Spark读取配置
我们知道，有一些配置可以在多个地方配置。以配置executor的memory为例，有以下三种方式：
- spark-submit的
--executor-memory选项 - spark-defaults.conf的
spark.executor.memory配置 - spark-env.sh的
SPARK_EXECUTOR_MEMORY配置
同一个配置可以在多处设置，这显然会造成迷惑，不知道spark为什么到现在还保留这样的逻辑。
如果我分别在这三处对executor的memory设置了不同的值，最终在Application中生效的是哪个？
处理这一问题的类是SparkSubmitArguments。在其构造函数中就完成了从 『spark-submit --选项』、『spark-defaults.conf』、『spark-env.sh』中读取配置，并根据策略决定使用哪个配置。下面分几步来分析这个重要的构造函数。
Step0：读取spark-env.sh配置并写入环境变量中
SparkSubmitArguments的参数列表包含一个env: Map[String, String] = sys.env参数。该参数包含一些系统环境变量的值和从spark-env.sh中读取的配置值，如图是我一个demo中env值的部分截图
这一步之所以叫做Step0，是因为env的值在构造SparkSubmitArguments对象之前就确认，即spark-env.sh在构造SparkSubmitArguments对象前就读取并将配置存入env中。
Step1：创建各配置成员并赋空值
这一步比较简单，定义了所有要从『spark-submit --选项』、『spark-defaults.conf』、『spark-env.sh』中读取的配置，并赋空值。下面的代码展示了其中一部分 ：
var master: String = null
var deployMode: String = null
var executorMemory: String = null
var executorCores: String = null
var totalExecutorCores: String = null
var propertiesFile: String = null
var driverMemory: String = null
var driverExtraClassPath: String = null
var driverExtraLibraryPath: String = null
var driverExtraJavaOptions: String = null
var queue: String = null
var numExecutors: String = null
var files: String = null
var archives: String = null
var mainClass: String = null
var primaryResource: String = null
var name: String = null
var childArgs: ArrayBuffer[String] = new ArrayBuffer[String]()
var jars: String = null
var packages: String = null
var repositories: String = null
var ivyRepoPath: String = null
var packagesExclusions: String = null
var verbose: Boolean = false
...
Step2：调用父类parse方法解析 spark-submit --选项
try {
parse(args.toList)
} catch {
case e: IllegalArgumentException => SparkSubmit.printErrorAndExit(e.getMessage())
}
这里调用父类的SparkSubmitOptionParser#parse(List<String> args)。parse函数查找args中设置的--选项和值并解析为name和value，如--master yarn-client会被解析为值为--master的name和值为yarn-client的value。这之后调用SparkSubmitArguments#handle(MASTER, "yarn-client")进行处理。
来看看handle函数干了什么：
/** Fill in values by parsing user options. */
override protected def handle(opt: String, value: String): Boolean = {
opt match {
case NAME =>
name = value
case MASTER =>
master = value
case CLASS =>
mainClass = value
case DEPLOY_MODE =>
if (value != "client" && value != "cluster") {
SparkSubmit.printErrorAndExit("--deploy-mode must be either \"client\" or \"cluster\"")
}
deployMode = value
case NUM_EXECUTORS =>
numExecutors = value
case TOTAL_EXECUTOR_CORES =>
totalExecutorCores = value
case EXECUTOR_CORES =>
executorCores = value
case EXECUTOR_MEMORY =>
executorMemory = value
case DRIVER_MEMORY =>
driverMemory = value
case DRIVER_CORES =>
driverCores = value
case DRIVER_CLASS_PATH =>
driverExtraClassPath = value
...
case _ =>
throw new IllegalArgumentException(s"Unexpected argument '$opt'.")
}
true
}
这个函数也很简单，根据参数opt及value，设置各个成员的值。接上例，parse中调用handle("--master", "yarn-client")后，在handle函数中，master成员将被赋值为yarn-client。
注意，case MASTER中的MASTER的值在SparkSubmitOptionParser定义为--master，MASTER与其他值定义如下：
protected final String MASTER = "--master";
protected final String CLASS = "--class";
protected final String CONF = "--conf";
protected final String DEPLOY_MODE = "--deploy-mode";
protected final String DRIVER_CLASS_PATH = "--driver-class-path";
protected final String DRIVER_CORES = "--driver-cores";
protected final String DRIVER_JAVA_OPTIONS = "--driver-java-options";
protected final String DRIVER_LIBRARY_PATH = "--driver-library-path";
protected final String DRIVER_MEMORY = "--driver-memory";
protected final String EXECUTOR_MEMORY = "--executor-memory";
protected final String FILES = "--files";
protected final String JARS = "--jars";
protected final String KILL_SUBMISSION = "--kill";
protected final String NAME = "--name";
protected final String PACKAGES = "--packages";
protected final String PACKAGES_EXCLUDE = "--exclude-packages";
protected final String PROPERTIES_FILE = "--properties-file";
protected final String PROXY_USER = "--proxy-user";
protected final String PY_FILES = "--py-files";
protected final String REPOSITORIES = "--repositories";
protected final String STATUS = "--status";
protected final String TOTAL_EXECUTOR_CORES = "--total-executor-cores";
...
