spark shell 刪除失效_Spark任務提交源碼解析

1. 前言

反反復復搗鼓了很久，終于開始學習Spark的源碼了，果不其然，那真的很有趣。這里我打算一本正經的胡說八道來講一下Spark作業的提交過程。

基礎mac系統基礎環境如下：

JDK 1.8
IDEA 2019.3
源碼Spark 2.3.3
Scala 2.11.8
提交腳本
# 事先準備好的Spark任務(源碼example LocalPi)基于local模式
bash spark-submit
--class com.lp.test.app.LocalPi
--master local
/Users/lipan/Desktop/spark-local/original-spark-local-train-1.0.jar
10

2. 提交流程

我們在提交Spark任務時都是從spark-submit(或者spark-shell)來提交一個作業的，從spark-submit腳本一步步深入進去看看任務的整體提交流程。首先看一下整體的流程概要圖：

根據上圖中的整體流程，接下來我們對里面的每一個流程的源碼進行一一剖析跟蹤。

2.1 spark-submit腳本

#!/usr/bin/env bash## 如果SPARK_HOME變量沒有設置值，則執行當前目錄下的find-spark-home腳本文件，設置SPARK_HOME值if [ -z "${SPARK_HOME}" ]; then  source "$(dirname "$0")"/find-spark-homefiecho "${SPARK_HOME}"# disable randomized hash for string in Python 3.3+export PYTHONHASHSEED=0# 這里可以看到將接收到的參數提交到了spark-class腳本執行exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"

2.2 spark-class腳本

#!/usr/bin/env bashif [ -z "${SPARK_HOME}" ]; then  source "$(dirname "$0")"/find-spark-homefi# 配置一些環境變量,它會將conf/spark-env.sh中的環境變量加載進來:. "${SPARK_HOME}"/bin/load-spark-env.sh# Find the java binary  如果有java_home環境變量會將java_home/bin/java給RUNNERif [ -n "${JAVA_HOME}" ]; then  RUNNER="${JAVA_HOME}/bin/java"else  if [ "$(command -v java)" ]; then    RUNNER="java"  else    echo "JAVA_HOME is not set" >&2    exit 1  fifi# Find Spark jars.# 這一段，主要是尋找java命令 尋找spark的jar包# 這里如果我們的jar包數量多，而且內容大，可以事先放到每個機器的對應目錄下，這里是一個優化點if [ -d "${SPARK_HOME}/jars" ]; then  SPARK_JARS_DIR="${SPARK_HOME}/jars"else  SPARK_JARS_DIR="${SPARK_HOME}/assembly/target/scala-$SPARK_SCALA_VERSION/jars"fiif [ ! -d "$SPARK_JARS_DIR" ] && [ -z "$SPARK_TESTING$SPARK_SQL_TESTING" ]; then  echo "Failed to find Spark jars directory ($SPARK_JARS_DIR)." 1>&2  echo "You need to build Spark with the target "package" before running this program." 1>&2  exit 1else  LAUNCH_CLASSPATH="$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 testsif [[ -n "$SPARK_TESTING" ]]; then  unset YARN_CONF_DIR  unset HADOOP_CONF_DIRfi# The launcher library will print arguments separated by a NULL character, to allow arguments with# characters that would be otherwise interpreted by the shell. Read that in a while loop, populating# an array that will be used to exec the final command.# 啟動程序庫將打印由NULL字符分隔的參數，以允許與shell進行其他解釋的字符進行參數。在while循環中讀取它，填充將用于執行最終命令的數組。## The exit code of the launcher is appended to the output, so the parent shell removes it from the# command array and checks the value to see if the launcher succeeded.# 啟動程序的退出代碼被追加到輸出，因此父shell從命令數組中刪除它，并檢查其值，看看啟動器是否成功。# 這里spark啟動了以SparkSubmit為主類的JVM進程。build_command() {  "$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"  printf "%d0" $?}# Turn off posix mode since it does not allow process substitution# 關閉posix模式，因為它不允許進程替換。# 調用build_command org.apache.spark.launcher.Main拼接提交命令set +o posixCMD=()while IFS= read -d '' -r ARG; do  CMD+=("$ARG")done < &2  exit 1fiif [ $LAUNCHER_EXIT_CODE != 0 ]; then  exit $LAUNCHER_EXIT_CODEfiCMD=("${CMD[@]:0:$LAST}")# ${CMD[@]} 參數如下# /Library/Java/JavaVirtualMachines/jdk1.8.0_172.jdk/Contents/Home/bin/java -cp /Users/lipan/workspace/source_code/spark-2.3.3/conf/:/Users/lipan/workspace/source_code/spark-2.3.3/assembly/target/scala-2.11/jars/* -Xmx1g org.apache.spark.deploy.SparkSubmit --master local --class com.lp.test.app.LocalPi /Users/lipan/Desktop/spark-local/original-spark-local-train-1.0.jar 10exec "${CMD[@]}"

相對于spark-submit，spark-class文件的執行邏輯稍顯復雜，總體如下：

檢查SPARK_HOME執行環境
執行load-spark-env.sh文件，加載一些默認的環境變量(包括加載spark-env.sh文件)
檢查JAVA_HOME執行環境
尋找Spark相關的jar包
執行org.apache.spark.launcher.Main解析參數，構建CMD命令
CMD命令判斷
執行org.apache.spark.deploy.SparkSubmit這個類。

2.3 org.apache.spark.launcher.Main

java -Xmx128m -cp ...jars org.apache.spark.launcher.Main "$@"

