新的API比较好用。设置一个Json的Format，设置一个schema。和spark的方式有点像。读取，就完事了。自动注册成相应schema的表。
更多内容参考：
https://ci.apache.org/projects/flink/flink-docs-release-1.6/dev/table/connect.html#kafka-connector

package org.apache.flink.streaming.scala.examples.kafka

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.{TableEnvironment, Types}
import org.apache.flink.table.descriptors.{Json, Kafka, Rowtime, Schema}
import org.apache.flink.types.Row
import org.apache.flink.streaming.api.scala._

object KafkaJsonConnector {

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // create a TableEnvironment for streaming queries
    val tableEnv = TableEnvironment.getTableEnvironment(env)

    tableEnv
      .connect(
        new Kafka()
          .version("0.10")
          .topic("SM_USER_PROFILE")
          .startFromEarliest()
          .property("zookeeper.connect", "localhost:2181")
          .property("bootstrap.servers", "localhost:9092"))
      .withFormat(
        new Json()
          .deriveSchema()
      )
      .withSchema(
        new Schema()
          .field("COD_USERNO","string")
          .field("COD_USER_ID","string")
      )
      .inAppendMode()
      .registerTableSource("sm_user")

    val stream = tableEnv.scan("sm_user")
    tableEnv.toAppendStream[Row](stream).print().setParallelism(1)
    env.execute("example")
  }

}