Spark Streaming +Kafka 使用底层API直接读取Kafka的Partition数据，手动更新Offset到Zookeeper集群

时间：2021-06-08 20:57:53

      Spark Streaming  +Kafka 使用底层API直接读取Kafka的Partition数据，正常Offset存储在CheckPoint中。但是这样无法实现Kafka监控工具对Kafka的监控，所以手动更新Offset到Zookeeper集群中


相关源码简单介绍：
1:TopicAndPartition是对 topic和partition的id的封装的一个样例类
  case class TopicAndPartition(topic: String, partitionId: Int)

2：OffsetRange 是对topic name，partition id，fromOffset（当前消费的开始偏移），untilOffset（当前消费的结束偏移）的封装。所以OffsetRange 包含信息有：topic名字，分区Id，开始偏移，结束偏移。
 /**
   *
   * @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


3：代码实现：
object Iteblog {

  val brokerAddress = "http://www.iteblog.com:9092"

  val groupID="testGroup"

  val kafkaParams = Map[String, String](
    "metadata.broker.list" -> brokerAddress,
    "group.id" -> "iteblog")

  def main(args: Array[String]) {

    val sparkConf = new SparkConf().setAppName("Test")
    sparkConf.set("spark.kryo.registrator", "utils.CpcKryoSerializer")
    val sc = new SparkContext(sparkConf)


    val ssc = new StreamingContext(sc, Seconds(2))
    val topicsSet = Set("iteblog")

    val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
    messages.foreachRDD(rdd => {
      // 把RDD转成HasOffsetRanges类型（KafkaRDD extends HasOffsetRanges）
      // OffsetRange 说明：Represents a range of offsets from a single Kafka TopicAndPartition.
      // OffsetRange 说明： Instances of this class can be created with `OffsetRange.create()`.
      val offsetsList: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
//     offsetRanges 的实现代码（KafkaRDD中）：tp：TopicAndPartition,fo:fromOffset
//      val offsetRanges = fromOffsets.map { case (tp, fo) =>
//        val uo = untilOffsets(tp)
//        OffsetRange(tp.topic, tp.partition, fo, uo.offset)
//      }.toArray

      val kc = new KafkaCluster(kafkaParams)
      for (offsets <- offsetsList) {
       //TopicAndPartition 主构造参数第一个是topic，第二个是 partition id
        val topicAndPartition = TopicAndPartition("iteblog", offsets.partition) //offsets.partition表示的是Kafka partition id
        val o = kc.setConsumerOffsets(groupID, Map((topicAndPartition, offsets.untilOffset)))//offsets.untilOffset:是
        if (o.isLeft) {
          println(s"Error updating the offset to Kafka cluster: ${o.left.get}")
        }
      }
    })

    ssc.start()
    ssc.awaitTermination()
    ssc.stop()
  }
}

核心代码讲解：
使用KafkaUtils的createDirectStream方法，调用底层API直接消费Kafka Partition的数据（Kafka Partition和RDD Partition 一一对应）。createDirectStream返回值是DStream，底层是RDD。
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)

messages.foreachRDD 是对messages底层RDD计算其偏移范围。
KafkaRDD和HasOffsetRanges关系(构造参数和泛型省略，具体见源码)：
KafkaRDD extends RDD[R](sc, Nil) with Logging with HasOffsetRanges

//rdd是messages.foreachRDD中的变量，rdd其类型是KafkaRDD，但是由于多态的原因rdd实际上不是KafkaRDD类型，而是RDD类型，所以需要向下转型为HasOffsetRanges，调用offsetRanges方法。（回忆OffsetRange是对什么的封装？答案：topic名字，分区Id，开始偏移，结束偏移。）
val offsetsList: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
offsetRanges 的实现代码（KafkaRDD中）：tp：TopicAndPartition,fo:fromOffset
val offsetRanges = fromOffsets.map { case (tp, fo) =>   val uo = untilOffsets(tp)   OffsetRange(tp.topic, tp.partition, fo, uo.offset)}.toArray

KafkaCluster 完成对更新偏移到zookeeper集群的封装工具类。
val kc = new KafkaCluster(kafkaParams)

 