也就是說org.apache.spark.launcher.Main是被spark-class調用，從spark-class接收參數。這個類是提供spark內部腳本調用的工具類，并不是真正的執行入口。它負責調用其他類，對參數進行解析，并生成執行命令，最后將命令返回給spark-class的 exec “${CMD[@]}”執行。

可以把”$@”執行相關參數帶入IDEA中的org.apache.spark.launcher.Main方法中執行，操作參考如下：

package org.apache.spark.launcher;import java.util.ArrayList;import java.util.Arrays;import java.util.HashMap;import java.util.List;import java.util.Map;import static org.apache.spark.launcher.CommandBuilderUtils.*;/** * Command line interface for the Spark launcher. Used internally by Spark scripts. * 這是提供spark內部腳本使用工具類 */ class Main {    /**     * Usage: Main [class] [class args]     * 分為spark-submit和spark-class兩種模式     * 如果提交的是class類的話,會包含其他如:master/worker/history等等     * unix系統的輸出的參數是集合,而windows參數是空格分隔     *     * spark-class提交過來的參數如下：     * org.apache.spark.deploy.SparkSubmit      * --class com.lp.test.app.LocalPi      * --master local      * /Users/lipan/Desktop/spark-local/spark-local-train-1.0.jar     */    public static void main(String[] argsArray) throws Exception {        checkArgument(argsArray.length > 0, "Not enough arguments: missing class name.");        // 判斷參數列表        List args = new ArrayList<>(Arrays.asList(argsArray));        String className = args.remove(0);        // 判斷是否打印執行信息        boolean printLaunchCommand = !isEmpty(System.getenv("SPARK_PRINT_LAUNCH_COMMAND"));        // 創建命令解析器        AbstractCommandBuilder builder;        /**         * 構建執行程序對象:spark-submit/spark-class         * 把參數都取出并解析,放入執行程序對象中         * 意思是,submit還是master和worker等程序在這里拆分,并獲取對應的執行參數         */        if (className.equals("org.apache.spark.deploy.SparkSubmit")) {            try {                // 構建spark-submit命令對象                builder = new SparkSubmitCommandBuilder(args);            } catch (IllegalArgumentException e) {                printLaunchCommand = false;                System.err.println("Error: " + e.getMessage());                System.err.println();                // 類名解析--class org.apache.spark.repl.Main                MainClassOptionParser parser = new MainClassOptionParser();                try {                    parser.parse(args);                } catch (Exception ignored) {                    // Ignore parsing exceptions.                }                // 幫助信息                List help = new ArrayList<>();                if (parser.className != null) {                    help.add(parser.CLASS);                    help.add(parser.className);                }                help.add(parser.USAGE_ERROR);                // 構建spark-submit幫助信息對象                builder = new SparkSubmitCommandBuilder(help);            }        } else {            // 構建spark-class命令對象            // 主要是在這個類里解析了命令對象和參數            builder = new SparkClassCommandBuilder(className, args);        }        /**         * 這里才真正構建了執行命令         * 調用了SparkClassCommandBuilder的buildCommand方法         * 把執行參數解析成了k/v格式         */        Map env = new HashMap<>();        List cmd = builder.buildCommand(env);        if (printLaunchCommand) {            System.err.println("Spark Command: " + join(" ", cmd));            System.err.println("========================================");        }        if (isWindows()) {            System.out.println(prepareWindowsCommand(cmd, env));        } else {            // In bash, use NULL as the arg separator since it cannot be used in an argument.            /**             * 輸出參數：/Library/Java/JavaVirtualMachines/jdk1.8.0_172.jdk/Contents/Home/bin/java             * -cp /Users/lipan/workspace/source_code/spark-2.3.3/conf/:/Users/lipan/workspace/source_code/spark-2.3.3/assembly/target/scala-2.11/jars/*             * -Xmx1g org.apache.spark.deploy.SparkSubmit             * --master local             * --class com.lp.test.app.LocalPi             * /Users/lipan/Desktop/spark-local/original-spark-local-train-1.0.jar 10             *  java -cp / org.apache.spark.deploy.SparkSubmit啟動該類             */            List bashCmd = prepareBashCommand(cmd, env);            for (String c : bashCmd) {                System.out.print(c);                System.out.print('0');            }        }    }    /**     * windows環境下     */    private static String prepareWindowsCommand(List cmd, Map childEnv) {        StringBuilder cmdline = new StringBuilder();        for (Map.Entry e : childEnv.entrySet()) {            cmdline.append(String.format("set %s=%s", e.getKey(), e.getValue()));            cmdline.append(" && ");        }        for (String arg : cmd) {            cmdline.append(quoteForBatchScript(arg));            cmdline.append(" ");        }        return cmdline.toString();    }    /**     * bash環境，如：Linux     */    private static List prepareBashCommand(List cmd, Map childEnv) {        if (childEnv.isEmpty()) {            return cmd;        }        List newCmd = new ArrayList<>();        newCmd.add("env");        for (Map.Entry e : childEnv.entrySet()) {            newCmd.add(String.format("%s=%s", e.getKey(), e.getValue()));        }        newCmd.addAll(cmd);        return newCmd;    }    /**     * 當spark-submit提交失敗時,這里會再進行一次解析,再不行才會提示用法     */    private static class MainClassOptionParser extends SparkSubmitOptionParser {        String className;        @Override        protected boolean handle(String opt, String value) {            if (CLASS.equals(opt)) {                className = value;            }            return false;        }        @Override        protected boolean handleUnknown(String opt) {            return false;        }        @Override        protected void handleExtraArgs(List extra) {        }    }   }

