关于RDD, 详细可以参考Spark的论文, 下面看下源码
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
Represents an immutable, partitioned collection of elements that can be operated on in parallel.
* Internally, each RDD is characterized by five main properties:
* - 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)
RDD分为一下几类,
basic(org.apache.spark.rdd.RDD): This class contains the basic operations available on all RDDs, such as `map`, `filter`, and `persist`.
org.apache.spark.rdd.PairRDDFunctions: contains operations available only on RDDs of key-value pairs, such as `groupByKey` and `join`
org.apache.spark.rdd.DoubleRDDFunctions: contains operations available only on RDDs of Doubles
org.apache.spark.rdd.SequenceFileRDDFunctions: contains operations available on RDDs that can be saved asSequenceFiles
RDD首先是泛型类, T表示存放数据的类型, 在处理数据是都是基于Iterator[T]
以SparkContext和依赖关系Seq deps为初始化参数
从RDD提供的这些接口大致就可以知道, 什么是RDD
1. RDD是一块数据, 可能比较大的数据, 所以不能保证可以放在一个机器的memory中, 所以需要分成partitions, 分布在集群的机器的memory
所以自然需要getPartitions, partitioner如果分区, getPreferredLocations分区如何考虑locality
Partition的定义很简单, 只有id, 不包含data
trait Partition extends Serializable { /** * Get the split's index within its parent RDD */ def index: Int // A better default implementation of HashCode override def hashCode(): Int = index }
2. RDD之间是有关联的, 一个RDD可以通过compute逻辑把父RDD的数据转化成当前RDD的数据, 所以RDD之间有因果关系
并且通过getDependencies, 可以取到所有的dependencies
3. RDD是可以被persisit的, 常用的是cache, 即StorageLevel.MEMORY_ONLY
4. RDD是可以被checkpoint的, 以提高failover的效率, 当有很长的RDD链时, 单纯的依赖replay会比较低效
5. RDD.iterator可以产生用于迭代真正数据的Iterator[T]
6. 在RDD上可以做各种transforms和actions
abstract class RDD[T: ClassManifest]( @transient private var sc: SparkContext, //@transient, 不需要序列化 @transient private var deps: Seq[Dependency[_]] ) extends Serializable with Logging {
/**辅助构造函数, 专门用于初始化1对1依赖关系的RDD,这种还是很多的, filter, map...
Construct an RDD with just a one-to-one dependency on one parent */ def this(@transient oneParent: RDD[_]) = this(oneParent.context , List(new OneToOneDependency(oneParent))) // 不同于一般的RDD, 这种情况因为只有一个parent, 所以直接传入parent RDD对象即可
// ======================================================================= // Methods that should be implemented by subclasses of RDD // ======================================================================= /** Implemented by subclasses to compute a given partition. */ def compute(split: Partition, context: TaskContext): Iterator[T] /** * Implemented by subclasses to return the set of partitions in this RDD. This method will only * be called once, so it is safe to implement a time-consuming computation in it. */ protected def getPartitions: Array[Partition] /** * Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only * be called once, so it is safe to implement a time-consuming computation in it. */ protected def getDependencies: Seq[Dependency[_]] = deps /** Optionally overridden by subclasses to specify placement preferences. */ protected def getPreferredLocations(split: Partition): Seq[String] = Nil /** Optionally overridden by subclasses to specify how they are partitioned. */ val partitioner: Option[Partitioner] = None // ======================================================================= // Methods and fields available on all RDDs // ======================================================================= /** The SparkContext that created this RDD. */ def sparkContext: SparkContext = sc /** A unique ID for this RDD (within its SparkContext). */ val id: Int = sc.newRddId() /** A friendly name for this RDD */ var name: String = null /** * Set this RDD's storage level to persist its values across operations after the first time * it is computed. This can only be used to assign a new storage level if the RDD does not * have a storage level set yet.. */ def persist(newLevel: StorageLevel): RDD[T] = { // TODO: Handle changes of StorageLevel if (storageLevel != StorageLevel.NONE && newLevel != storageLevel) { throw new UnsupportedOperationException( "Cannot change storage level of an RDD after it was already assigned a level") } storageLevel = newLevel // Register the RDD with the SparkContext sc.persistentRdds(id) = this this } /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def persist(): RDD[T] = persist(StorageLevel.MEMORY_ONLY) /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): RDD[T] = persist()
/** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */ def getStorageLevel = storageLevel // Our dependencies and partitions will be gotten by calling subclass's methods below, and will // be overwritten when we're checkpointed private var dependencies_ : Seq[Dependency[_]] = null @transient private var partitions_ : Array[Partition] = null /** An Option holding our checkpoint RDD, if we are checkpointed
* checkpoint就是把RDD存到磁盘文件中, 以提高failover的效率, 虽然也可以选择replay
* 并且在RDD的实现中, 如果存在checkpointRDD, 则可以直接从中读到RDD数据, 而不需要compute */ private def checkpointRDD: Option[RDD[T]] = checkpointData.flatMap(_.checkpointRDD)
/**
* Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
* This should ''not'' be called by users directly, but is available for implementors of custom
* subclasses of RDD.
