sparkContext是spark程序的入口，可以创建RDD、accumulators和broadcast

/**
 * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
 * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.
 *
 * Only one SparkContext may be active per JVM.  You must `stop()` the active SparkContext before
 * creating a new one.  This limitation may eventually be removed; see SPARK-2243 for more details.
 *
 * @param config a Spark Config object describing the application configuration. Any settings in
 *   this config overrides the default configs as well as system properties.
 */
class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationClient {

sparkContext的初始化只需要一个sparkConf，sparkConf的实质是一个<k, v>对

/**
 * Configuration for a Spark application. Used to set various Spark parameters as key-value pairs.
 *
 * Most of the time, you would create a SparkConf object with `new SparkConf()`, which will load
 * values from any `spark.*` Java system properties set in your application as well. In this case,
 * parameters you set directly on the `SparkConf` object take priority over system properties.
 *
 * For unit tests, you can also call `new SparkConf(false)` to skip loading external settings and
 * get the same configuration no matter what the system properties are.
 *
 * All setter methods in this class support chaining. For example, you can write
 * `new SparkConf().setMaster("local").setAppName("My app")`.
 *
 * Note that once a SparkConf object is passed to Spark, it is cloned and can no longer be modified
 * by the user. Spark does not support modifying the configuration at runtime.
 *
 * @param loadDefaults whether to also load values from Java system properties
 */
class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {

sparkConf的主要参数有

• master
• appName
• jars
• executorEnv
• sparkHome

    /**
   * Creates a modified version of a SparkConf with the parameters that can be passed separately
   * to SparkContext, to make it easier to write SparkContext's constructors. This ignores
   * parameters that are passed as the default value of null, instead of throwing an exception
   * like SparkConf would.
   */
  private[spark] def updatedConf(
      conf: SparkConf,
      master: String,
      appName: String,
      sparkHome: String = null,
      jars: Seq[String] = Nil,
      environment: Map[String, String] = Map()): SparkConf =
  {
    val res = conf.clone()
    res.setMaster(master)
    res.setAppName(appName)
    if (sparkHome != null) {
      res.setSparkHome(sparkHome)
    }
    if (jars != null && !jars.isEmpty) {
      res.setJars(jars)
    }
    res.setExecutorEnv(environment.toSeq)
    res
  }