通过在spark-shell中执行word count的过程，让读者了解到可以使用spark-shell提交Spark作业。现在读者应该很想知道spark-shell究竟做了什么呢？
脚本分析
在Spark安装目录的bin文件夹下可以找到spark-shell，其中有代码清单1-1所示的一段脚本。
代码清单1-1 spark-shell脚本
function main() {
if $cygwin; then
stty -icanon min 1 -echo > /dev/null 2>&1
export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
"${SPARK_HOME}"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@"
stty icanon echo > /dev/null 2>&1
else
export SPARK_SUBMIT_OPTS
"${SPARK_HOME}"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@"
fi
}我们看到脚本spark-shell里执行了spark-submit脚本，那么打开spark-submit脚本，发现代码清单1-2中所示的脚本。
代码清单1-2 spark-submit脚本
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 "$@"可以看到spark-submit中又执行了脚本spark-class。打开脚本spark-class，首先发现以下一段脚本：
# Find the java binary
if [ -n "${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上面的脚本是为了找到Java命令。在spark-class脚本中还会找到以下内容：
build_command() {
"$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
printf "%d\0" $?
}
CMD=()
while IFS= read -d '' -r ARG; do
CMD+=("$ARG")
done < <(build_command "$@")根据代码清单1-2，脚本spark-submit在执行spark-class脚本时，给它增加了参数SparkSubmit 。所以读到这，应该知道Spark启动了以SparkSubmit为主类的JVM进程。
远程监控
为便于在本地对Spark进程进行远程监控，在spark-shell脚本中找到以下配置：
SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Dscala.usejavacp=true"
并追加以下jmx配置：
-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.port=10207 -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false
如果Spark安装在其他机器，那么在本地打开jvisualvm后需要添加远程主机，如图1所示：
图1 添加远程主机
右键单击已添加的远程主机，添加JMX连接，如图2：
图2 添加JMX连接
如果Spark安装在本地，那么打开jvisualvm后就会在应用程序窗口看到org.apache.spark.deploy.SparkSubmit进程，只需双击即可。
选择右侧的“线程”选项卡，选择main线程，然后点击“线程Dump”按钮，如图3。
图3 查看Spark线程
从线程Dump的内容中找到线程main的信息如代码清单1-3所示。
代码清单1-3 main线程的Dump信息
"main" #1 prio=5 os_prio=31 tid=0x00007fa012802000 nid=0x1303 runnable [0x000000010d11c000] java.lang.Thread.State: RUNNABLE at java.io.FileInputStream.read0(Native Method) at java.io.FileInputStream.read(FileInputStream.java:207) at jline.internal.NonBlockingInputStream.read(NonBlockingInputStream.java:169) - locked <0x00000007837a8ab8> (a jline.internal.NonBlockingInputStream) at jline.internal.NonBlockingInputStream.read(NonBlockingInputStream.java:137) at jline.internal.NonBlockingInputStream.read(NonBlockingInputStream.java:246) at jline.internal.InputStreamReader.read(InputStreamReader.java:261) - locked <0x00000007837a8ab8> (a jline.internal.NonBlockingInputStream) at jline.internal.InputStreamReader.read(InputStreamReader.java:198) - locked <0x00000007837a8ab8> (a jline.internal.NonBlockingInputStream) at jline.console.ConsoleReader.readCharacter(ConsoleReader.java:2145) at jline.console.ConsoleReader.readLine(ConsoleReader.java:2349) at jline.console.ConsoleReader.readLine(ConsoleReader.java:2269) at scala.tools.nsc.interpreter.jline.InteractiveReader.readOneLine(JLineReader.scala:57) at scala.tools.nsc.interpreter.InteractiveReader$$anonfun$readLine$2.apply(InteractiveReader.scala:37) at scala.tools.nsc.interpreter.InteractiveReader$$anonfun$readLine$2.apply(InteractiveReader.scala:37) at scala.tools.nsc.interpreter.InteractiveReader$.restartSysCalls(InteractiveReader.scala:44) at scala.tools.nsc.interpreter.InteractiveReader$class.readLine(InteractiveReader.scala:37) at scala.tools.nsc.interpreter.jline.InteractiveReader.readLine(JLineReader.scala:28) at scala.tools.nsc.interpreter.ILoop.readOneLine(ILoop.scala:404) at scala.tools.nsc.interpreter.ILoop.loop(ILoop.scala:413) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:923) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909) at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97) at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909) at org.apache.spark.repl.Main$.doMain(Main.scala:68) at org.apache.spark.repl.Main$.main(Main.scala:51) at org.apache.spark.repl.Main.main(Main.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738) at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187) at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
