Question

嗨，我是Spark和Kafka的新手，我正在编写示例代码来使用Spark使用来自Kafka主题的消息，

object Init {
 def main(args: Array[String]): Unit = {
   val kafkaParams = Map[String, Object](
      "bootstrap.servers" -> "ip-10-0-1-10.ec2.internal:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "12",
      "auto.offset.reset" -> "earliest",
      "enable.auto.commit" -> (false: java.lang.Boolean)
      )
  val topics = Array("TestLogs")
  val stream = KafkaUtils.createDirectStream[String, String](
  SparkConfig.streamContext,
  PreferConsistent,
  Subscribe[String, String](topics, kafkaParams)
  )
  stream.print()
  SparkConfig.streamContext.start()
  SparkConfig.streamContext.awaitTermination()
}
}

当我使用

在群集上运行以上代码时

"spark2-submit  --jars spark-streaming-kafka-0-10_2.11-2.3.0.jar --class Init 
--master local kafkademo_2.11-0.1.jar"

消费者进入无限循环，不打印任何消息，我通过Ctrl + C明确终止进程

INFO consumer.ConsumerConfig: ConsumerConfig values:
    metric.reporters = []
    metadata.max.age.ms = 300000
    partition.assignment.strategy = [org.apache.kafka.clients.consumer.RangeAssignor]
    reconnect.backoff.ms = 50
    sasl.kerberos.ticket.renew.window.factor = 0.8
    max.partition.fetch.bytes = 1048576
    bootstrap.servers = [ip-10-0-1-10.ec2.internal:9092]
    ssl.keystore.type = JKS
    enable.auto.commit = false
    sasl.mechanism = GSSAPI
    interceptor.classes = null
    exclude.internal.topics = true
    ssl.truststore.password = null
    client.id = consumer-1
    ssl.endpoint.identification.algorithm = null
    max.poll.records = 2147483647
    check.crcs = true
    request.timeout.ms = 40000
    heartbeat.interval.ms = 3000
    auto.commit.interval.ms = 5000
    receive.buffer.bytes = 65536
    ssl.truststore.type = JKS
    ssl.truststore.location = null
    ssl.keystore.password = null
    fetch.min.bytes = 1
    send.buffer.bytes = 131072
    value.deserializer = class org.apache.kafka.common.serialization.StringDeserializer
    group.id = 12
    retry.backoff.ms = 100
    ssl.secure.random.implementation = null
    sasl.kerberos.kinit.cmd = /usr/bin/kinit
    sasl.kerberos.service.name = null
    sasl.kerberos.ticket.renew.jitter = 0.05
    ssl.trustmanager.algorithm = PKIX
    ssl.key.password = null
    fetch.max.wait.ms = 500
    sasl.kerberos.min.time.before.relogin = 60000
    connections.max.idle.ms = 540000
    session.timeout.ms = 30000
    metrics.num.samples = 2
    key.deserializer = class org.apache.kafka.common.serialization.StringDeserializer
    ssl.protocol = TLS
    ssl.provider = null
    ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
    ssl.keystore.location = null
    ssl.cipher.suites = null
    security.protocol = PLAINTEXT
    ssl.keymanager.algorithm = SunX509
    metrics.sample.window.ms = 30000
    auto.offset.reset = latest

11/09/11 07:03:05 INFO utils.AppInfoParser：Kafka版本：0.10.0-kafka-2.1.0 18/09/11 07:03:05 INFO utils.AppInfoParser：Kafka commitId：未知

如果我使用控制台用户进行测试，它将显示消息，

kafka-console-consumer --zookeeper ip-10-0-2-11.ec2.internal:2181 --topic 
TestLogs --from-beginning

INFO consumer.ConsumerFetcherManager: [ConsumerFetcherManager-1536650065051] 
Added fetcher for partitions ArrayBuffer([TestLogs-0, initOffset -1 to broker 
BrokerEndPoint(177,ip-10-0-1-10.ec2.internal,9092)] )
Welcome to Kafka APIS

请帮助我解决此问题。

Answer 1

由于依赖性问题，我无法重现您遇到的问题，但是我可以提供示例工作代码，您可以在其中收听任何主题。

import kafka.serializer.{DefaultDecoder, StringDecoder}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark._
import org.apache.spark.streaming.{Seconds, StreamingContext}


object KafkaStreamingConsumer {
  def main(args: Array[String])
  {
    val sparkConf = new SparkConf().setAppName("KafkaStreaming").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    sc.setLogLevel("ERROR")
    val ssc = new StreamingContext(sc, Seconds(10))
    val kafkaConf = Map(
      "metadata.broker.list" -> "ip-10-0-1-10.ec2.internal:9092",
      "zookeeper.connect" -> "localhost:2181",
      "group.id" -> "kafkaSparkStreaming",
      "zookeeper.connection.timeout.ms" -> "1000"
    )

    val message = KafkaUtils.createStream[Array[Byte], String, DefaultDecoder, StringDecoder](
      ssc,
      kafkaConf,
      Map("TestLogs" ->1),
      StorageLevel.MEMORY_ONLY
    )
    val lines = message.map(_._2)
    lines.print()
    ssc.start()
    ssc.awaitTermination()
  }

}

我正在使用Spark Streaming库从Kafka主题流式传输数据。如果您在使用上述代码时发现任何问题，请告诉我。

Answer 2

首先确保主题TestLogs包含数据，然后，如果您已经使用了组ID为12的消息，那么您将仅收到尚未针对该特定组ID提交的新消息：在这种情况下，您可以重播该主题通过重置该组的kafka偏移量或简单地更改组ID（例如13）。

Spark Kafka Consumer不使用主题消息

2 个答案: