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RDD的源码
RDD是一个抽象类定义了所有RDD共有的一些属性和方法,下面介绍了主要的属性和方法。
abstract class RDD[T: ClassTag]( @transient private var _sc: SparkContext, @transient private var deps: Seq[Dependency[_]] ) extends Serializable with Logging {
RDD有5个主要的属性
* - A list of partitions * - A function for computing each split * - A list of dependencies on other RDDs * - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned) * - Optionally, a list of preferred locations to compute each split on (e.g. block locations for * an HDFS file)
(一)子类
CoGroupedRDD, EdgeRDD, EdgeRDDImpl, HadoopRDD, JdbcRDD, NewHadoopRDD, PartitionPruningRDD, ShuffledRDD, UnionRDD, VertexRDD, VertexRDDImpl
(二)属性
1、SpackContext
@transient private var _sc: SparkContext
在主构建函数中定义,表示RDD所在运行环境,可用于获取配置,清理环境等。
2、Seq[Dependency[_]]
@transient private var deps: Seq[Dependency[_]]
定义了这个RDD对父RDD的依赖关系。
(三)方法
1、tranformation与action
RDD中定义了所有RDD所共用的tranformation与action,如map, filter, reduce, first等,举个filter的例子:
def filter(f: T => Boolean): RDD[T] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[T, T]( this, (context, pid, iter) => iter.filter(cleanF), preservesPartitioning = true) }
2、缓存
包括pesist的多个实现及cache等,举个例子
/** * Mark this RDD for persisting using the specified level. * * @param newLevel the target storage level * @param allowOverride whether to override any existing level with the new one */ private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type = { // TODO: Handle changes of StorageLevel if (storageLevel != StorageLevel.NONE && newLevel != storageLevel && !allowOverride) { throw new UnsupportedOperationException( "Cannot change storage level of an RDD after it was already assigned a level") } // If this is the first time this RDD is marked for persisting, register it // with the SparkContext for cleanups and accounting. Do this only once. if (storageLevel == StorageLevel.NONE) { sc.cleaner.foreach(_.registerRDDForCleanup(this)) sc.persistRDD(this) } storageLevel = newLevel this }
RDD的源码
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