def actorStream[T: ClassTag](props: Props,name: String,storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2,supervisorStrategy: SupervisorStrategy = ActorSupervisorStrategy.defaultStrategy): ReceiverInputDStream[T] = withNamedScope("actor stream") {receiverStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy)) }3、TCP socketsocketStream：converter是从socket输入流转换成元素T的迭代器的方法def socketStream[T: ClassTag](hostname: String,port: Int,converter: (InputStream) => Iterator[T],storageLevel: StorageLevel): ReceiverInputDStream[T] = {new SocketInputDStream[T](this, hostname, port, converter, storageLevel) }socketTextStream：storageLevel默认是MEMORY_AND_DISK_SER_2,converter是从inputstream中按行读取转换成迭代器的固定方法def socketTextStream(hostname: String,port: Int,storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2): ReceiverInputDStream[String] = withNamedScope("socket text stream") {socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel) }4、fileStream:filter：文件过滤器，newFileOnly：只读取新的文件。还有其他一些使用默认参数的方法。def fileStream[K: ClassTag,V: ClassTag,F <: NewInputFormat[K, V]: ClassTag ] (directory: String,filter: Path => Boolean,newFilesOnly: Boolean,conf: Configuration): InputDStream[(K, V)] = {new FileInputDStream[K, V, F](this, directory, filter, newFilesOnly, Option(conf)) }一个以固定格式读取文件作为输入的接口def textFileStream(directory: String): DStream[String] = withNamedScope("text file stream") {fileStream[LongWritable, Text, TextInputFormat](directory).map(_._2.toString) }与receiverInputDStream不同，它是以文件作为输入，所以不需要receiver去读取。而是直接根据path生成hadoopRDD，再将所有的RDD Union起来。也就是说，在一个batchDuration时间间隔内，就将这个间隔内新的file组合成一个RDD。5、将多个DStream 联合，返回UnionDStream。compute方法就是将多个DStream中的Rdd union/** * Create a unified DStream from multiple DStreams of the same type and same slide duration. */ def union[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = withScope {new UnionDStream[T](streams.toArray) }6、transform:将dstreams中得到的所有rdds转换成一个RDD/** * Create a new DStream in which each RDD is generated by applying a function on RDDs of * the DStreams. */ def transform[T: ClassTag](dstreams: Seq[DStream[_]],transformFunc: (Seq[RDD[_]], Time) => RDD[T]): DStream[T] = withScope {new TransformedDStream[T](dstreams, sparkContext.clean(transformFunc)) }二、DStream操作(DStream.scala)与RDD不同的是，，DStream是以一个outputStream作为一个job。那outputStream是如何产生的呢？在调用foreachRDD方法时通过注册将一个DStream在DStreamGraph中标记为outputStream。那有哪些API会注册outputStream呢？foreachRDD/printsaveAsNewAPIHadoopFiles/saveAsTextFiles1、map/flatMap/filter/mapPartitions与RDD类似，分别生成MappedDstream/FlatMappedDStream/FilteredDStream等,真正运算时根据receiverInputDStream的compute方法产生BlockRDD，再在这个RDD上赋予map的方法参数执行操作。2、重新分区方法最终是将BlockRDD进行重新分区/** * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the * returned DStream has exactly numPartitions partitions. */ def repartition(numPartitions: Int): DStream[T] = ssc.withScope {this.transform(_.repartition(numPartitions)) }3、reduce：这个方法将DStream的每个RDD都执行reduceFunc方法，并最终每个RDD只有一个分区,返回的还是一个DStream[T]区别：RDD.scala的reduce方法是提交runJob的，返回一个确切的值。