Я немного новичок в Scala и Spark, поэтому не стесняйтесь судить меня, но не слишком сложно.
Я пытаюсь запустить стандартный пример DirectKafkaWordCount (поставляется с установкой Spark2), чтобы проверить, как Spark Streamingработает с Kafka.
Это код примера (также можно найти здесь ):
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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
При попытке запустить его пришлось поставитьspark-streaming-kafka-0-10_2.11-2.3.1.jar и kafka-clients-0.10.0.1.jar в каталог /usr/hdp/3.0.0.0-1634/spark2/jars/ (что меня несколько удивило,так как я предполагал, что все стандартные примеры, предоставляемые с установкой, должны были работать из коробки, но пример WordCount требовался для этих пакетов).После добавления этих jar-файлов я попытался прочитать записи из темы test и выполнить подсчет слов с помощью команды
/ usr / hdp / 3.0.0.0-1634 / spark2 / bin /run-example streaming.DirectKafkaWordCount localhost: 9092 test
Однако происходит сбой приложения, и полученная ошибка выглядит следующим образом:
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)
Это смущает меня, так как у меня естьпредоставил загрузочный сервер (localhost: 9092) в команде запуска.Есть идеи, куда копать отсюда?
Моя конфигурация:
Spark - 2.3.1
Кафка - 2.11-1.0.1