我是spark scala的新手，在尝试运行这个简单的代码（试图从kafka主题中读取内容）时，我在创建direct stream时遇到了一个错误，这表明我为重载函数createdirectstream提供了错误数量的类型参数。下面是我得到错误的那一行

val messages = KafkaUtils.createDirectStream [String, String, StringDecoder, StringDecoder]
(streamingContext, kafkaParams, topicsSet)

下面是完整的代码。

package com.test.spark

import java.util.Properties

import org.apache.spark
import kafka.serializer.StringDecoder

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

import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

object KafkaAirDRsProcess {

  def main(args: Array[String]) {
    val sparkConf = new SparkConf().setAppName("AirDR Kafka to Spark")

    val sc = new SparkContext(sparkConf)
    val streamingContext = new StreamingContext(sc, Seconds(10))

    // Create direct kafka stream with brokers and topics
    val brokers = "10.21.165.145:6667 "
    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)

    val topics="AIRMAIN , dummy"
    val topicsSet = topics.split(",").toSet
    //val topicsSet=topics.map(_.toString).toSet

    val messages = KafkaUtils.createDirectStream [String, String, StringDecoder, StringDecoder]
    (streamingContext, kafkaParams, topicsSet)
    val LinesDStream = messages.map(_._2)
    val AirDRStream= LinesDStream.map(AirDRFilter.parseAirDR)

    AirDRStream.foreachRDD(foreachFunc = rdd => {
      System.out.println("--- New RDD with " + rdd.count() + " records");

      if (rdd.count() > 0) {
        rdd.toDF().registerTempTable("AirDRTemp")
        val FilteredCDR = sqlContext.sql("select * from AirDRTemp"  )

        println("======================print result =================")
        FilteredCDR.show()
      }

    });

    //streamingContext.checkpoint("/tmp/mytest/ckpt/")
    streamingContext.start()
    streamingContext.awaitTermination()
  }
}

下面是intellij错误的快照