将json数据映射成对象的处理办法

    case class Person(channel: String,                      IP: String,                      mid: String,                      user_id_temp: String,                      user_id: String,                      request_type: String,                      request_method: String,                      access_time: String) extends Serializable {      private val serialVersionUID = 7247714666080613254L    }val conf = new SparkConf()    //    conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")    //    conf.registerKryoClasses(Array(classOf[Person]))    val spark = SparkSession.builder().master("local[*]").appName("sql").config(conf).getOrCreate()    val input5 = spark.sparkContext.textFile("file:///C:/.../Request", 100)    val input = input5.flatMap(line => {      implicit val formats = Serialization.formats(ShortTypeHints(List()))  //序列化      val data = parse(line)      //解析json成对象      val eventa = data.extract[Person]      Some(eventa)    }).persist()    input.foreachPartition(s => {      val topic = "xx"      val brokers = "xx"      val props = new Properties()      props.put("metadata.broker.list", brokers)      props.put("serializer.class", "kafka.serializer.StringEncoder")      val kafkaConfig = new ProducerConfig(props)      val producer = new Producer[String, Object](kafkaConfig)      val event = new JSONObject()      while(s.hasNext){        val x = s.next()        event          .put("access_time", x.access_time)          .put("channel", x.channel) //          .put("ip", x.IP) //          .put("mid", x.mid) //          .put("user_id", x.user_id)          .put("user_id_temp", x.user_id_temp)          .put("request_type", x.request_type)          .put("request_method", x.request_method)        println(event)        producer.send(new KeyedMessage[String, Object](topic, event.toString))        Thread.sleep(100)      }