【Spark程序执行1】SparkContext对象构建
【Spark程序执行2】阶段划分（dagScheduler）
class SparkContext(config: SparkConf) extends Logging {
......
//spark配置信息：基础配置 | 环境配置
private var _conf: SparkConf = _
private var _eventLogDir: Option[URI] = None
private var _eventLogCodec: Option[String] = None
private var _listenerBus: LiveListenerBus = _
//spark环境变量
private var _env: SparkEnv = _
//spark状态传输
private var _statusTracker: SparkStatusTracker = _
private var _progressBar: Option[ConsoleProgressBar] = None
private var _ui: Option[SparkUI] = None
//Hadoop配置信息属性，加载Hadoop相关属性：*.xml配置
private var _hadoopConfiguration: Configuration = _
//executer内存
private var _executorMemory: Int = _
//通信后台 ，主要和Excutor进行通信
private var _schedulerBackend: SchedulerBackend = _
//任务调度器，调度任务，
private var _taskScheduler: TaskScheduler = _
//心跳接受
private var _heartbeatReceiver: RpcEndpointRef = _
//阶段调度，阶段划分以及任务切分
@volatile private var _dagScheduler: DAGScheduler = _
private var _applicationId: String = _
private var _applicationAttemptId: Option[String] = None
private var _eventLogger: Option[EventLoggingListener] = None
private var _driverLogger: Option[DriverLogger] = None
private var _executorAllocationManager: Option[ExecutorAllocationManager] = None
private var _cleaner: Option[ContextCleaner] = None
//监听
private var _listenerBusStarted: Boolean = false
private var _jars: Seq[String] = _
private var _files: Seq[String] = _
private var _shutdownHookRef: AnyRef = _
//状态存储
private var _statusStore: AppStatusStore = _
//心跳发射
private var _heartbeater: Heartbeater = _
private var _resources: scala.collection.immutable.Map[String, ResourceInformation] = _
private var _shuffleDriverComponents: ShuffleDriverComponents = _
private var _plugins: Option[PluginContainer] = None
......
}
2.RDD依赖
2.1：RDD之间的窄依赖
从构建RDD的其中一个方法入手：通过读取文件构建RDD
def textFile(
path: String,
minPartitions: Int = defaultMinPartitions): RDD[String] = withScope {
assertNotStopped()
//读取hadoop file文件
hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
minPartitions).map(pair => pair._2.toString).setName(path)
}
通过map构建MapPartitionsRDD对象，在构建MapPartitionRDD会传递一个this对象，this就是当前RDD对象，那说明转换算子时，是将当前对象传递给下一个RDD对象；
def map[U: ClassTag](f: T => U): RDD[U] = withScope {
val cleanF = sc.clean(f)
//
new MapPartitionsRDD[U, T](this, (_, _, iter) => iter.map(cleanF))
}
如下图所示：
每个RDD会继承RDD抽象类，并且会调用RDD的构造函数
依赖RDD的辅助构造函数，并且在辅助构造函数中会传递一个OneToOneDependency对象，并且构建这个对象时，会将父RDD对象传递；
def this(@transient oneParent: RDD[_]) =
//在辅助构造函数中，会传递一个oneToOneDependency 对象
this(oneParent.context, List(new OneToOneDependency(oneParent)))
RDD的构造函数
abstract class RDD[T: ClassTag](
@transient private var _sc: SparkContext,
@transient private var deps: Seq[Dependency[_]] //依赖关系列表
) extends Serializable with Logging {
......
}
OneToOneDependency对象会继承NarrowDependency类，表示窄依赖；
@DeveloperApi
class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
override def getParents(partitionId: Int): List[Int] = List(partitionId)
}
而NarrowDependency 会将传递的RDD作为自己的属性对象，做后续操作
@DeveloperApi
abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
def getParents(partitionId: Int): Seq[Int]
override def rdd: RDD[T] = _rdd
}
通过上述操作，转换算子通过窄依赖形成RDD之间的依赖关系；
2.2 RDD之间的宽依赖（Shuffle依赖）：通过rdd的groupBy来说明宽依赖
def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null)
: RDD[(K, Iterable[T])] = withScope {
val cleanF = sc.clean(f)
//调用groupByKey
this.map(t => (cleanF(t), t)).groupByKey(p)
}
def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
// groupByKey shouldn't use map side combine because map side combine does not
// reduce the amount of data shuffled and requires all map side data be inserted
// into a hash table, leading to more objects in the old gen.