/** * Return a new DStream in which each RDD has a single element generated by reducing each RDD * of this DStream. */ def reduce(reduceFunc: (T, T) => T): DStream[T] = ssc.withScope {this.map(x => (null, x)).reduceByKey(reduceFunc, 1).map(_._2) }4、count:这个方法是将DStream中的每个RDD进行计数，返回一个包含技术的DStream/** * Return a new DStream in which each RDD has a single element generated by counting each RDD * of this DStream. */ def count(): DStream[Long] = ssc.withScope {this.map(_ => (null, 1L)).transform(_.union(context.sparkContext.makeRDD(Seq((null, 0L)), 1))).reduceByKey(_ + _).map(_._2) }5、countByValue:类似count方法，只是该方法是按value值计数的def countByValue(numPartitions: Int = ssc.sc.defaultParallelism)(implicit ord: Ordering[T] = null): DStream[(T, Long)] = ssc.withScope {this.map(x => (x, 1L)).reduceByKey((x: Long, y: Long) => x + y, numPartitions) }6、foreachRDD：foreachFunc是在一个RDD进行自定义的任何操作def foreachRDD(foreachFunc: RDD[T] => Unit): Unit = ssc.withScope {val cleanedF = context.sparkContext.clean(foreachFunc, false)this.foreachRDD((r: RDD[T], t: Time) => cleanedF(r)) }def foreachRDD(foreachFunc: (RDD[T], Time) => Unit): Unit = ssc.withScope {// because the DStream is reachable from the outer object here, and because// DStreams can't be serialized with closures, we can't proactively check// it for serializability and so we pass the optional false to SparkContext.cleannew ForEachDStream(this, context.sparkContext.clean(foreachFunc, false)).register() }7、transform：在最终生成的RDD上执行transformFunc方法定义的转换操作def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U]def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U]8、transformWith:将自身DStream生成的RDD与other生成的RDD一起，执行transformWith方法。def transformWith[U: ClassTag, V: ClassTag](other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V]): DStream[V]def transformWith[U: ClassTag, V: ClassTag](other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V]): DStream[V]9、union联合def union(that: DStream[T]): DStream[T] = ssc.withScope {new UnionDStream[T](Array(this, that)) }10、saveAsObjectFiles/saveAsTextFiles保存为文件三、K/V类型RDD转换操作1、groupByKeydef groupByKey(): DStream[(K, Iterable[V])] = ssc.withScope {groupByKey(defaultPartitioner()) }def groupByKey(numPartitions: Int): DStream[(K, Iterable[V])] = ssc.withScope {groupByKey(defaultPartitioner(numPartitions)) }def groupByKey(partitioner: Partitioner): DStream[(K, Iterable[V])] = ssc.withScope {val createCombiner = (v: V) => ArrayBuffer[V](v)val mergeValue = (c: ArrayBuffer[V], v: V) => (c += v)val mergeCombiner = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => (c1 ++ c2)combineByKey(createCombiner, mergeValue, mergeCombiner, partitioner).asInstanceOf[DStream[(K, Iterable[V])]] }2、reduceByKeydef reduceByKey(reduceFunc: (V, V) => V): DStream[(K, V)]def reduceByKey(reduceFunc: (V, V) => V,numPartitions: Int): DStream[(K, V)]def reduceByKey(reduceFunc: (V, V) => V,partitioner: Partitioner): DStream[(K, V)]3、combineByKeydef combineByKey[C: ClassTag](createCombiner: V => C,mergeValue: (C, V) => C,mergeCombiner: (C, C) => C,partitioner: Partitioner,mapSideCombine: Boolean = true): DStream[(K, C)] = ssc.withScope {val cleanedCreateCombiner = sparkContext.clean(createCombiner)val cleanedMergeValue = sparkContext.clean(mergeValue)val cleanedMergeCombiner = sparkContext.clean(mergeCombiner)new ShuffledDStream[K, V, C](self,cleanedCreateCombiner,cleanedMergeValue,cleanedMergeCombiner,partitioner,mapSideCombine) }4、mapValues/flatMapValues与RDD的操作类似，不解释5、join内部调用transformWith，transformWith的参数就是将两个参数RDD作join操作。def join[W: ClassTag](other: DStream[(K, W)],partitioner: Partitioner): DStream[(K, (V, W))] = ssc.withScope {self.transformWith(other,(rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.join(rdd2, partitioner)) }6、saveAsNewAPIHadoopFiles保存到文件
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