总结来说，parse函数解析了spark-submit中的--选项，并根据解析出的name和value给SparkSubmitArguments的各个成员（例如master、deployMode、executorMemory等）设置值。
Step3：mergeDefaultSparkProperties加载spark-defaults.conf中配置
Step3读取spark-defaults.conf中的配置文件并存入sparkProperties中，sparkProperties将在下一步中发挥作用
//< 保存从spark-defaults.conf读取的配置
val sparkProperties: HashMap[String, String] = new HashMap[String, String]()
//< 获取配置文件路径，若在spark-env.sh中设置SPARK_CONF_DIR，则以该值为准；否则为 $SPARK_HOME/conf/spark-defaults.conf
def getDefaultPropertiesFile(env: Map[String, String] = sys.env): String = {
env.get("SPARK_CONF_DIR")
.orElse(env.get("SPARK_HOME").map { t => s"$t${File.separator}conf" })
.map { t => new File(s"$t${File.separator}spark-defaults.conf")}
.filter(_.isFile)
.map(_.getAbsolutePath)
.orNull
}
//< 读取spark-defaults.conf配置并存入sparkProperties中
private def mergeDefaultSparkProperties(): Unit = {
// Use common defaults file, if not specified by user
propertiesFile = Option(propertiesFile).getOrElse(Utils.getDefaultPropertiesFile(env))
// Honor --conf before the defaults file
defaultSparkProperties.foreach { case (k, v) =>
if (!sparkProperties.contains(k)) {
sparkProperties(k) = v
}
}
}
Step4：loadEnvironmentArguments确认每个配置成员最终值
先来看看代码（由于篇幅太长，省略了一部分）
private def loadEnvironmentArguments(): Unit = {
master = Option(master)
.orElse(sparkProperties.get("spark.master"))
.orElse(env.get("MASTER"))
.orNull
driverExtraClassPath = Option(driverExtraClassPath)
.orElse(sparkProperties.get("spark.driver.extraClassPath"))
.orNull
driverExtraJavaOptions = Option(driverExtraJavaOptions)
.orElse(sparkProperties.get("spark.driver.extraJavaOptions"))
.orNull
driverExtraLibraryPath = Option(driverExtraLibraryPath)
.orElse(sparkProperties.get("spark.driver.extraLibraryPath"))
.orNull
driverMemory = Option(driverMemory)
.orElse(sparkProperties.get("spark.driver.memory"))
.orElse(env.get("SPARK_DRIVER_MEMORY"))
.orNull
...
keytab = Option(keytab).orElse(sparkProperties.get("spark.yarn.keytab")).orNull
principal = Option(principal).orElse(sparkProperties.get("spark.yarn.principal")).orNull
// Try to set main class from JAR if no --class argument is given
if (mainClass == null && !isPython && !isR && primaryResource != null) {
val uri = new URI(primaryResource)
val uriScheme = uri.getScheme()
uriScheme match {
case "file" =>
try {
val jar = new JarFile(uri.getPath)
// Note that this might still return null if no main-class is set; we catch that later
mainClass = jar.getManifest.getMainAttributes.getValue("Main-Class")
} catch {
case e: Exception =>
SparkSubmit.printErrorAndExit(s"Cannot load main class from JAR $primaryResource")
}
case _ =>
SparkSubmit.printErrorAndExit(
s"Cannot load main class from JAR $primaryResource with URI $uriScheme. " +
"Please specify a class through --class.")
}
}
// Global defaults. These should be keep to minimum to avoid confusing behavior.
master = Option(master).getOrElse("local[*]")
// In YARN mode, app name can be set via SPARK_YARN_APP_NAME (see SPARK-5222)
if (master.startsWith("yarn")) {
name = Option(name).orElse(env.get("SPARK_YARN_APP_NAME")).orNull
}
// Set name from main class if not given
name = Option(name).orElse(Option(mainClass)).orNull
if (name == null && primaryResource != null) {
name = Utils.stripDirectory(primaryResource)
}
// Action should be SUBMIT unless otherwise specified
action = Option(action).getOrElse(SUBMIT)
}
我们单独以确定master值的那部分代码来说明，相关代码如下
master = Option(master)
.orElse(sparkProperties.get("spark.master"))
.orElse(env.get("MASTER"))
.orNull
// Global defaults. These should be keep to minimum to avoid confusing behavior.
master = Option(master).getOrElse("local[*]")
确定master的值的步骤如下：
- Option(master)：若master值不为null，则以master为准；否则进入2。若master不为空，从上文的分析我们可以知道是从解析spark-submit --master选项得到的值
- .orElse(sparkProperties.get("spark.master"))：若sparkProperties.get("spark.master")范围非null则以该返回值为准；否则进入3。从Step3中可以知道sparkProperties中的值都是从spark-defaults.conf中读取
- .orElse(env.get("MASTER"))：若env.get("MASTER")返回非null，则以该返回值为准；否则进入4。env中的值从spark-env.sh读取而来
- 若以上三处均为设置master，则取默认值local[*]
查看其余配置成员的值的决定过程也和master一致，稍有不同的是并不是所有配置都能在spark-defaults.conf、spark-env.sh和spark-submit选项中设置。但优先级还是一致的。
由此，我们可以得出结论，对于spark配置。若一个配置在多处设置，则优先级如下：
spark-submit --选项 > spark-defaults.conf配置 > spark-env.sh配置 > 默认值
最后，附上流程图
