Spark2.1.0——剖析spark-shell

        在《Spark2.1.0——運行環境準備》一文介紹瞭如何準備基本的Spark運行環境，並在《Spark2.1.0——Spark初體驗》一文經過在spark-shell中執行word count的過程，讓讀者瞭解到可使用spark-shell提交Spark做業。如今讀者應該很想知道spark-shell究竟作了什麼呢？

腳本分析

        在Spark安裝目錄的bin文件夾下能夠找到spark-shell，其中有代碼清單1-1所示的一段腳本。

代碼清單1-1       spark-shell腳本

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
}

 

咱們看到腳本spark-shell裏執行了spark-submit腳本，那麼打開spark-submit腳本，發現代碼清單1-2中所示的腳本。

代碼清單1-2        spark-submit腳本

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

 

能夠看到spark-submit中又執行了腳本spark-class。打開腳本spark-class，首先發現如下一段腳本：

# 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

 

上面的腳本是爲了找到Java命令。在spark-class腳本中還會找到如下內容：

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

CMD=()
while IFS= read -d '' -r ARG; do
  CMD+=("$ARG")
done < <(build_command "$@")

 

根據代碼清單1-2，腳本spark-submit在執行spark-class腳本時，給它增長了參數SparkSubmit 。因此讀到這，應該知道Spark啓動了以SparkSubmit爲主類的JVM進程。

遠程監控

 

        爲便於在本地對Spark進程進行遠程監控，在spark-shell腳本中找到如下配置：

SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Dscala.usejavacp=true"

並追加如下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

若是Spark安裝在其餘機器，那麼在本地打開jvisualvm後須要添加遠程主機，如圖1所示：

圖1  添加遠程主機

右鍵單擊已添加的遠程主機，添加JMX鏈接，如圖2：

圖2  添加JMX鏈接

        若是Spark安裝在本地，那麼打開jvisualvm後就會在應用程序窗口看到org.apache.spark.deploy.SparkSubmit進程，只需雙擊便可。

        選擇右側的「線程」選項卡，選擇main線程，而後點擊「線程Dump」按鈕，如圖3。

圖3 查看Spark線程

 

從線程Dump的內容中找到線程main的信息如代碼清單1-3所示。

代碼清單1-3       main線程的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)

從main線程的棧信息中看出程序的調用順序：SparkSubmit.main→repl.Main→Iloop.process。

源碼分析

咱們根據上面的線索，直接閱讀Iloop的process方法的源碼（Iloop是Scala語言自身的類庫中的用於實現交互式shell的實現類，提供對REPL（Read-eval-print-loop）的實現），見代碼清單1-4。

代碼清單1-4       process的實現

  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
  }

根據代碼清單1-4，Iloop的process方法調用了loadFiles方法。Spark中的SparkILoop繼承了Iloop並重寫了loadFiles方法，其實現以下：

  override def loadFiles(settings: Settings): Unit = {
    initializeSpark()
    super.loadFiles(settings)
  }

根據上面展現的代碼，loadFiles方法調用了SparkILoop的initializeSpark方法，initializeSpark的實現見代碼清單1-5。

代碼清單1-5        initializeSpark的實現

  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.
    }
  }

咱們看到initializeSpark向交互式shell發送了一大串代碼，Scala的交互式shell將調用org.apache.spark.repl.Main的createSparkSession方法（見代碼清單1-6）建立SparkSession。咱們看到常量spark將持有SparkSession的引用，而且sc持有SparkSession內部初始化好的SparkContext。因此咱們纔可以在spark-shell的交互式shell中使用sc和spark。

代碼清單1-6        createSparkSession的實現

  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
  }

根據代碼清單1-6，createSparkSession方法經過SparkSession的API建立SparkSession實例。本書將有關SparkSession等API的內容在《Spark內核設計的藝術》一書的第10章講解，初次接觸Spark的讀者如今只須要了解便可。

 

關於《Spark內核設計的藝術 架構設計與實現》

通過近一年的準備，基於Spark2.1.0版本的《Spark內核設計的藝術 架構設計與實現》一書現已出版發行，圖書如圖：

 

紙質版售賣連接以下：

京東： https://item.jd.com/12302500.html