从main线程的栈信息中看出程序的调用顺序：SparkSubmit.mainrepl.MainIloop.process。
源码分析
我们根据上面的线索，直接阅读Iloop的process方法的源码（Iloop是Scala语言自身的类库中的用于实现交互式shell的实现类，提供对REPL（Read-eval-print-loop）的实现），见代码清单1-4。
代码清单1-4 process的实现
def process(settings: Settings): Boolean = savingContextLoader {
this.settings = settings
createInterpreter()
// sets in to some kind of reader depending on environmental cues
in = in0.fold(chooseReader(settings))(r => SimpleReader(r, out, interactive = true))
globalFuture = future {
intp.initializeSynchronous()
loopPostInit()
!intp.reporter.hasErrors
}
loadFiles(settings)
printWelcome()
try loop() match {
case LineResults.EOF => out print Properties.shellInterruptedString
case _ =>
}
catch AbstractOrMissingHandler()
finally closeInterpreter()
true
}根据代码清单1-4，Iloop的process方法调用了loadFiles方法。Spark中的SparkILoop继承了Iloop并重写了loadFiles方法，其实现如下：
override def loadFiles(settings: Settings): Unit = {
initializeSpark()
super.loadFiles(settings)
}根据上面展示的代码，loadFiles方法调用了SparkILoop的initializeSpark方法，initializeSpark的实现见代码清单1-5。
代码清单1-5 initializeSpark的实现
def initializeSpark() {
intp.beQuietDuring {
processLine("""
@transient val spark = if (org.apache.spark.repl.Main.sparkSession != null) {
org.apache.spark.repl.Main.sparkSession
} else {
org.apache.spark.repl.Main.createSparkSession()
}
@transient val sc = {
val _sc = spark.sparkContext
if (_sc.getConf.getBoolean("spark.ui.reverseProxy", false)) {
val proxyUrl = _sc.getConf.get("spark.ui.reverseProxyUrl", null)
if (proxyUrl != null) {
println(s"Spark Context Web UI is available at ${proxyUrl}/proxy/${_sc.applicationId}")
} else {
println(s"Spark Context Web UI is available at Spark Master Public URL")
}
} else {
_sc.uiWebUrl.foreach {
webUrl => println(s"Spark context Web UI available at ${webUrl}")
}
}
println("Spark context available as 'sc' " +
s"(master = ${_sc.master}, app id = ${_sc.applicationId}).")
println("Spark session available as 'spark'.")
_sc
}
""")
processLine("import org.apache.spark.SparkContext._")
processLine("import spark.implicits._")
processLine("import spark.sql")
processLine("import org.apache.spark.sql.functions._")
replayCommandStack = Nil // remove above commands from session history.
}
}我们看到initializeSpark向交互式shell发送了一大串代码，Scala的交互式shell将调用org.apache.spark.repl.Main的createSparkSession方法（见代码清单1-6）创建SparkSession。我们看到常量spark将持有SparkSession的引用，并且sc持有SparkSession内部初始化好的SparkContext。所以我们才能够在spark-shell的交互式shell中使用sc和spark。
代码清单1-6 createSparkSession的实现
def createSparkSession(): SparkSession = {
val execUri = System.getenv("SPARK_EXECUTOR_URI")
conf.setIfMissing("spark.app.name", "Spark shell")
conf.set("spark.repl.class.outputDir", outputDir.getAbsolutePath())
if (execUri != null) {
conf.set("spark.executor.uri", execUri)
}
if (System.getenv("SPARK_HOME") != null) {
conf.setSparkHome(System.getenv("SPARK_HOME"))
}
val builder = SparkSession.builder.config(conf)
if (conf.get(CATALOG_IMPLEMENTATION.key, "hive").toLowerCase == "hive") {
if (SparkSession.hiveClassesArePresent) {
sparkSession = builder.enableHiveSupport().getOrCreate()
logInfo("Created Spark session with Hive support")
} else {
builder.config(CATALOG_IMPLEMENTATION.key, "in-memory")
sparkSession = builder.getOrCreate()
logInfo("Created Spark session")
}
} else {
sparkSession = builder.getOrCreate()
logInfo("Created Spark session")
}
sparkContext = sparkSession.sparkContext
sparkSession
}根据代码清单1-6，createSparkSession方法通过SparkSession的API创建SparkSession实例。本书将有关SparkSession等API的内容在《Spark内核设计的艺术》一书的第10章讲解，初次接触Spark的读者现在只需要了解即可。