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()

  }

}

我使用intellijide，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：
error: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org.apache.spark包中的类型日志，因为缺少该类型日志（或其依赖项）。检查生成定义是否缺少依赖项或存在冲突依赖项(重新运行 -Ylog-classpath 如果“streamingcontext.class”是针对org.apache.spark的不兼容版本编译的，则完整重建可能会有所帮助。
error: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org.apache.spark包中的类型日志，因为缺少该类型日志（或其依赖项）。检查生成定义是否缺少依赖项或存在冲突依赖项(重新运行 -Ylog-classpath 如果“dstream.class”是针对org.apache.spark的不兼容版本编译的，则完整重建可能会有所帮助。