15、Spark Streaming源码解读之No Receivers彻底思考

在前几期文章里讲了带Receiver的Spark Streaming 应用的相关源码解读，但是现在开发Spark Streaming的应用越来越多的采用No Receivers（Direct Approach）的方式，No Receiver的方式的优势：
1. 更强的控制*度
2. 语义一致性

其实No Receivers的方式更符合我们读取数据，操作数据的思路的。因为Spark 本身是一个计算框架，他底层会有数据来源，如果没有Receivers，我们直接操作数据来源，这其实是一种更自然的方式。如果要操作数据来源，肯定要有一个封装器，这个封装器一定是RDD类型。以直接访问Kafka中的数据为例，看一下源码中直接读写Kafka中数据的例子代码：

    
    
    
object DirectKafkaWordCount {
     
     
      def main(args: Array[String]) {
     
     
      if (args.length < 2) {
     
     
      System.err.println(s"""
     
     
      |Usage: DirectKafkaWordCount <brokers> <topics>
     
     
      | <brokers> is a list of one or more Kafka brokers
     
     
      | <topics> is a list of one or more kafka topics to consume from
     
     
      |
     
     
      """.stripMargin)
     
     
      System.exit(1)
     
     
      }
     
     
     
     
     
      StreamingExamples.setStreamingLogLevels()
     
     
     
     
     
      val Array(brokers, topics) = args
     
     
     
     
     
      // Create context with 2 second batch interval
     
     
      val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
     
     
      val ssc = new StreamingContext(sparkConf, Seconds(2))
     
     
     
     
     
      // Create direct kafka stream with brokers and topics
     
     
      val topicsSet = topics.split(",").toSet
     
     
      val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
     
     
      val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
     
     
      ssc, kafkaParams, topicsSet)
     
     
     
     
     
      // Get the lines, split them into words, count the words and print
     
     
      val lines = messages.map(_._2)
     
     
      val words = lines.flatMap(_.split(" "))
     
     
      val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
     
     
      wordCounts.print()
     
     
     
     
     
      // Start the computation
     
     
      ssc.start()
     
     
      ssc.awaitTermination()
     
     
      }
     
     
     }

Spark streaming 会将数据源封装成一个RDD，也就是KafkaRDD：

     
     
     
/**
      
      
       * A batch-oriented interface for consuming from Kafka.
      
      
       * Starting and ending offsets are specified in advance,
      
      
       * so that you can control exactly-once semantics.
      
      
       * @param kafkaParams Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
      
      
       * configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers" to be set
      
      
       * with Kafka broker(s) specified in host1:port1,host2:port2 form.
      
      
       * @param offsetRanges offset ranges that define the Kafka data belonging to this RDD
      
      
       * @param messageHandler function for translating each message into the desired type
      
      
       */
      
      
      private[kafka]
      
      
      class KafkaRDD[
      
      
       K: ClassTag,
      
      
       V: ClassTag,
      
      
       U <: Decoder[_]: ClassTag,
      
      
       T <: Decoder[_]: ClassTag,
      
      
       R: ClassTag] private[spark] (
      
      
       sc: SparkContext,
      
      
       kafkaParams: Map[String, String],
      
      
       val offsetRanges: Array[OffsetRange], //该RDD的数据偏移量
      
      
       leaders: Map[TopicAndPartition, (String, Int)],
      
      
       messageHandler: MessageAndMetadata[K, V] => R
      
      
       ) extends RDD[R](sc, Nil) with Logging with HasOffsetRanges

可以看到KafkaRDD 混入了HasOffsetRanges，它是一个trait：

   
   
   
trait HasOffsetRanges {
    
    
     def offsetRanges: Array[OffsetRange]
    
    
    }

其中OffsetRange,标识了RDD的数据的主题、分区、开始偏移量和结束偏移量：

    
    
    
inal class OffsetRange private(
     
     
      val topic: String,
     
     
      val partition: Int,
     
     
      val fromOffset: Long,
     
     
      val untilOffset: Long) extends Serializable

回到KafkaRDD，看一下KafkaRDD的getPartitions方法：

   
   
   
 override def getPartitions: Array[Partition] = {
    
    
     offsetRanges.zipWithIndex.map { case (o, i) =>
    
    
     val (host, port) = leaders(TopicAndPartition(o.topic, o.partition))
    
    
     new KafkaRDDPartition(i, o.topic, o.partition, o.fromOffset, o.untilOffset, host, port)
    
    
     }.toArray
    
    
     }

返回KafkaRDDPartition：

   
   
   
private[kafka]
    
    
    class KafkaRDDPartition(
    
    
     val index: Int,
    
    
     val topic: String,
    
    
     val partition: Int,
    
    
     val fromOffset: Long,
    
    
     val untilOffset: Long,
    
    
     val host: String,
    
    
     val port: Int
    
    
    ) extends Partition {
    
    
     /** Number of messages this partition refers to */
    
    
     def count(): Long = untilOffset - fromOffset
    
    
    }

KafkaRDDPartition清晰的描述了数据的具体位置，每个 KafkaRDDPartition分区的数据交给 KafkaRDD的compute方法计算：

   
   
   
 override def compute(thePart: Partition, context: TaskContext): Iterator[R] = {
    
    
     val part = thePart.asInstanceOf[KafkaRDDPartition]
    
    
     assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part))
    
    
     if (part.fromOffset == part.untilOffset) {
    
    
     log.info(s"Beginning offset ${part.fromOffset} is the same as ending offset " +
    
    
     s"skipping ${part.topic} ${part.partition}")
    