val createCombiner = (v: V) => CompactBuffer(v)
val mergevalue = (buf: CompactBuffer[V], v: V) => buf += v
val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
//合并操作
val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
createCombiner, mergevalue, mergeCombiners, partitioner, mapSideCombine = false)
bufs.asInstanceOf[RDD[(K, Iterable[V])]]
}
combineByKeyWithClassTag方法中会构建ShuffleRDD对象，
ShuffleRDD会继承RDD抽象类，并且在调用父RDD构造函数时，传递依赖参数时，传递Nil默认值。
@DeveloperApi
class ShuffledRDD[K: ClassTag, V: ClassTag, C: ClassTag](
@transient var prev: RDD[_ <: Product2[K, V]],
part: Partitioner)
//Nil为默认的依赖关系
extends RDD[(K, C)](prev.context, Nil) {
......
}
getDependencies()方法，获取依赖关系，依赖关系为ShuffleDependency
override def getDependencies: Seq[Dependency[_]] = {
val serializer = userSpecifiedSerializer.getOrElse {
val serializerManager = SparkEnv.get.serializerManager
if (mapSideCombine) {
serializerManager.getSerializer(implicitly[ClassTag[K]], implicitly[ClassTag[C]])
} else {
serializerManager.getSerializer(implicitly[ClassTag[K]], implicitly[ClassTag[V]])
}
}
List(new ShuffleDependency(prev, part, serializer, keyOrdering, aggregator, mapSideCombine))
}
ShuffleDependency类属性
class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
@transient private val _rdd: RDD[_ <: Product2[K, V]],
val partitioner: Partitioner,
val serializer: Serializer = SparkEnv.get.serializer,
val keyOrdering: Option[Ordering[K]] = None,
val aggregator: Option[Aggregator[K, V, C]] = None,
val mapSideCombine: Boolean = false,
val shuffleWriterProcessor: ShuffleWriteProcessor = new ShuffleWriteProcessor)
extends Dependency[Product2[K, V]] {
}
依赖类说明
@DeveloperApi
abstract class Dependency[T] extends Serializable {
def rdd: RDD[T]
}
//不会引起shuffle操作的算子之间的依赖关系
@DeveloperApi
abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
def getParents(partitionId: Int): Seq[Int]
override def rdd: RDD[T] = _rdd
}
//引起shuffle操作的算子之间的依赖关系
@DeveloperApi
class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
@transient private val _rdd: RDD[_ <: Product2[K, V]],
val partitioner: Partitioner,
val serializer: Serializer = SparkEnv.get.serializer,
val keyOrdering: Option[Ordering[K]] = None,
val aggregator: Option[Aggregator[K, V, C]] = None,
val mapSideCombine: Boolean = false,
val shuffleWriterProcessor: ShuffleWriteProcessor = new ShuffleWriteProcessor)
extends Dependency[Product2[K, V]] {
if (mapSideCombine) {
require(aggregator.isDefined, "Map-side combine without Aggregator specified!")
}
override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]
private[spark] val keyClassName: String = reflect.classTag[K].runtimeClass.getName
private[spark] val valueClassName: String = reflect.classTag[V].runtimeClass.getName
// Note: It's possible that the combiner class tag is null, if the combineByKey
// methods in PairRDDFunctions are used instead of combineByKeyWithClassTag.
private[spark] val combinerClassName: Option[String] =
Option(reflect.classTag[C]).map(_.runtimeClass.getName)
val shuffleId: Int = _rdd.context.newShuffleId()
val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
shuffleId, this)
_rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
_rdd.sparkContext.shuffleDriverComponents.registerShuffle(shuffleId)
}
//一对一依赖，窄依赖的子类，也是不会引起shuffle操作算子之间的依赖关系
@DeveloperApi
class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
override def getParents(partitionId: Int): List[Int] = List(partitionId)
}
//多对一依赖 窄依赖的子类
@DeveloperApi
class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int)
extends NarrowDependency[T](rdd) {
override def getParents(partitionId: Int): List[Int] = {
if (partitionId >= outStart && partitionId < outStart + length) {
List(partitionId - outStart + inStart)
} else {
Nil
}
}
}
RDD之间的依赖关系图：
MapPartitionsRDD之间通过窄依赖，ShuffledRDD与MapPartitionsRDD之间，以及ShuffledRDD与ShuffledRDD之间通过ShuffleDependency