*/
/** 这是RDD访问数据的核心, 在RDD中的Partition中只包含id而没有真正数据
* 那么如果获取RDD的数据? 参考storage模块
* 在cacheManager.getOrCompute中, 会将RDD和Partition id对应到相应的block, 并从中读出数据*/ final def iterator(split: Partition, context: TaskContext): Iterator[T] = { if (storageLevel != StorageLevel.NONE) {//StorageLevel不为None,说明这个RDD persist过, 可以直接读出来 SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel) } else { computeOrReadCheckpoint(split, context) //如果没有persisit过, 只有从新计算出, 或从checkpoint中读出 } }
// Transformations (return a new RDD) //...... 各种transformations的接口,map, union...
/** * Return a new RDD by applying a function to all elements of this RDD. */ def map[U: ClassManifest](f: T => U): RDD[U] = new MappedRDD(this, sc.clean(f))
// Actions (launch a job to return a value to the user program) //......各种actions的接口,count, collect...
/** * Return the number of elements in the RDD. */ def count(): Long = {// 只有在action中才会真正调用runJob, 所以transform都是lazy的 sc.runJob(this, (iter: Iterator[T]) => { var result = 0L while (iter.hasNext) { result += 1L iter.next() } result }).sum }
// ======================================================================= // Other internal methods and fields // =======================================================================
/** Returns the first parent RDD
返回第一个parent RDD*/ protected[spark] def firstParent[U: ClassManifest] = { dependencies.head.rdd.asInstanceOf[RDD[U]] }
//................
}
这里先只讨论一些basic的RDD, pairRDD会单独讨论
FilteredRDD
One-to-one Dependency, FilteredRDD
使用FilteredRDD, 将当前RDD作为第一个参数, f函数作为第二个参数, 返回值是filter过后的RDD
/** * Return a new RDD containing only the elements that satisfy a predicate. */ def filter(f: T => Boolean): RDD[T] = new FilteredRDD(this, sc.clean(f))
在compute中, 对parent RDD的Iterator[T]进行filter操作
private[spark] class FilteredRDD[T: ClassManifest]( //filter是典型的one-to-one dependency, 使用辅助构造函数 prev: RDD[T], //parent RDD f: T => Boolean) //f,过滤函数 extends RDD[T](prev) { //firstParent会从deps中取出第一个RDD对象, 就是传入的prev RDD, 在One-to-one Dependency中,parent和child的partition信息相同 override def getPartitions: Array[Partition] = firstParent[T].partitions override val partitioner = prev.partitioner // Since filter cannot change a partition's keys override def compute(split: Partition, context: TaskContext) = firstParent[T].iterator(split, context).filter(f) //compute就是真正产生RDD的逻辑 }
UnionRDD
Range Dependency, 仍然是narrow的
先看看如果使用union的, 第二个参数是, 两个RDD的array, 返回值就是把这两个RDD union后产生的新的RDD
/** * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ def union(other: RDD[T]): RDD[T] = new UnionRDD(sc, Array(this, other))
先定义UnionPartition, Union操作的特点是, 只是把多个RDD的partition合并到一个RDD中, 而partition本身没有变化, 所以可以直接重用parent partition
3个参数
idx, partition id, 在当前UnionRDD中的序号
rdd, parent RDD
splitIndex, parent partition的id
private[spark] class UnionPartition[T: ClassManifest](idx: Int, rdd: RDD[T], splitIndex: Int) extends Partition { var split: Partition = rdd.partitions(splitIndex)//从parent RDD中取出相应的partition, 重用 def iterator(context: TaskContext) = rdd.iterator(split, context)//Iterator也可以重用 def preferredLocations() = rdd.preferredLocations(split) override val index: Int = idx//partition id是新的, 因为多个合并后, 序号肯定会发生变化 }
定义UnionRDD
class UnionRDD[T: ClassManifest]( sc: SparkContext, @transient var rdds: Seq[RDD[T]]) //parent RDD Seq extends RDD[T](sc, Nil) { // Nil since we implement getDependencies override def getPartitions: Array[Partition] = { val array = new Array[Partition](rdds.map(_.partitions.size).sum) //UnionRDD的partition数,是所有parent RDD中的partition数目的和 var pos = 0 for (rdd <- rdds; split <- rdd.partitions) { array(pos) = new UnionPartition(pos, rdd, split.index) //创建所有的UnionPartition pos += 1 } array } override def getDependencies: Seq[Dependency[_]] = { val deps = new ArrayBuffer[Dependency[_]] var pos = 0 for (rdd <- rdds) { deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size)//创建RangeDependency pos += rdd.partitions.size)//由于是RangeDependency, 所以pos的递增是加上整个区间size } deps } override def compute(s: Partition, context: TaskContext): Iterator[T] = s.asInstanceOf[UnionPartition[T]].iterator(context)//Union的compute非常简单,什么都不需要做 override def getPreferredLocations(s: Partition): Seq[String] = s.asInstanceOf[UnionPartition[T]].preferredLocations() }
本文章摘自博客园，原文发布日期：2013-12-24
