Spark Streaming标准示例中缺少必需的配置“ bootstrap.servers”错误

时间：2018-09-26 16:15:14

标签： scala apache-spark apache-kafka spark-streaming spark-structured-streaming

我对Scala和Spark还是陌生的，所以可以自由地评判我，但不要太难。

我正在尝试启动标准DirectKafkaWordCount示例（Spark2安装提供），以测试Spark Streaming如何与Kafka一起使用。

这是示例代码（也可以在here中找到）：

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 */

// scalastyle:off println
package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka010._

/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: DirectKafkaWordCount <brokers> <topics>
 *   <brokers> is a list of one or more Kafka brokers
 *   <topics> is a list of one or more kafka topics to consume from
 *
 * Example:
 *    $ bin/run-example streaming.DirectKafkaWordCount broker1-host:port,broker2-host:port \
 *    topic1,topic2
 */
object DirectKafkaWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println(s"""
        |Usage: DirectKafkaWordCount <brokers> <topics>
        |  <brokers> is a list of one or more Kafka brokers
        |  <topics> is a list of one or more kafka topics to consume from
        |
        """.stripMargin)
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    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](
      ssc,
      LocationStrategies.PreferConsistent,
      ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams))

    // Get the lines, split them into words, count the words and print
    val lines = messages.map(_.value)
    val words = lines.flatMap(_.split(" "))
    val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
    wordCounts.print()

    // Start the computation
    ssc.start()
    ssc.awaitTermination()
  }
}
// scalastyle:on println

在尝试启动它时，我不得不将spark-streaming-kafka-0-10_2.11-2.3.1.jar和kafka-clients-0.10.0.1.jar放到/usr/hdp/3.0.0.0- 1634 / spark2 / jars /目录（这让我有些惊讶，因为我认为安装随附的所有标准示例都必须开箱即用，但WordCount示例要求使用这些软件包）。添加这些罐子之后，我尝试从主题 test 中读取记录，并通过命令进行字数统计

  

/usr/hdp/3.0.0.0-1634/spark2/bin/run-example   stream.DirectKafkaWordCount本地主机：9092测试

应用程序失败，但是，我得到的错误如下：

Exception in thread "main" org.apache.kafka.common.config.ConfigException: Missing required configuration "bootstrap.servers" which has no default value.
        at org.apache.kafka.common.config.ConfigDef.parse(ConfigDef.java:421)
        at org.apache.kafka.common.config.AbstractConfig.<init>(AbstractConfig.java:55)
        at org.apache.kafka.common.config.AbstractConfig.<init>(AbstractConfig.java:62)
        at org.apache.kafka.clients.consumer.ConsumerConfig.<init>(ConsumerConfig.java:376)
        at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:557)
        at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:540)
        at org.apache.spark.streaming.kafka010.Subscribe.onStart(ConsumerStrategy.scala:84)
        at org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.consumer(DirectKafkaInputDStream.scala:70)
        at org.apache.spark.streaming.kafka010.DirectKafkaInputDStream.start(DirectKafkaInputDStream.scala:240)
        at org.apache.spark.streaming.DStreamGraph$$anonfun$start$7.apply(DStreamGraph.scala:54)
        at org.apache.spark.streaming.DStreamGraph$$anonfun$start$7.apply(DStreamGraph.scala:54)
        at scala.collection.parallel.mutable.ParArray$ParArrayIterator.foreach_quick(ParArray.scala:143)
        at scala.collection.parallel.mutable.ParArray$ParArrayIterator.foreach(ParArray.scala:136)
        at scala.collection.parallel.ParIterableLike$Foreach.leaf(ParIterableLike.scala:972)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:49)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)
        at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:51)
        at scala.collection.parallel.ParIterableLike$Foreach.tryLeaf(ParIterableLike.scala:969)
        at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:152)
        at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:443)
        at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
        at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
        at ... run in separate thread using org.apache.spark.util.ThreadUtils ... ()
        at org.apache.spark.streaming.StreamingContext.liftedTree1$1(StreamingContext.scala:578)
        at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:572)
        at org.apache.spark.examples.streaming.DirectKafkaWordCount$.main(DirectKafkaWordCount.scala:70)
        at org.apache.spark.examples.streaming.DirectKafkaWordCount.main(DirectKafkaWordCount.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:904)
        at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

这使我感到困惑，因为我在启动命令中提供了引导服务器（localhost：9092）。有什么想法可以从这里挖掘吗？

我的配置：

火花-2.3.1

卡夫卡-2.11-1.0.1

3 个答案:

答案 0 :(得分：1)

您需要在kafka参数中添加df = df.withColumn('myCol', when(col('myCol') == '', None).otherwise(col('myCol'))) ，因为使用者需要引导服务器来使用任何主题的消息。 bootstrap.servers。

spark-streaming-kafka-0-10_2.11-2.3.1.jar

资源： https://spark.apache.org/docs/latest/streaming-kafka-0-10-integration.html#creating-a-direct-stream

答案 1 :(得分：1)

该示例已经一年多了没有更新，但是您似乎需要将metadata.broker.list重命名为bootstrap.servers，这是所有其他Kafka客户使用的属性名称。

我不确定run-example脚本是否正确传递了参数，但是您需要提供Kafka代理的外部IP或主机名，而不是localhost。

此外，建议在Spark2 +中通过DStream和RDD使用结构化流和Dataframe API

答案 2 :(得分：0)

以防万一，如果您正在使用kafka进行春季启动，并且遇到此错误

  

org.apache.kafka.common.config.ConfigException：缺少必需的配置“ bootstrap.servers”，该配置没有默认值。

确保已准备好以下这些东西：

  1. spring.kafka.bootstrap-servers在poperrty或yml文件中设置此属性。
  2. Zookeeper和kafka服务器正在运行。
  3. 通过此命令“ kafka-console-consumer.bat / sh”运行消费者（根据操作系统）。
  4. spring.kafka.consumer.group-id需要设置。
  5. spring.kafka.consumer.auto-offset-reset =最早

这对某人有帮助。

谢谢

Atul

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