    
     Iterator.empty
    
    
     } else {
    
    
     new KafkaRDDIterator(part, context)
    
    
     }
    
    
     }

KafkaRDD的compute方法返回了KafkaIterator对象：

   
   
   
 private class KafkaRDDIterator(
    
    
     part: KafkaRDDPartition,
    
    
     context: TaskContext) extends NextIterator[R] {
    
    
    
    
    
     context.addTaskCompletionListener{ context => closeIfNeeded() }
    
    
    
    
    
     log.info(s"Computing topic ${part.topic}, partition ${part.partition} " +
    
    
     s"offsets ${part.fromOffset} -> ${part.untilOffset}")
    
    
    
    
    
     val kc = new KafkaCluster(kafkaParams)
    
    
     val keyDecoder = classTag[U].runtimeClass.getConstructor(classOf[VerifiableProperties])
    
    
     .newInstance(kc.config.props)
    
    
     .asInstanceOf[Decoder[K]]
    
    
     val valueDecoder = classTag[T].runtimeClass.getConstructor(classOf[VerifiableProperties])
    
    
     .newInstance(kc.config.props)
    
    
     .asInstanceOf[Decoder[V]]
    
    
     val consumer = connectLeader
    
    
     var requestOffset = part.fromOffset
    
    
     var iter: Iterator[MessageAndOffset] = null
    
    
        //..................
    
    
    }

KafkaIterator中创建了一个KakfkaCluster对象用于与Kafka集群进行交互，获取数据。

回到开头的例子，我们使用 KafkaUtils . createDirectStream 创建了InputDStream：

   
   
   
 val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
    
    
     ssc, kafkaParams, topicsSet)

看一下createDirectStream源码：

   
   
   
 def createDirectStream[
    
    
     K: ClassTag,
    
    
     V: ClassTag,
    
    
     KD <: Decoder[K]: ClassTag,
    
    
     VD <: Decoder[V]: ClassTag] (
    
    
     ssc: StreamingContext,
    
    
     kafkaParams: Map[String, String],
    
    
     topics: Set[String]
    
    
     ): InputDStream[(K, V)] = {
    
    
     val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
    
    
    //创建KakfaCluster对象
    
    
     val kc = new KafkaCluster(kafkaParams)
    
    
    //更具kc的信息获取数据偏移量
    
    
     val fromOffsets = getFromOffsets(kc, kafkaParams, topics)
    
    
     new DirectKafkaInputDStream[K, V, KD, VD, (K, V)](
    
    
     ssc, kafkaParams, fromOffsets, messageHandler)
    
    
     }

首先通过KafkaCluster从Kafka集群获取信息，创建DirectKafkaInputDStream对象返回

DirectKafkaInputDStream的compute方法源码：

   
   
   
 override def compute(validTime: Time): Option[KafkaRDD[K, V, U, T, R]] = {
    
    
        //计算最近的数据终止偏移量
    
    
     val untilOffsets = clamp(latestLeaderOffsets(maxRetries))
    
    
        //利用数据的偏移量创建KafkaRDD
    
    
     val rdd = KafkaRDD[K, V, U, T, R](
    
    
     context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler)
    
    
    
    
    
     // Report the record number and metadata of this batch interval to InputInfoTracker.
    
    
     val offsetRanges = currentOffsets.map { case (tp, fo) =>
    
    
     val uo = untilOffsets(tp)
    
    
     OffsetRange(tp.topic, tp.partition, fo, uo.offset)
    
    
     }
    
    
     val description = offsetRanges.filter { offsetRange =>
    
    
     // Don't display empty ranges.
    
    
     offsetRange.fromOffset != offsetRange.untilOffset
    
    
     }.map { offsetRange =>
    
    
     s"topic: ${offsetRange.topic}\tpartition: ${offsetRange.partition}\t" +
    
    
     s"offsets: ${offsetRange.fromOffset} to ${offsetRange.untilOffset}"
    
    
     }.mkString("\n")
    
    
     // Copy offsetRanges to immutable.List to prevent from being modified by the user
    
    
     val metadata = Map(
    
    
     "offsets" -> offsetRanges.toList,
    
    
     StreamInputInfo.METADATA_KEY_DESCRIPTION -> description)
    
    
     val inputInfo = StreamInputInfo(id, rdd.count, metadata)
    
    
     ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
    
    
    
    
    
     currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset)
    
    
     Some(rdd)
    
    
     }

可以看到DirectKafkaInputDStream的compute方法中，首先从Kafka集群获取数据的偏移量，然后利用获取偏移量创建RDD，这个Receiver的RDD创建方式不同

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15、Spark Streaming源码解读之No Receivers彻底思考

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