Spark 源码解析之SparkContext

浏览: 338 发布日期: 2016-12-09 分类: scala


SparkContext 是Spark 应用的主入口,通过它可以连接Spark 集群,并在集群中创建RDD,累加器,广播变量等;==每一个启动 JVM 上只能有一个SparkContext,在启动一个新的SparkContext之前,必须停掉处于活动状态的SparkContext==。

 * 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 {


StreamingContext 是Spark Streaming 应用的主入口,它可以从输入的数据源中创建DStream。它可以通过制定Spark master URL 和 appName来创建,也可以从SparkConf 中创建,==或者从已经存在的SparkContext 中创建==。相关联的SparkContext 可以通过context.sparkContext得到。创建和转换DStreams后,流计算可以使用context.start() 启动或使用context.stop() 停止。
context.awaitTermination() 允许当前线程一直等待,直到context 进行stop() 或者抛出异常才会终止。

 * Main entry point for Spark Streaming functionality. It provides methods used to create
 * [[org.apache.spark.streaming.dstream.DStream]]s from various input sources. It can be either
 * created by providing a Spark master URL and an appName, or from a org.apache.spark.SparkConf
 * configuration (see core Spark documentation), or from an existing org.apache.spark.SparkContext.
 * The associated SparkContext can be accessed using `context.sparkContext`. After
 * creating and transforming DStreams, the streaming computation can be started and stopped
 * using `context.start()` and `context.stop()`, respectively.
 * `context.awaitTermination()` allows the current thread to wait for the termination
 * of the context by `stop()` or by an exception.
class StreamingContext private[streaming] (
    sc_ : SparkContext,
    cp_ : Checkpoint,
    batchDur_ : Duration
  ) extends Logging {


SQLContext 是Spark 中运行==结构化数据==的主入口,可以创建DataFrame 对象,并执行SQL 查询。

 * The entry point for working with structured data (rows and columns) in Spark.  Allows the
 * creation of [[DataFrame]] objects as well as the execution of SQL queries.
 * @groupname basic Basic Operations
 * @groupname ddl_ops Persistent Catalog DDL
 * @groupname cachemgmt Cached Table Management
 * @groupname genericdata Generic Data Sources
 * @groupname specificdata Specific Data Sources
 * @groupname config Configuration
 * @groupname dataframes Custom DataFrame Creation
 * @groupname Ungrouped Support functions for language integrated queries
 * @since 1.0.0
class SQLContext private[sql](
    @transient val sparkContext: SparkContext,
    @transient protected[sql] val cacheManager: CacheManager,
    @transient private[sql] val listener: SQLListener,
    val isRootContext: Boolean)
  extends org.apache.spark.Logging with Serializable {