Main中主要涉及到的一些類SparkSubmitCommandBuilder、SparkClassCommandBuilder 和 buildCommand都是在對參數和構建命令進行處理，這里不一一展開詳解。

2.4 org.apache.spark.deploy.SparkSubmit

org.apache.spark.launcher.Main中會解析過濾參數，構建執行命令，返回給spark-class腳本，最后通過 exec “${CMD[@]}” 真正調用SparkSubmit類。

可通過解析后提交的參數”$@”設置在IDEA中逐步跟蹤源碼，操作參考如下：

2.4.1 SparkSubmitAction

在org.apache.spark.launcher.Main類的最前面定義了一個類SparkSubmitAction枚舉狀態類。

/** * Whether to submit, kill, or request the status of an application. * The latter two operations are currently supported only for standalone and Mesos cluster modes. * 這個類主要是提交app,終止和請求狀態,但目前終止和請求只能在standalone和mesos模式下 */// 繼承了枚舉類,定義了4個屬性,多了一個打印spark版本private[deploy] object SparkSubmitAction extends Enumeration {  type SparkSubmitAction = Value  val SUBMIT, KILL, REQUEST_STATUS, PRINT_VERSION = Value}

2.4.2 SparkSubmit

在SparkSubmit類中的方法執行可參考如下，在每個方法中都有詳細的注釋。具體細節也可以根據文末的鏈接地址中載源碼斷進行斷點調試。

2.4.2.1 Main

override def main(args: Array[String]): Unit = {    // 初始化logging系統,并跟日志判斷是否需要在app啟動時重啟    val uninitLog = initializeLogIfNecessary(true, silent = true)    /**     * 構建spark提交需要的參數并進行賦值 SparkSubmitArguments     * 1.解析參數     * 2.從屬性文件填充“sparkProperties”映射(未指定默認情況下未：spark-defaults.conf)     * 3.移除不是以"spark." 開頭的變量     * 4.參數填充對應到實體屬性上     * 5.action參數驗證     */    val appArgs = new SparkSubmitArguments(args)    // 參數不重復則輸出配置    if (appArgs.verbose) {      printStream.println(appArgs)    }    appArgs.action match {      case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)      case SparkSubmitAction.KILL => kill(appArgs)      case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)    }  }

2.4.2.2 submit

  /**   * 通過匹配SUBMIT執行的submit()   *   * 首先是根據不同調度模式和yarn不同模式,導入調用類的路徑,默認配置及輸入參數,準備相應的啟動環境   * 然后通過對應的環境來調用相應子類的main方法   * 這里因為涉及到重復調用,所以采用了@tailrec尾遞歸,即重復調用方法的最后一句并返回結果   * 即:runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)   */  @tailrec  private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {    /**     * 先準備運行環境,傳入解析的各種參數     * 這里會先進入     * lazy val secMgr = new SecurityManager(sparkConf)     * 先初始化SecurityManager后,再進入prepareSubmitEnvironment()     * prepareSubmitEnvironment()代碼比較長,放到最下面去解析     */    val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)    // 主要是調用runMain()啟動相應環境的main()的方法    // 環境準備好以后,會先往下運行判斷,這里是在等著調用    def doRunMain(): Unit = {      // 提交時可以指定--proxy-user,如果沒有指定,則獲取當前用戶      if (args.proxyUser != null) {        val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,          UserGroupInformation.getCurrentUser())        try {          proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {            override def run(): Unit = {              // 這里是真正的執行,runMain()              runMain(childArgs, childClasspath, sparkConf, 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 {        // 沒有指定用戶時執行        runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)      }    }    // 啟動main后重新初始化logging    if (uninitLog) {      Logging.uninitialize()    }    // standalone模式有兩種提交網關,    // (1)使用o.a.s.apply.client作為包裝器的傳統RPC網關和基于REST服務的網關    // (2)spark1.3后默認使用REST    // 如果master終端沒有使用REST服務,spark會故障切換到RPC 這里判斷standalone模式和使用REST服務    if (args.isStandaloneCluster && args.useRest) {      // 異常捕獲,判斷正確的話輸出信息,進入doRunMain()      try {        logInfo("Running Spark using the REST application submission protocol.")        doRunMain()      } catch {        // Fail over to use the legacy submission gateway        // 否則異常輸出信息,并設置submit失敗        case e: SubmitRestConnectionException =>          logWarning(s"Master endpoint ${args.master} was not a REST server. " +            "Falling back to legacy submission gateway instead.")          args.useRest = false          submit(args, false)      }      // In all other modes, just run the main class as prepared      // 其他模式,按準備的環境調用上面的doRunMain()運行相應的main()      // 在進入前,初始化了SparkContext和SparkSession    } else {      doRunMain()    }  }

