本期內容
1. DStream與RDD關係徹底研究
2. Streaming中RDD的生成徹底研究
spark-1.6.1\examples\src\main\scala\org\apache\spark\examples\streaming\NetworkWordCount.scala
object NetworkWordCount { def main(args: Array[String]) { if (args.length < 2) { System.err.println("Usage: NetworkWordCount <hostname> <port>") System.exit(1) } //StreamingExamples.setStreamingLogLevels() // Create the context with a 1 second batch size val sparkConf = new SparkConf().setAppName("NetworkWordCount") val ssc = new StreamingContext(sparkConf, Seconds(1)) // Create a socket stream on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') // Note that no duplication in storage level only for running locally. // Replication necessary in distributed scenario for fault tolerance. val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() //此處的print也是Transformation，其內部分調用print方法 ssc.start() ssc.awaitTermination() } }
1. DStream有下三個特徵：
（1）DStream依賴其他DStream，除了第一個DStream（基於數據源產生的）
（2）DSream基於時間間隔（Batch Duration）生成RDD
（3）DStream在每個Batch Duration後生成RDD
* DStreams internally is characterized by a few basic properties: * - A list of other DStreams that the DStream depends on * - A time interval at which the DStream generates an RDD * - A function that is used to generate an RDD after each time interval */ abstract class DStream[T: ClassTag] ( @transient private[streaming] var ssc: StreamingContext
ssc.socketTextStream ) extends Serializable with Logging {
NetworkWordCount代碼中的transform都是基於DStream。
ssc.socketTextStream -> socketStream -> SocketInputDStream -> ReceiverInputDStream -> InputDStream ->DStreamlines.flatMap -> FlatMappedDStream -> DStream
reduceByKey -> reduceByKey -> combineByKey -> ShuffledDStream ->DStream
綜上所述，DStream是RDD的模板。SparkStreaming在執行時看最後一個DStream。
再看DStream中 generatedRDDs是怎麼獲取的。
搞清楚generatedRDDs的HashMap是在哪實例化，就搞清楚了RDD是怎麼產生的。
DStream的L335行, 說明根據既定時間得到 RDD或通過緩存得到或計算出RDD並緩存RDD。
/** * Get the RDD corresponding to the given time; either retrieve it from cache * or compute-and-cache it. */ private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = { // If RDD was already generated, then retrieve it from HashMap, // or else compute the RDD generatedRDDs.get(time).orElse { // Compute the RDD if time is valid (e.g. correct time in a sliding window) // of RDD generation, else generate nothing. if (isTimeValid(time)) { //驗證時間有效性 val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) { // Disable checks for existing output directories in jobs launched by the streaming // scheduler, since we may need to write output to an existing directory during checkpoint // recovery; see SPARK-4835 for more details. We need to have this call here because // compute() might cause Spark jobs to be launched. PairRDDFunctions.disableOutputSpecValidation.withValue(true) { compute(time) } } rddOption.foreach { case newRDD => // Register the generated RDD for caching and checkpointing if (storageLevel != StorageLevel.NONE) { newRDD.persist(storageLevel) logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel") } if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) { newRDD.checkpoint() logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing") } generatedRDDs.put(time, newRDD) } rddOption } else { None } } }
轉至SocketInputStream，
class SocketInputDStream[T: ClassTag]( ssc_ : StreamingContext, host: String, port: Int, bytesToObjects: InputStream => Iterator[T], storageLevel: StorageLevel ) extends ReceiverInputDStream[T](ssc_) {
ReceiverInputDStream的compute方法
/** * Generates RDDs with blocks received by the receiver of this stream. */ 根據接收到的blocks生成RDD。 override def compute(validTime: Time): Option[RDD[T]] = { val blockRDD = { if (validTime < graph.startTime) { // If this is called for any time before the start time of the context, // then this returns an empty RDD. This may happen when recovering from a // driver failure without any write ahead log to recover pre-failure data. new BlockRDD[T](ssc.sc, Array.empty) } else { // Otherwise, ask the tracker for all the blocks that have been allocated to this stream // for this batch //此處可以看出：Spark根據沒有流處理，只有批處理。 val receiverTracker = ssc.scheduler.receiverTracker val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty) // Register the input blocks information into InputInfoTracker val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum) ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) // JobScheduler 記錄所有的數據輸入來源 // Create the BlockRDD createBlockRDD(validTime, blockInfos) //創建BlocakRDD } } Some(blockRDD) }
創建BlockRDD
private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = { if (blockInfos.nonEmpty) { val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray // Are WAL record handles present with all the blocks val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty } if (areWALRecordHandlesPresent) { // If all the blocks have WAL record handle, then create a WALBackedBlockRDD val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray new WriteAheadLogBackedBlockRDD[T]( ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid) } else { // Else, create a BlockRDD. However, if there are some blocks with WAL info but not // others then that is unexpected and log a warning accordingly. if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) { if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { logError("Some blocks do not have Write Ahead Log information; " + "this is unexpected and data may not be recoverable after driver failures") } else { logWarning("Some blocks have Write Ahead Log information; this is unexpected") } } val validBlockIds = blockIds.filter { id => // 數據是否存在，不存在就過濾掉； ssc.sparkContext.env.blockManager.master.contains(id) // 此處的master是BlocManager的Master } if (validBlockIds.size != blockIds.size) { logWarning("Some blocks could not be recovered as they were not found in memory. " + "To prevent such data loss, enabled Write Ahead Log (see programming guide " + "for more details.") } new BlockRDD[T](ssc.sc, validBlockIds) //根據有效的BlockIds生成BlockRDD } } else { // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD // according to the configuration if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { new WriteAheadLogBackedBlockRDD[T]( ssc.sparkContext, Array.empty, Array.empty, Array.empty) } else { new BlockRDD[T](ssc.sc, Array.empty) //生成空的BlockRDD } } }
再看MappedDStream，其中的compute方法中使用了parent來獲得RDD，
從中也明白了，對DStream的Transformation操作其實就是對RDD的Transformation操作，在RDD上的這種映射關係是要加上時間維度。
private[streaming] class MappedDStream[T: ClassTag, U: ClassTag] ( parent: DStream[T], mapFunc: T => U ) extends DStream[U](parent.ssc) { override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration override def compute(validTime: Time): Option[RDD[U]] = { parent.getOrCompute(validTime).map(_.map[U](mapFunc)) } }
其中mapFunc是 之前代碼中的 計數
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
