回解析后的参数给CMD-------》判断解析参数是否正确（代表了用户设置的参数是否正确）--------》正确的话执行org.apache.spark.deploy.SparkSubmit这个类

3.4　SparkSubmit 

2.1最后提交语句，D:\src\spark-2.3.0\core\src\main\scala\org\apache\spark\deploy\SparkSubmit.scala

exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"

 

override def main(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)
    //拿到submit脚本传入的参数
    val appArgs = new SparkSubmitArguments(args)
    if (appArgs.verbose) {
      // scalastyle:off println
      printStream.println(appArgs)
      // scalastyle:on println
    }
    //根据传入的参数匹配对应的执行方法
    appArgs.action match {
        //根据传入的参数提交命令
      case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)
        //只有standalone和mesos集群模式才触发
      case SparkSubmitAction.KILL => kill(appArgs)
      //只有standalone和mesos集群模式才触发
      case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
    }
  }

3.4.1　submit十分关键，主要分为两步骤

（1）调用prepareSubmitEnvironment

（2）调用doRunMain