2.4.2.3 prepareSubmitEnvironment

  /**   * 準備各種模式的配置參數   *   * @param args 用于環境準備的已分析SparkSubmitArguments   * @param conf 在Hadoop配置中，僅在單元測試中設置此參數。   * @return a 4-tuple:   *         (1) the arguments for the child process,   *         (2) a list of classpath entries for the child,   *         (3) a map of system properties, and   *         (4) the main class for the child   *         返回一個4元組(childArgs, childClasspath, sparkConf, childMainClass)   *         childArgs：子進程的參數   *         childClasspath：子級的類路徑條目列表   *         sparkConf：系統參數map集合   *         childMainClass：子級的主類   *   *         Exposed for testing.   *   *         由于不同的部署方式其賣弄函數是不一樣的，主要是由spark的提交參數決定   */  private[deploy] def prepareSubmitEnvironment(                                                args: SparkSubmitArguments,                                                conf: Option[HadoopConfiguration] = None)  : (Seq[String], Seq[String], SparkConf, String) = {    try {      doPrepareSubmitEnvironment(args, conf)    } catch {      case e: SparkException =>        printErrorAndExit(e.getMessage)        throw e    }  }    private def doPrepareSubmitEnvironment(                                          args: SparkSubmitArguments,                                          conf: Option[HadoopConfiguration] = None)  : (Seq[String], Seq[String], SparkConf, String) = {    // Return values    val childArgs = new ArrayBuffer[String]()    val childClasspath = new ArrayBuffer[String]()    // SparkConf 會默認加一些系統參數    val sparkConf = new SparkConf()    var childMainClass = ""    // 設置集群模式    // 也就是提交時指定--master local/yarn/yarn-client/yarn-cluster/spark://192.168.2.1:7077或者 mesos,k8s等運行模式    val clusterManager: Int = args.master match {      case "yarn" => YARN      case "yarn-client" | "yarn-cluster" =>        printWarning(s"Master ${args.master} is deprecated since 2.0." +          " Please use master "yarn" with specified deploy mode instead.")        YARN      case m if m.startsWith("spark") => STANDALONE      case m if m.startsWith("mesos") => MESOS      case m if m.startsWith("k8s") => KUBERNETES      case m if m.startsWith("local") => LOCAL      case _ =>        printErrorAndExit("Master must either be yarn or start with spark, mesos, k8s, or local")        -1    }    // 設置部署模式 --deploy-mode    var deployMode: Int = args.deployMode match {      case "client" | null => CLIENT      case "cluster" => CLUSTER      case _ => printErrorAndExit("Deploy mode must be either client or cluster"); -1    }    //由于指定“yarn-cluster”和“yarn-client”的不受支持的方式封裝了主模式和部署模式，    // 因此我們有一些邏輯來推斷master和部署模式(如果只指定一種模式)，或者在它們不一致時提前退出    if (clusterManager == YARN) {      (args.master, args.deployMode) match {        case ("yarn-cluster", null) =>          deployMode = CLUSTER          args.master = "yarn"        case ("yarn-cluster", "client") =>          printErrorAndExit("Client deploy mode is not compatible with master "yarn-cluster"")        case ("yarn-client", "cluster") =>          printErrorAndExit("Cluster deploy mode is not compatible with master "yarn-client"")        case (_, mode) =>          args.master = "yarn"      }      // Make sure YARN is included in our build if we're trying to use it      if (!Utils.classIsLoadable(YARN_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {        printErrorAndExit(          "Could not load YARN classes. " +            "This copy of Spark may not have been compiled with YARN support.")      }    }    // 判斷k8s模式master和非testing模式    if (clusterManager == KUBERNETES) {      args.master = Utils.checkAndGetK8sMasterUrl(args.master)      // Make sure KUBERNETES is included in our build if we're trying to use it      if (!Utils.classIsLoadable(KUBERNETES_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {        printErrorAndExit(          "Could not load KUBERNETES classes. " +            "This copy of Spark may not have been compiled with KUBERNETES support.")      }    }    // 錯判斷不可用模式    (clusterManager, deployMode) match {      case (STANDALONE, CLUSTER) if args.isPython =>        printErrorAndExit("Cluster deploy mode is currently not supported for python " +          "applications on standalone clusters.")      case (STANDALONE, CLUSTER) if args.isR =>        printErrorAndExit("Cluster deploy mode is currently not supported for R " +          "applications on standalone clusters.")      case (KUBERNETES, _) if args.isPython =>        printErrorAndExit("Python applications are currently not supported for Kubernetes.")      case (KUBERNETES, _) if args.isR =>        printErrorAndExit("R applications are currently not supported for Kubernetes.")      case (KUBERNETES, CLIENT) =>        printErrorAndExit("Client mode is currently not supported for Kubernetes.")      case (LOCAL, CLUSTER) =>        printErrorAndExit("Cluster deploy mode is not compatible with master "local"")      case (_, CLUSTER) if isShell(args.primaryResource) =>        printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.")      case (_, CLUSTER) if isSqlShell(args.mainClass) =>        printErrorAndExit("Cluster deploy mode is not applicable to Spark SQL shell.")      case (_, CLUSTER) if isThriftServer(args.mainClass) =>        printErrorAndExit("Cluster deploy mode is not applicable to Spark Thrift server.")      case _ =>    }    // args.deployMode為空則設置deployMode值為參數,因為上面判斷了args.deployMode為空deployMode為client    (args.deployMode, deployMode) match {      case (null, CLIENT) => args.deployMode = "client"      case (null, CLUSTER) => args.deployMode = "cluster"      case _ =>    }    // 根據資源管理器和部署模式，進行邏輯判斷出幾種特殊運行方式。    val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER    val isMesosCluster = clusterManager == MESOS && deployMode == CLUSTER    val isStandAloneCluster = clusterManager == STANDALONE && deployMode == CLUSTER    val isKubernetesCluster = clusterManager == KUBERNETES && deployMode == CLUSTER    // 這里主要是添加相關的依賴    if (!isMesosCluster && !isStandAloneCluster) {      // 如果有maven依賴項，則解析它們，并將類路徑添加到jar中。