Spark Streaming源码解读之No Receivers详解

背景：
目前No Receivers在企业中使用的越来越多。No Receivers具有更强的控制度，语义一致性。No Receivers是我们操作数据来源自然方式，操作数据来源使用一个封装器，且是RDD类型的。所以Spark Streaming就产生了自定义RDD –> KafkaRDD.

源码分析：
1. 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,//因为传输的时候需要编码，所以需要Decoder  T <: Decoder[_]: ClassTag,  R: ClassTag] private[spark] (    sc: SparkContext,    kafkaParams: Map[String, String],    val offsetRanges: Array[OffsetRange],//offsetRanges指定数据范围    leaders: Map[TopicAndPartition, (String, Int)],    messageHandler: MessageAndMetadata[K, V] => R  ) extends RDD[R](sc, Nil) with Logging with HasOffsetRanges {

2.  HasOffsetRanges: RDD是a list of partitions.

/** * Represents any object that has a collection of [[OffsetRange]]s. This can be used to access the * offset ranges in RDDs generated by the direct Kafka DStream (see * [[KafkaUtils.createDirectStream()]]). * {{{//foreachRDD就可以获取当前batch Duractions中的产生的RDD的分区的数据。 *   KafkaUtils.createDirectStream(...).foreachRDD { rdd => *      val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges *      ... *   } * }}} */trait HasOffsetRanges {  def offsetRanges: Array[OffsetRange]}

3.  Count: 此时的范围指的是多少条数据。OffsetRange指定了什么topic下的什么partiiton下的读取数据范围。

/** * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class * can be created with `OffsetRange.create()`. * @param topic Kafka topic name * @param partition Kafka partition id * @param fromOffset Inclusive starting offset * @param untilOffset Exclusive ending offset */final class OffsetRange private(    val topic: String,    val partition: Int,    val fromOffset: Long,    val untilOffset: Long) extends Serializable {  import OffsetRange.OffsetRangeTuple//offset消息的偏移量/** Number of messages this OffsetRange refers to */def count(): Long = untilOffset - fromOffset

1. 在KafkaRDD中getPartition

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}

5.  KafkaRDDPartition:  相当于Kafka数据来源的指针。或者相当于引用。清晰的说明数据在哪里。

/** @param topic kafka topic name  * @param partition kafka partition id  * @param fromOffset inclusive starting offset  * @param untilOffset exclusive ending offset  * @param host preferred kafka host, i.e. the leader at the time the rdd was created  * @param port preferred kafka host's port  */private[kafka]class KafkaRDDPartition(  val index: Int,  val topic: String,  val partition: Int,  val fromOffset: Long,  val untilOffset: Long,  val host: String, //就是读取数据来源的host,port同样是  val port: Int) extends Partition {//KafkaRDD的一个partition只能属于一个topic  /** Number of messages this partition refers to */  def count(): Long = untilOffset - fromOffset}

6.  KafkaRDD中的compute计算每个数据分片

override def compute(thePart: Partition, context: TaskContext): Iterator[R] = {  val part = thePart.asInstanceOf[KafkaRDDPartition]  assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part))//如果fromOffset和untilOffset相等表面该消息是空  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)  }}

7.  KafkaRDDIterator: 获取数据

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  // The idea is to use the provided preferred host, except on task retry atttempts,  // to minimize number of kafka metadata requests  private def connectLeader: SimpleConsumer = {    if (context.attemptNumber > 0) {      kc.connectLeader(part.topic, part.partition).fold(        errs => throw new SparkException(          s"Couldn't connect to leader for topic ${part.topic} ${part.partition}: " +            errs.mkString("\n")),        consumer => consumer      )    } else {      kc.connect(part.host, part.port)    }  }  private def handleFetchErr(resp: FetchResponse) {    if (resp.hasError) {      val err = resp.errorCode(part.topic, part.partition)      if (err == ErrorMapping.LeaderNotAvailableCode ||        err == ErrorMapping.NotLeaderForPartitionCode) {        log.error(s"Lost leader for topic ${part.topic} partition ${part.partition}, " +          s" sleeping for ${kc.config.refreshLeaderBackoffMs}ms")        Thread.sleep(kc.config.refreshLeaderBackoffMs)      }      // Let normal rdd retry sort out reconnect attempts      throw ErrorMapping.exceptionFor(err)    }  }  private def fetchBatch: Iterator[MessageAndOffset] = {    val req = new FetchRequestBuilder()      .addFetch(part.topic, part.partition, requestOffset, kc.config.fetchMessageMaxBytes)      .build()    val resp = consumer.fetch(req)    handleFetchErr(resp)    // kafka may return a batch that starts before the requested offset    resp.messageSet(part.topic, part.partition)      .iterator      .dropWhile(_.offset < requestOffset)  }  override def close(): Unit = {    if (consumer != null) {      consumer.close()    }  }  override def getNext(): R = {    if (iter == null || !iter.hasNext) {      iter = fetchBatch    }    if (!iter.hasNext) {      assert(requestOffset == part.untilOffset, errRanOutBeforeEnd(part))      finished = true      null.asInstanceOf[R]    } else {      val item = iter.next()      if (item.offset >= part.untilOffset) {        assert(item.offset == part.untilOffset, errOvershotEnd(item.offset, part))        finished = true        null.asInstanceOf[R]      } else {        requestOffset = item.nextOffset        messageHandler(new MessageAndMetadata(          part.topic, part.partition, item.message, item.offset, keyDecoder, valueDecoder))      }    }  }}

