千家信息网

第15课:Spark Streaming源码解读之No Receivers彻底思考

发表于:2024-11-15 作者:千家信息网编辑
千家信息网最后更新 2024年11月15日,本期内容:Direct AccessKafka前面有几期我们讲了带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N
千家信息网最后更新 2024年11月15日第15课:Spark Streaming源码解读之No Receivers彻底思考

本期内容:

  • Direct Access

  • Kafka

前面有几期我们讲了带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. 更强的控制自由度

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:

object DirectKafkaWordCount {  def main(args: Array[String]) {    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会封装一个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  * configuration parameters. 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],    leaders: Map[TopicAndPartition, (String, Int)],    messageHandler: MessageAndMetadata[K, V] => R  ) extends RDD[R](sc, Nil) with Logging with HasOffsetRanges {  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  }...  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)    }  }

RDD中重要的方法 getPartitions 和 compute 其中compute中返回了一个 KafkaRDDIterator:

private class KafkaRDDIterator(      part: KafkaRDDPartition,      context: TaskContext) extends NextIterator[R] {    val kc = new KafkaCluster(kafkaParams)...    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))        }      }    }  }

其中会调用KafkaCluster的connect方法:

org/apache/spark/streaming/kafka/KafkaCluster.scala  def connect(host: String, port: Int): SimpleConsumer =    new SimpleConsumer(host, port, config.socketTimeoutMs,      config.socketReceiveBufferBytes, config.clientId)

KafkaCluster的connect方法返回了一个 SimpleConsumer,如果想自定义控制kafka消息的消费,则可自定义Kafka的consumer。

我们再回过头看看:

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

实际生成了什么:

 def createDirectStream[    K: ClassTag,    V: ClassTag,    KD <: Decoder[K]: ClassTag,    VD <: Decoder[V]: ClassTag,    R: ClassTag] (      ssc: StreamingContext,      kafkaParams: Map[String, String],      fromOffsets: Map[TopicAndPartition, Long],      messageHandler: MessageAndMetadata[K, V] => R  ): InputDStream[R] = {    val cleanedHandler = ssc.sc.clean(messageHandler)    new DirectKafkaInputDStream[K, V, KD, VD, R]      ssc, kafkaParams, fromOffsets, cleanedHandler)  }

生成了一个DirectKafkaInputDStream:

org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala override def compute(validTime: Time): Option[KafkaRDD[K, V, U, T, R]] = {    val untilOffsets = clamp(latestLeaderOffsets(maxRetries))    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)  }

这里面即产生了KafkaRDD实例。

我们再重新思考有Receiver和No Receiver的Spark Streaming应用 Direct访问的好处:

1. 不需要缓存,不会出现OOM等问题(数据缓存在Kafka中)

2. 如果采用Receiver的方式,Receiver和Worker上Executor绑定了,不方便做分布式(配置一下也可以做)。如果采用Direct的方式,直接是RDD操作,数据默认分布在多个Executor上,天然就是分布式的。

3. 数据消费的问题,在实际操作的时候,如果采用Receiver的方式,如果数据操作来不及消费,Delay多次之后,Spark Streaming程序有可能崩溃。如果是Direct的方式,就不会。

4. 完全的语义一致性,不会重复消费,且只被消费一次。



备注:

1、DT大数据梦工厂微信公众号DT_Spark
2、IMF晚8点大数据实战YY直播频道号:68917580
3、新浪微博: http://www.weibo.com/ilovepains

0