對于包含Python代碼的包，也將它們添加到py文件中      val resolvedMavenCoordinates = DependencyUtils.resolveMavenDependencies(        args.packagesExclusions, args.packages, args.repositories, args.ivyRepoPath,        args.ivySettingsPath)      if (!StringUtils.isBlank(resolvedMavenCoordinates)) {        args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)        if (args.isPython || isInternal(args.primaryResource)) {          args.pyFiles = mergeFileLists(args.pyFiles, resolvedMavenCoordinates)        }      }      // 安裝任何可能通過--jar或--packages傳遞的R包。Spark包可能在jar中包含R源代碼。      if (args.isR && !StringUtils.isBlank(args.jars)) {        RPackageUtils.checkAndBuildRPackage(args.jars, printStream, args.verbose)      }    }    args.sparkProperties.foreach { case (k, v) => sparkConf.set(k, v) }    // sparkConf 加載Hadoop相關配置文件    val hadoopConf = conf.getOrElse(SparkHadoopUtil.newConfiguration(sparkConf))    // 工作臨時目錄    val targetDir = Utils.createTempDir()    //  判斷當前模式下sparkConf的k/v鍵值對中key是否在JVM中全局可用    // 確保keytab在JVM中的任何位置都可用(keytab是Kerberos的身份認證,詳情可參考：http://ftuto.lofter.com/post/31e97f_6ad659f)    if (clusterManager == YARN || clusterManager == LOCAL || clusterManager == MESOS) {      // 當前運行環境的用戶不為空,args中yarn模式參數key列表不為空,則提示key列表文件不存在      if (args.principal != null) {        if (args.keytab != null) {          require(new File(args.keytab).exists(), s"Keytab file: ${args.keytab} does not exist")          // 在sysProps中添加keytab和主體配置，以供以后使用;例如，在spark sql中，用于與HiveMetastore對話的隔離類裝入器將使用這些設置。          // 它們將被設置為Java系統屬性，然后由SparkConf加載          sparkConf.set(KEYTAB, args.keytab)          sparkConf.set(PRINCIPAL, args.principal)          UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab)        }      }    }    // Resolve glob path for different resources.    // 設置全局資源,也就是合并各種模式依賴的路徑的資源和hadoopConf中設置路徑的資源，各種jars,file,pyfile和壓縮包    args.jars = Option(args.jars).map(resolveGlobPaths(_, hadoopConf)).orNull    args.files = Option(args.files).map(resolveGlobPaths(_, hadoopConf)).orNull    args.pyFiles = Option(args.pyFiles).map(resolveGlobPaths(_, hadoopConf)).orNull    args.archives = Option(args.archives).map(resolveGlobPaths(_, hadoopConf)).orNull    // 創建SecurityManager實例    lazy val secMgr = new SecurityManager(sparkConf)    // 在Client模式下，下載遠程資源文件。    var localPrimaryResource: String = null    var localJars: String = null    var localPyFiles: String = null    if (deployMode == CLIENT) {      localPrimaryResource = Option(args.primaryResource).map {        downloadFile(_, targetDir, sparkConf, hadoopConf, secMgr)      }.orNull      localJars = Option(args.jars).map {        downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)      }.orNull      localPyFiles = Option(args.pyFiles).map {        downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)      }.orNull    }    // When running in YARN, for some remote resources with scheme:    //   1. Hadoop FileSystem doesn't support them.    //   2. We explicitly bypass Hadoop FileSystem with "spark.yarn.dist.forceDownloadSchemes".    // We will download them to local disk prior to add to YARN's distributed cache.    // For yarn client mode, since we already download them with above code, so we only need to    // figure out the local path and replace the remote one.    // yarn模式下,hdfs不支持加載到內存,所以采用"spark.yarn.dist.forceDownloadSchemes"方案(在添加到YARN分布式緩存之前，文件將被下載到本地磁盤的逗號分隔列表。用于YARN服務不支持Spark支持的方案的情況)    // 所以先把方案列表文件下載到本地,再通過相應方案加載資源到分布式內存中    // 在yarn-client模式中,上面的代碼中已經把遠程文件下載到了本地,只需要獲取本地路徑替換掉遠程路徑即可    if (clusterManager == YARN) {      // 加載方案列表      val forceDownloadSchemes = sparkConf.get(FORCE_DOWNLOAD_SCHEMES)      // 判斷是否需要下載的方法      def shouldDownload(scheme: String): Boolean = {        forceDownloadSchemes.contains("*") || forceDownloadSchemes.contains(scheme) ||          Try {            FileSystem.getFileSystemClass(scheme, hadoopConf)          }.isFailure      }      // 下載資源的方法      def downloadResource(resource: String): String = {        val uri = Utils.resolveURI(resource)        uri.getScheme match {          case "local" | "file" => resource          case e if shouldDownload(e) =>            val file = new File(targetDir, new Path(uri).getName)            if (file.exists()) {              file.toURI.toString            } else {              downloadFile(resource, targetDir, sparkConf, hadoopConf, secMgr)            }          case _ => uri.toString        }      }      // 下載主要運行資源      args.primaryResource = Option(args.primaryResource).map {        downloadResource      }.orNull      // 下載文件      args.files = Option(args.files).map { files =>        Utils.stringToSeq(files).map(downloadResource).mkString(",")      }.orNull      args.pyFiles = Option(args.pyFiles).map { pyFiles =>        Utils.stringToSeq(pyFiles).map(downloadResource).mkString(",")      }.orNull      // 下載jars      args.jars = Option(args.jars).map { jars =>        Utils.stringToSeq(jars).map(downloadResource).mkString(",")      }.orNull      // 下載壓縮文件      args.archives = Option(args.archives).map { archives =>        Utils.stringToSeq(archives).map(downloadResource).mkString(",")      }.orNull    }    // 如果我們正在運行python應用，請將主類設置為特定的python運行器    if (args.isPython && deployMode == CLIENT) {      if (args.primaryResource == PYSPARK_SHELL) {        args.mainClass = "org.apache.spark.api.python.PythonGatewayServer"      } else {        // If a python file is provided, add it to the child arguments and list of files to deploy.        // Usage: PythonAppRunner  [app arguments]        args.mainClass = "org.apache.spark.deploy.PythonRunner"        args.childArgs = ArrayBuffer(localPrimaryResource, localPyFiles) ++ args.childArgs        if (clusterManager != YARN) {          // The YARN backend distributes the primary file differently, so don't merge it.          args.files = mergeFileLists(args.files, args.primaryResource)        }      }      if (clusterManager != YARN) {        // The YARN backend handles python files differently, so don't merge the lists.        