卡夫卡制作人挂在发送 [英] Kafka producer hangs on send

问题描述

逻辑是，从自定义源获取数据的流式作业必须同时写入 Kafka 和 HDFS.

The logic is that a streaming job, getting data from a custom source has to write both to Kafka as well as HDFS.

我编写了一个(非常)基本的 Kafka 生产者来执行此操作，但是整个流工作都挂在 send 方法上.

I wrote a (very) basic Kafka producer to do this, however the whole streaming job hangs on the send method.

class KafkaProducer(val kafkaBootstrapServers: String, val kafkaTopic: String, val sslCertificatePath: String, val sslCertificatePassword: String) {

  val kafkaProps: Properties = new Properties()
  kafkaProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, kafkaBootstrapServers)
  kafkaProps.put("acks", "1")
  kafkaProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")
  kafkaProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")
  kafkaProps.put("ssl.truststore.location", sslCertificatePath)
  kafkaProps.put("ssl.truststore.password", sslCertificatePassword)

  val kafkaProducer: KafkaProducer[Long, Array[String]] = new KafkaProducer(kafkaProps)

  def sendKafkaMessage(message: Message): Unit = {
    message.data.foreach(list => {
      val producerRecord: ProducerRecord[Long, Array[String]] = new ProducerRecord[Long, Array[String]](kafkaTopic, message.timeStamp.getTime, list.toArray)
      kafkaProducer.send(producerRecord)
    })
  }
}

以及调用生产者的代码:

And the code calling the producer:

receiverStream.foreachRDD(rdd => {
      val messageRowRDD: RDD[Row] = rdd.mapPartitions(partition => {
        val parser: Parser = new Parser
        val kafkaProducer: KafkaProducer = new KafkaProducer(kafkaBootstrapServers, kafkaTopic, kafkaSslCertificatePath, kafkaSslCertificatePass)
        val newPartition = partition.map(message => {
          Logger.getLogger("importer").error("Writing Message to Kafka...")
          kafkaProducer.sendKafkaMessage(message)
          Logger.getLogger("importer").error("Finished writing Message to Kafka")
          Message.data.map(singleMessage => parser.parseMessage(Message.timeStamp.getTime, singleMessage))
        })
        newPartition.flatten
      })

      val df = sqlContext.createDataFrame(messageRowRDD, Schema.messageSchema)

      Logger.getLogger("importer").info("Entries-count: " + df.count())
      val row = Try(df.first)

      row match {
        case Success(s) => Persister.writeDataframeToDisk(df, outputFolder)
        case Failure(e) => Logger.getLogger("importer").warn("Resulting DataFrame is empty. Nothing can be written")
      }
    })

从日志中我可以看出每个执行程序都到达了发送到 kafka"点，但不会再进一步​​了.所有执行者都挂在那里，没有抛出异常.

From the logs I can tell that each executor is reaching the "sending to kafka" point, however not any further. All executors hang on that and no exception is thrown.

Message 类是一个非常简单的 case 类，有 2 个字段、一个时间戳和一个字符串数组.

The Message class is a very simple case class with 2 fields, a timestamp and an array of strings.

推荐答案

这是由于 Kafka 中的 acks 字段造成的.

This was due to the acks field in Kafka.

Acks 设置为 1，发送速度更快.

Acks was set to 1 and sends went ahead a lot faster.

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