1 代码如下

// 创建推送kafka的对象
    val kafkaProducer: Broadcast[KafkaSink[String, String]] = {

      val kafkaProducerConfig = {
        val p = new Properties()
        p.setProperty("bootstrap.servers", "25.50.192.184:20092,25.50.192.185:20094,25.50.192.186:20093")
        p.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
        p.setProperty("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
        p
      }
      //SparkSession=ss   KafkaSink工具类在下面 
      ss.sparkContext.broadcast(KafkaSink[String, String](kafkaProducerConfig))
    }
    //aMpowerCurve2 是一个Dataframe
    //将dafaframe中的字段拿出来 放到数组中
    val arrcolumn: Array[String] =aMpowerCurve2.schema.fieldNames
    //对dataframe中的每条数据进行处理
    aMpowerCurve2.foreachPartition(row => {
      row.foreach((data: Row) => {
        val dataMap = new java.util.HashMap[String, Object]()
        for (a <- arrcolumn) {
          //将每个字段的名和字段对应的值 写入Map中
          dataMap.put(a, data.getAs[String](a))
        }
        //将数据（Map）转化成json格式
        //需要下载依赖 并导包import org.json.JSONObject
        val mesJson = new JSONObject(dataMap)
        //将数据写入对应的topic
        val rm = kafkaProducer.value.send("cj_zhny_cons_load", mesJson.toString())
        val recordMetadata = rm.get();
        val topicname = recordMetadata.topic();
        val partition = recordMetadata.partition();
        val offset = recordMetadata.offset();
        println("topicname:" + topicname + "partition:" + partition + "offset:" + offset);
      })
    })





//kafkaSink工具类(公司前辈写的)
package utils.kafka

import org.apache.kafka.clients.producer.{ KafkaProducer, ProducerRecord, RecordMetadata }

class KafkaSink[K, V](createProducer: () => KafkaProducer[K, V]) extends Serializable {
  lazy val producer = createProducer()
  def send(topic: String, key: K, value: V): java.util.concurrent.Future[RecordMetadata] =
    producer.send(new ProducerRecord[K, V](topic, key, value))

  def send(topic: String, value: V): java.util.concurrent.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 {
        producer.close()
      }
      producer
    }
    new KafkaSink(createProducerFunc)
  }
  def apply[K, V](config: java.util.Properties): KafkaSink[K, V] = apply(config.toMap)
}