args.files = mergeFileLists(args.files, args.pyFiles)      }      if (localPyFiles != null) {        sparkConf.set("spark.submit.pyFiles", localPyFiles)      }    }    // 在R應用程序的yarn模式中，添加SparkR包存檔和包含所有構建的R庫的R包存檔到存檔中，以便它們可以隨作業一起分發    if (args.isR && clusterManager == YARN) {      val sparkRPackagePath = RUtils.localSparkRPackagePath      if (sparkRPackagePath.isEmpty) {        printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")      }      val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)      if (!sparkRPackageFile.exists()) {        printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")      }      val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString      // Distribute the SparkR package.      // Assigns a symbol link name "sparkr" to the shipped package.      args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")      // Distribute the R package archive containing all the built R packages.      if (!RUtils.rPackages.isEmpty) {        val rPackageFile =          RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)        if (!rPackageFile.exists()) {          printErrorAndExit("Failed to zip all the built R packages.")        }        val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString        // Assigns a symbol link name "rpkg" to the shipped package.        args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")      }    }    // TODO: Support distributing R packages with standalone cluster    if (args.isR && clusterManager == STANDALONE && !RUtils.rPackages.isEmpty) {      printErrorAndExit("Distributing R packages with standalone cluster is not supported.")    }    // TODO: Support distributing R packages with mesos cluster    if (args.isR && clusterManager == MESOS && !RUtils.rPackages.isEmpty) {      printErrorAndExit("Distributing R packages with mesos cluster is not supported.")    }    // 如果我們正在運行R應用，請將主類設置為特定的R運行器    if (args.isR && deployMode == CLIENT) {      if (args.primaryResource == SPARKR_SHELL) {        args.mainClass = "org.apache.spark.api.r.RBackend"      } else {        // If an R file is provided, add it to the child arguments and list of files to deploy.        // Usage: RRunner  [app arguments]        args.mainClass = "org.apache.spark.deploy.RRunner"        args.childArgs = ArrayBuffer(localPrimaryResource) ++ args.childArgs        args.files = mergeFileLists(args.files, args.primaryResource)      }    }    if (isYarnCluster && args.isR) {      // In yarn-cluster mode for an R app, add primary resource to files      // that can be distributed with the job      args.files = mergeFileLists(args.files, args.primaryResource)    }    // Special flag to avoid deprecation warnings at the client    sys.props("SPARK_SUBMIT") = "true"    //  為各種部署模式設置相應參數這里返回的是元組OptionAssigner類沒有方法,只是設置了參數類型    val options = List[OptionAssigner](      // All cluster managers      OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.master"),      OptionAssigner(args.deployMode, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,        confKey = "spark.submit.deployMode"),      OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.app.name"),      OptionAssigner(args.ivyRepoPath, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.jars.ivy"),      OptionAssigner(args.driverMemory, ALL_CLUSTER_MGRS, CLIENT,        confKey = "spark.driver.memory"),      OptionAssigner(args.driverExtraClassPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,        confKey = "spark.driver.extraClassPath"),      OptionAssigner(args.driverExtraJavaOptions, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,        confKey = "spark.driver.extraJavaOptions"),      OptionAssigner(args.driverExtraLibraryPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,        confKey = "spark.driver.extraLibraryPath"),      // Propagate attributes for dependency resolution at the driver side      OptionAssigner(args.packages, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.packages"),      OptionAssigner(args.repositories, STANDALONE | MESOS, CLUSTER,        confKey = "spark.jars.repositories"),      OptionAssigner(args.ivyRepoPath, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.ivy"),      OptionAssigner(args.packagesExclusions, STANDALONE | MESOS,        CLUSTER, confKey = "spark.jars.excludes"),      // Yarn only      OptionAssigner(args.queue, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.queue"),      OptionAssigner(args.numExecutors, YARN, ALL_DEPLOY_MODES,        confKey = "spark.executor.instances"),      OptionAssigner(args.pyFiles, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.pyFiles"),      OptionAssigner(args.jars, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.jars"),      OptionAssigner(args.files, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.files"),      OptionAssigner(args.archives, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.archives"),      OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.principal"),      OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.keytab"),      // Other options      OptionAssigner(args.executorCores, STANDALONE | YARN | KUBERNETES, ALL_DEPLOY_MODES,        confKey = "spark.executor.cores"),      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN | KUBERNETES, ALL_DEPLOY_MODES,        confKey = "spark.executor.memory"),      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,        confKey = "spark.cores.max"),      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,        confKey = "spark.files"),      OptionAssigner(args.jars, LOCAL, CLIENT, confKey = "spark.jars"),      OptionAssigner(args.jars, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,        confKey = "spark.jars"),      OptionAssigner(args.driverMemory, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,        confKey = "spark.driver.memory"),      OptionAssigner(args.driverCores, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,        confKey = "spark.driver.cores"),      OptionAssigner(args.supervise.toString, STANDALONE | MESOS, CLUSTER,        confKey = "spark.driver.supervise"),      OptionAssigner(args.ivyRepoPath, STANDALONE, CLUSTER, confKey = "spark.jars.ivy"),      // An internal option used only for spark-shell to add user jars to repl's classloader,      // previously it uses "spark.jars" or "spark.yarn.dist.jars" which now may be pointed to      // remote jars, so adding a new option to only specify local jars for spark-shell internally.      OptionAssigner(localJars, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.repl.local.jars")    )    // 在客戶端模式下，直接啟動應用程序主類    // 另外，將主應用程序jar和所有添加的jar(如果有)添加到classpath    if (deployMode == CLIENT) {      childMainClass = args.mainClass      if (localPrimaryResource != null && isUserJar(localPrimaryResource)) {        childClasspath += localPrimaryResource      }      if (localJars != null) {        childClasspath ++= localJars.split(",")      }    }    // 添加主應用程序jar和任何添加到類路徑的jar，以yarn客戶端需要這些jar。    // 這里假設primaryResource和user jar都是本地jar，否則它不會被添加到yarn客戶端的類路徑中。    if (isYarnCluster) {      if (isUserJar(args.primaryResource)) {        childClasspath += args.primaryResource      }      if (args.jars != null) {        childClasspath ++= args.jars.split(",")      }    }    if (deployMode == CLIENT) {      if (args.childArgs != null) {        childArgs ++= args.childArgs      }    }    // 將所有參數映射到我們選擇的模式的命令行選項或系統屬性    for (opt  x.split(",").toSeq).getOrElse(Seq.empty)      if (isUserJar(args.primaryResource)) {        jars = jars ++ Seq(args.primaryResource)      }      sparkConf.set("spark.jars", jars.mkString(","))    }    // 在standalone cluster模式下，使用REST客戶端提交應用程序(Spark 1.3+)。所有Spark參數都將通過系統屬性傳遞給客戶端。    if (args.isStandaloneCluster) {      if (args.useRest) {        childMainClass = REST_CLUSTER_SUBMIT_CLASS        childArgs += (args.primaryResource, args.mainClass)      } else {        // In legacy standalone cluster mode, use Client as a wrapper around the user class        childMainClass = STANDALONE_CLUSTER_SUBMIT_CLASS        if (args.supervise) {          childArgs += "--supervise"        }        Option(args.driverMemory).foreach { m => childArgs += ("--memory", m) }        Option(args.driverCores).foreach { c => childArgs += ("--cores", c) }        childArgs += "launch"        childArgs += (args.master, args.primaryResource, args.mainClass)      }      if (args.childArgs != null) {        childArgs ++= args.childArgs      }    }    // 讓YARN知道這是一個pyspark應用程序，因此它將分發所需的庫。    if (clusterManager == YARN) {      if (args.isPython) {        sparkConf.set("spark.yarn.isPython", "true")      }    }    if (clusterManager == MESOS && UserGroupInformation.isSecurityEnabled) {      setRMPrincipal(sparkConf)    }    // 在yarn-cluster模式下，將yarn.Client用作用戶類的包裝器    if (isYarnCluster) {      childMainClass = YARN_CLUSTER_SUBMIT_CLASS      if (args.isPython) {        childArgs += ("--primary-py-file", args.primaryResource)        childArgs += ("--class", "org.apache.spark.deploy.PythonRunner")      } else if (args.isR) {        val mainFile = new Path(args.primaryResource).getName        childArgs += ("--primary-r-file", mainFile)        childArgs += ("--class", "org.apache.spark.deploy.RRunner")      } else {        if (args.primaryResource != SparkLauncher.NO_RESOURCE) {          childArgs += ("--jar", args.primaryResource)        }        childArgs += ("--class", args.mainClass)      }      if (args.childArgs != null) {        args.childArgs.foreach { arg => childArgs += ("--arg", arg) }      }    }    if (isMesosCluster) {      assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API")      childMainClass = REST_CLUSTER_SUBMIT_CLASS      if (args.isPython) {        // Second argument is main class        childArgs += (args.primaryResource, "")        if (args.pyFiles != null) {          sparkConf.set("spark.submit.pyFiles", args.pyFiles)        }      } else if (args.isR) {        // Second argument is main class        childArgs += (args.primaryResource, "")      } else {        childArgs += (args.primaryResource, args.mainClass)      }      if (args.childArgs != null) {        childArgs ++= args.childArgs      }    }    if (isKubernetesCluster) {      childMainClass = KUBERNETES_CLUSTER_SUBMIT_CLASS      if (args.primaryResource != SparkLauncher.NO_RESOURCE) {        childArgs ++= Array("--primary-java-resource", args.primaryResource)      }      childArgs ++= Array("--main-class", args.mainClass)      if (args.childArgs != null) {        args.childArgs.foreach { arg =>          childArgs += ("--arg", arg)        }      }    }    // 加載通過--conf和默認屬性文件指定的所有屬性    for ((k, v)       // 如果存在，用解析的URI替換舊的URI      sparkConf.getOption(config).foreach { oldValue =>        sparkConf.set(config, Utils.resolveURIs(oldValue))      }    }    // 清理和格式化python文件的路徑    // 如果默認配置中有設置spark.submit.pyFiles,name--py-files不用添加    sparkConf.getOption("spark.submit.pyFiles").foreach { pyFiles =>      val resolvedPyFiles = Utils.resolveURIs(pyFiles)      val formattedPyFiles = if (!isYarnCluster && !isMesosCluster) {        PythonRunner.formatPaths(resolvedPyFiles).mkString(",")      } else {        // 返回清理和格式化后的python文件路徑        resolvedPyFiles      }      sparkConf.set("spark.submit.pyFiles", formattedPyFiles)    }    // 最終prepareSubmitEnvironment()返回的元組,對應了(mainclass args, jars classpath, sparkConf, mainclass path)    (childArgs, childClasspath, sparkConf, childMainClass)  }