对offsetRanges数组遍历：setConsumerOffsets是KafkaCluster 
 

强调：OffsetRange是对什么的封装？答案：topic名字，分区Id，开始偏移，结束偏移
 

for (offsets <- offsetsList) {
    //offsets.untilOffset是结束偏移量
   val o = kc.setConsumerOffsets(groupID, Map((topicAndPartition, offsets.untilOffset)))        if (o.isLeft) {          println(s"Error updating the offset to Kafka cluster: ${o.left.get}")        } }
以上是完成对createDirectStream创建的DStream中的一个底层RDD完成偏移的更新到zookeeper集群，通过foreachRDD完成对所有RDD的更新！！！至此已经完成RDD偏移的计算以及更新，但是具体的更新方法？在KafkaCluster中。接下来看KafkaCluster代码。
直接上代码：
package org.apache.spark.streaming.kafka//使用org.apache.spark.streaming.kafka的原因：  private[spark] object SimpleConsumerConfig限制只在spark包中使用！import kafka.api.OffsetCommitRequestimport kafka.common.{ErrorMapping, OffsetMetadataAndError, TopicAndPartition}import kafka.consumer.SimpleConsumerimport org.apache.spark.SparkExceptionimport org.apache.spark.streaming.kafka.KafkaCluster.SimpleConsumerConfigimport scala.collection.mutable.ArrayBufferimport scala.util.Randomimport scala.util.control.NonFatalclass KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable {                 //Err是类型ArrayBuffer[Throwable]别名   type Err = ArrayBuffer[Throwable]   @transient private var _config: SimpleConsumerConfig = null   def config: SimpleConsumerConfig = this.synchronized {     if (_config == null) {       //SimpleConsumerConfig的apply方法部分代码：       //val brokers = kafkaParams.get("metadata.broker.list").orElse(kafkaParams.get("bootstrap.servers"))       //所以kafkaParams必须包含key=metadata.broker.list或者bootstrap.servers对应的Value       _config = SimpleConsumerConfig(kafkaParams)     }     _config   }  /**    *    * @param groupId: String    * @param offsets: Map[TopicAndPartition, Long]    * @return    */   def setConsumerOffsets(groupId: String,                          offsets: Map[TopicAndPartition, Long]                           ): Either[Err, Map[TopicAndPartition, Short]] = {     setConsumerOffsetMetadata(groupId, offsets.map { kv =>       kv._1 -> OffsetMetadataAndError(kv._2)     })   }   def setConsumerOffsetMetadata(groupId: String,                                 metadata: Map[TopicAndPartition, OffsetMetadataAndError]                                  ): Either[Err, Map[TopicAndPartition, Short]] = {     var result = Map[TopicAndPartition, Short]()     val req = OffsetCommitRequest(groupId, metadata)     val errs = new Err     val topicAndPartitions = metadata.keySet     withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer =>       val resp = consumer.commitOffsets(req)       val respMap = resp.requestInfo       val needed = topicAndPartitions.diff(result.keySet)       needed.foreach { tp: TopicAndPartition =>         respMap.get(tp).foreach { err: Short =>           if (err == ErrorMapping.NoError) {             result += tp -> err           } else {             errs.append(ErrorMapping.exceptionFor(err))           }         }       }       if (result.keys.size == topicAndPartitions.size) {         return Right(result)       }     }     val missing = topicAndPartitions.diff(result.keySet)     errs.append(new SparkException(s"Couldn't set offsets for ${missing}"))     Left(errs)   }   private def withBrokers(brokers: Iterable[(String, Int)], errs: Err)                          (fn: SimpleConsumer => Any): Unit = {     brokers.foreach { hp =>       var consumer: SimpleConsumer = null       try {         consumer = connect(hp._1, hp._2)         fn(consumer)       } catch {         case NonFatal(e) =>           errs.append(e)       } finally {         if (consumer != null) {           consumer.close()         }       }     }   }   def connect(host: String, port: Int): SimpleConsumer =     new SimpleConsumer(host, port, config.socketTimeoutMs,       config.socketReceiveBufferBytes, config.clientId) }


小拓展：
Map的连续获取值用法：
object MapDemo extends App {  val map = Map("1" -> "11", "2" -> "22", "3" -> "33")  map.map {    case (a, b) =>      println(a + " " + b)  }//语法：先获取key=4的value，如果存在返回否则获取key=5的value，如果存在返回，不存在的话直接异常。  val result = map.get("4").orElse(map.get("5")).getOrElse(throw new Exception("exception"))  println(result)}


核心代码讲解之SimpleConsumerConfig的apply方法：
    /**     * Make a consumer config without requiring group.id or zookeeper.connect,     * since communicating with brokers also needs common settings such as timeout     */    def apply(kafkaParams: Map[String, String]): SimpleConsumerConfig = {// These keys are from other pre-existing kafka configs for specifying brokers,accept either     // map连续获取值用法见上面示例      val brokers = kafkaParams.get("metadata.broker.list")        .orElse(kafkaParams.get("bootstrap.servers"))        .getOrElse(throw new SparkException(          "Must specify metadata.broker.list or bootstrap.servers"))      val props = new Properties()      kafkaParams.foreach { case (key, value) =>        // prevent warnings on parameters ConsumerConfig doesn't know about        if (key != "metadata.broker.list" && key != "bootstrap.servers") {          props.put(key, value)        }      }     //如果没有zookeeper.connect和group.id，设置其value为空字符串。      Seq("zookeeper.connect", "group.id").foreach { s =>        if (!props.contains(s)) {          props.setProperty(s, "")        }      }      new SimpleConsumerConfig(brokers, props)    }
OffsetMetadataAndError样例类：
case class OffsetMetadataAndError(offsetMetadata: OffsetMetadata, error: Short = Errors.NONE.code) {  def offset = offsetMetadata.offset  def metadata = offsetMetadata.metadata  override def toString = "[%s,ErrorCode %d]".format(offsetMetadata, error)}



TopicAndPartition是对 topic和partition的id的封装的一个样例类
 /**    *    * @param groupId: String    * @param offsets: Map[TopicAndPartition, Long]
    * Map中key和value含义TopicAndPartition 是topic和partition id封装long是消费的结束偏移
    * @return    */ def setConsumerOffsets(groupId: String,offsets: Map[TopicAndPartition, Long]): Either[Err,  Map[TopicAndPartition, Short]] = {     setConsumerOffsetMetadata(groupId, offsets.map { kv =>       kv._1 -> OffsetMetadataAndError(kv._2)     })   }



具体更多细节见代码注释吧，写的很详细。就不在重复贴在博客了。
github地址：https://github.com/Dax1n/spark-kafka-directstream-zookeeper


   


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