8.  KafkaCluster:封装了与Kafka集群交互

/** * Convenience methods for interacting with a Kafka cluster. * @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), *   NOT zookeeper servers, specified in host1:port1,host2:port2 form */private[spark]class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable {

举例：一般使用KafkaUtils的createDirectStream读取数据。

KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](ssc,kafkaParams,topics)

9.  在使用Kafka的Direct方式操作数据的时候，通过使用

/** * Create an input stream that directly pulls messages from Kafka Brokers * without using any receiver. This stream can guarantee that each message * from Kafka is included in transformations exactly once (see points below). * * Points to note: *  - No receivers: This stream does not use any receiver. It directly queries Kafka *  - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked *    by the stream itself. For interoperability with Kafka monitoring tools that depend on *    Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. *    You can access the offsets used in each batch from the generated RDDs (see *    [[org.apache.spark.streaming.kafka.HasOffsetRanges]]). *  - Failure Recovery: To recover from driver failures, you have to enable checkpointing *    in the [[StreamingContext]]. The information on consumed offset can be *    recovered from the checkpoint. See the programming guide for details (constraints, etc.). *  - End-to-end semantics: This stream ensures that every records is effectively received and *    transformed exactly once, but gives no guarantees on whether the transformed data are *    outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure *    that the output operation is idempotent, or use transactions to output records atomically. *    See the programming guide for more details. * * @param ssc StreamingContext object * @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) (NOT zookeeper servers), specified in *   host1:port1,host2:port2 form. *   If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest" *   to determine where the stream starts (defaults to "largest") * @param topics Names of the topics to consume * @tparam K type of Kafka message key * @tparam V type of Kafka message value * @tparam KD type of Kafka message key decoder * @tparam VD type of Kafka message value decoder * @return DStream of (Kafka message key, Kafka message value) */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)  val kc = new KafkaCluster(kafkaParams)//kc 是Kafka的集群//fromOffsets获取具体的偏移量  val fromOffsets = getFromOffsets(kc, kafkaParams, topics)  new DirectKafkaInputDStream[K, V, KD, VD, (K, V)](    ssc, kafkaParams, fromOffsets, messageHandler)}

    10. getFromOffsets：

private[kafka] def getFromOffsets(    kc: KafkaCluster,    kafkaParams: Map[String, String],    topics: Set[String]  ): Map[TopicAndPartition, Long] = {  val reset = kafkaParams.get("auto.offset.reset").map(_.toLowerCase)  val result = for {    topicPartitions <- kc.getPartitions(topics).right    leaderOffsets <- (if (reset == Some("smallest")) {      kc.getEarliestLeaderOffsets(topicPartitions)    } else {      kc.getLatestLeaderOffsets(topicPartitions)    }).right  } yield {    leaderOffsets.map { case (tp, lo) =>//创建Kafka Direct DStream的时候，他会和Kafka集群进行交互,来获得他的partition和offset的信息。//实质上是通过DirectKafkaInputDStream        (tp, lo.offset)    }  }  KafkaCluster.checkErrors(result)}

    11. 不同topic的partition对应着我们生成的KafkaRDD的partition.

/** *  A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where * each given Kafka topic/partition corresponds to an RDD partition. * The spark configuration spark.streaming.kafka.maxRatePerPartition gives the maximum number *  of messages * per second that each '''partition''' will accept. * Starting offsets are specified in advance, * and this DStream is not responsible for committing offsets, * so that you can control exactly-once semantics. * For an easy interface to Kafka-managed offsets, *  see {@link org.apache.spark.streaming.kafka.KafkaCluster} * @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), *   NOT zookeeper servers, specified in host1:port1,host2:port2 form. * @param fromOffsets per-topic/partition Kafka offsets defining the (inclusive) *  starting point of the stream * @param messageHandler function for translating each message into the desired type */private[streaming]class DirectKafkaInputDStream[  K: ClassTag,  V: ClassTag,  U <: Decoder[K]: ClassTag,  T <: Decoder[V]: ClassTag,  R: ClassTag](    ssc_ : StreamingContext,    val kafkaParams: Map[String, String],    val fromOffsets: Map[TopicAndPartition, Long],    messageHandler: MessageAndMetadata[K, V] => R  ) extends InputDStream[R](ssc_) with Logging {

12. DirectKafkaInputDStream源码如下：每次compute之后会产生KafkaRDD

override def compute(validTime: Time): Option[KafkaRDD[K, V, U, T, R]] = {// untilOffsets需要获取的数据区间。这样你就知道你要计算多少条数据了。val untilOffsets = clamp(latestLeaderOffsets(maxRetries))//创建RDD实例，所以这里DirectKafkaInputDStream和RDD是一一对应的。  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)}

总体流程如下：

采用Direct的好处？
1. Direct方式没有数据缓存，因此不会出现内存溢出。但是如果采用的Receiver的话就需要缓存。
2. 如果采用Receiver的方式的华，不方便做分布式，而Direct方式默认数据就在多台机器上。
3. 在实际操作的时候如果采用Receiver的方式的弊端是假设数据来不及处理，但是Direct就不会，因为是直接读取数据。
4. 语义一致性，Direct的方式数据一定会被执行。