2.4.2.4 doRunMain

    // 主要是調用runMain()啟動相應環境的main()的方法    // 環境準備好以后,會先往下運行判斷,這里是在等著調用    def doRunMain(): Unit = {      // 提交時可以指定--proxy-user,如果沒有指定,則獲取當前用戶      if (args.proxyUser != null) {        val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,          UserGroupInformation.getCurrentUser())        try {          proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {            override def run(): Unit = {              // 這里是真正的執行,runMain()              runMain(childArgs, childClasspath, sparkConf, 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 {        // 沒有指定用戶時執行        runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)      }    }

2.4.2.5 runMain

/** * 使用提供的啟動環境運行子類的main方法。 * 請注意，如果我們正在運行集群部署模式或python應用程序，則該主類將不是用戶提供的主類。 * * 這里的參數有子類需要的參數,子類路徑,sparkConf,子類main()路徑,參數重復判斷 */private def runMain(                     childArgs: Seq[String],                     childClasspath: Seq[String],                     sparkConf: SparkConf,                     childMainClass: String,                     verbose: Boolean): Unit = {  if (verbose) {    printStream.println(s"Main class:$childMainClass")    printStream.println(s"Arguments:${childArgs.mkString("")}")    printStream.println(s"Spark config:${Utils.redact(sparkConf.getAll.toMap).mkString("")}")    printStream.println(s"Classpath elements:${childClasspath.mkString("")}")    printStream.println("")  }  // 初始化類加載器  val loader = if (sparkConf.get(DRIVER_USER_CLASS_PATH_FIRST)) {    // 如果用戶設置了class,通過ChildFirstURLClassLoader來加載    new ChildFirstURLClassLoader(new Array[URL](0), Thread.currentThread.getContextClassLoader)  } else {    // 如果用戶沒有設置,通過MutableURLClassLoader來加載    new MutableURLClassLoader(new Array[URL](0), Thread.currentThread.getContextClassLoader)  }  // 設置由上面自定義的類加載器來加載class到JVM  Thread.currentThread.setContextClassLoader(loader)  // 從Classpath中添加jars  for (jar       e.printStackTrace(printStream)      if (childMainClass.contains("thriftserver")) {        printStream.println(s"Failed to load main class $childMainClass.")        printStream.println("You need to build Spark with -Phive and -Phive-thriftserver.")      }      System.exit(CLASS_NOT_FOUND_EXIT_STATUS)    case e: NoClassDefFoundError =>      e.printStackTrace(printStream)      if (e.getMessage.contains("org/apache/hadoop/hive")) {        printStream.println(s"Failed to load hive class.")        printStream.println("You need to build Spark with -Phive and -Phive-thriftserver.")      }      System.exit(CLASS_NOT_FOUND_EXIT_STATUS)  }  /**   * 通過classOf[]構建從屬于mainClass的SparkApplication對象   * 然后通過mainclass實例化了SparkApplication   * SparkApplication是一個抽象類,這里主要是實現它的start()   */  val app: SparkApplication = if (classOf[SparkApplication].isAssignableFrom(mainClass)) {    mainClass.newInstance().asInstanceOf[SparkApplication]  } else {    // SPARK-4170    if (classOf[scala.App].isAssignableFrom(mainClass)) {      printWarning("Subclasses of scala.App may not work correctly. Use a main() method instead.")    }    // 如果mainclass無法實例化SparkApplication,則使用替代構建子類JavaMainApplication實例    new JavaMainApplication(mainClass)  }  @tailrec  def findCause(t: Throwable): Throwable = t match {    case e: UndeclaredThrowableException =>      if (e.getCause() != null) findCause(e.getCause()) else e    case e: InvocationTargetException =>      if (e.getCause() != null) findCause(e.getCause()) else e    case e: Throwable =>      e  }  try {    // 啟動實例    app.start(childArgs.toArray, sparkConf)  } catch {    case t: Throwable =>      findCause(t) match {        case SparkUserAppException(exitCode) =>          System.exit(exitCode)        case t: Throwable =>          throw t      }  }}

2.4.3 SparkApplication

package org.apache.spark.deployimport java.lang.reflect.Modifierimport org.apache.spark.SparkConf/** * 這是spark任務的入口抽象類,需要實現它的無參構造 */private[spark] trait SparkApplication {  def start(args: Array[String], conf: SparkConf): Unit}/** * 用main方法包裝標準java類的SparkApplication實現 * * 用main方法包裝標準java類的SparkApplication實現配置是通過系統配置文件傳遞,在同一個JVM中加載太多配置會可能導致配置溢出 */private[deploy] class JavaMainApplication(klass: Class[_]) extends SparkApplication {  override def start(args: Array[String], conf: SparkConf): Unit = {    val mainMethod = klass.getMethod("main", new Array[String](0).getClass)    if (!Modifier.isStatic(mainMethod.getModifiers)) {      throw new IllegalStateException("The main method in the given main class must be static")    }    val sysProps = conf.getAll.toMap    sysProps.foreach { case (k, v) =>      sys.props(k) = v    }    mainMethod.invoke(null, args)  }}

如果是在本地模式，到SparkApplication這個類這里已經運行結束。

但是如果是yarn cluster模式，它創建的實例是不同的，也就是start()啟動的類其實是YarnClusterApplication，同樣繼承了SparkApplication，在后續的文章中回繼續跟進。