检测到Spark-Kafka无效依赖项

Question

我有一个基本的Spark - Kafka代码，我尝试运行以下代码：

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel

import java.util.regex.Pattern
import java.util.regex.Matcher
import org.apache.spark.streaming.kafka._
import kafka.serializer.StringDecoder
import Utilities._
object WordCount {
  def main(args: Array[String]): Unit = {

    val ssc = new StreamingContext("local[*]", "KafkaExample", Seconds(1))

    setupLogging()

    // Construct a regular expression (regex) to extract fields from raw Apache log lines
    val pattern = apacheLogPattern()

    // hostname:port for Kafka brokers, not Zookeeper
    val kafkaParams = Map("metadata.broker.list" -> "localhost:9092")
    // List of topics you want to listen for from Kafka
    val topics = List("testLogs").toSet
    // Create our Kafka stream, which will contain (topic,message) pairs. We tack a
    // map(_._2) at the end in order to only get the messages, which contain individual
    // lines of data.
    val lines = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, topics).map(_._2)

    // Extract the request field from each log line
    val requests = lines.map(x => {val matcher:Matcher = pattern.matcher(x); if (matcher.matches()) matcher.group(5)})

    // Extract the URL from the request
    val urls = requests.map(x => {val arr = x.toString().split(" "); if (arr.size == 3) arr(1) else "[error]"})

    // Reduce by URL over a 5-minute window sliding every second
    val urlCounts = urls.map(x => (x, 1)).reduceByKeyAndWindow(_ + _, _ - _, Seconds(300), Seconds(1))

    // Sort and print the results
    val sortedResults = urlCounts.transform(rdd => rdd.sortBy(x => x._2, false))
    sortedResults.print()

    // Kick it off
    ssc.checkpoint("/home/")
    ssc.start()
    ssc.awaitTermination()

  }


}

我正在使用IntelliJ IDE，并使用sbt创建scala项目。 build.sbt文件的详细信息如下：

name := "Sample"

version := "1.0"

organization := "com.sundogsoftware"

scalaVersion := "2.11.8"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "2.2.0" % "provided",
  "org.apache.spark" %% "spark-streaming" % "1.4.1",
  "org.apache.spark" %% "spark-streaming-kafka" % "1.4.1",
  "org.apache.hadoop" % "hadoop-hdfs" % "2.6.0"

)

但是，当我尝试构建代码时，会产生以下错误：

错误：scalac：加载类文件'StreamingContext.class'时检测到缺少或无效的依赖项。无法访问类型Logging in package org.apache.spark，因为它（或其依赖项）缺失。检查构建定义是否存在缺失或冲突的依赖项。 （重新运行-Ylog-classpath以查看有问题的类路径。）如果针对不兼容的org.apache.spark版本编译了“StreamingContext.class”，则完全重建可能会有所帮助。

错误：scalac：加载类文件'DStream.class'时检测到缺少或无效的依赖项。无法访问类型Logging in package org.apache.spark，因为它（或其依赖项）缺失。检查构建定义是否存在缺失或冲突的依赖项。 （重新运行-Ylog-classpath以查看有问题的类路径。）如果针对不兼容的org.apache.spark版本编译了“DStream.class”，则完全重建可能会有所帮助。

Answer 1

当同时使用不同的Spark库时，所有库的版本应始终匹配。

另外，你使用的kafka版本也很重要，例如：spark-streaming-kafka-0-10_2.11

...
scalaVersion := "2.11.8"
val sparkVersion = "2.2.0"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % sparkVersion % "provided",
  "org.apache.spark" %% "spark-streaming" % sparkVersion,
  "org.apache.spark" %% "spark-streaming-kafka-0-10_2.11" % sparkVersion,
  "org.apache.hadoop" % "hadoop-hdfs" % "2.6.0"

)

如果您需要检查应该使用的确切依赖项，这是一个有用的站点：https://search.maven.org/