spark RDD只能通过原生API去写。不是spark streaming哦。

导maven包：

这一步不能复制粘贴，要看看你机器的kafka版本是多少。然后去下载对应的包

        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.10</artifactId>
            <version>0.9.0.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.9.0.0</version>
        </dependency>

导包：

WriteToKafka的包

import java.util.Properties

import org.apache.kafka.common.serialization.StringSerializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

KafkaSink的包

import java.util.concurrent.Future
import org.apache.kafka.clients.producer.{ KafkaProducer, ProducerRecord, RecordMetadata }

代码：

复制粘贴即可，记得修改为你的kafka地址。

object WriteToKafka {
  def main(args: Array[String]): Unit = {
      Logger.getRootLogger.setLevel(Level.WARN)//设置log显示级别的，报错就把这个删了
      val conf=new SparkConf().setMaster("local").setAppName("app")
      val sc:SparkContext=new SparkContext(conf)
      val rdd:RDD[String]=sc.parallelize(Array("1","2","3","4"))
      // 广播KafkaSink
      val kafkaProducer: Broadcast[KafkaSink[String, String]] = {
        val kafkaProducerConfig = {
          val p = new Properties()
          p.setProperty("bootstrap.servers", "192.168.163.120:9092")//修改为你的kafka地址
          p.setProperty("key.serializer", classOf[StringSerializer].getName)
          p.setProperty("value.serializer", classOf[StringSerializer].getName)
          p
        }
      sc.broadcast(KafkaSink[String, String](kafkaProducerConfig))
    }
    rdd.foreach(record=>{
      kafkaProducer.value.send("test",record)
    })
  }
}

class KafkaSink[K, V](createProducer: () => KafkaProducer[K, V]) extends Serializable {
  /* This is the key idea that allows us to work around running into
     NotSerializableExceptions. */
  lazy val producer = createProducer()
  def send(topic: String, key: K, value: V): Future[RecordMetadata] =
    producer.send(new ProducerRecord[K, V](topic, key, value))
  def send(topic: String, value: V): Future[RecordMetadata] =
    producer.send(new ProducerRecord[K, V](topic, value))
}

object KafkaSink {
  import scala.collection.JavaConversions._
  def apply[K, V](config: Map[String, Object]): KafkaSink[K, V] = {
    val createProducerFunc = () => {
      val producer = new KafkaProducer[K, V](config)
      sys.addShutdownHook {
        // Ensure that, on executor JVM shutdown, the Kafka producer sends
        // any buffered messages to Kafka before shutting down.
        producer.close()
      }
      producer
    }
    new KafkaSink(createProducerFunc)
  }
  def apply[K, V](config: java.util.Properties): KafkaSink[K, V] = apply(config.toMap)
}

参考其他博主的，不过忘了在哪里找到的了，想起了会补上来的

希望能帮到有需要的朋友。