Flink（1.13.6版本） sql完整从kafka读取嵌套json数据并解析后分别写入kafka和mysql实例代码

(1) 添加依赖

	org.apache.flink
	flink-connector-kafka_2.11
	1.13.6



	com.alibaba
	fastjson
	1.2.51




	org.apache.flink
	flink-connector-jdbc_2.11
	1.13.6


	mysql
	mysql-connector-java
	5.1.35



	org.apache.flink
	flink-table-api-scala-bridge_2.12
	1.13.6


	org.apache.flink
	flink-table-planner-blink_2.12
	1.13.6


	org.apache.flink
	flink-table-common
	1.13.6

（2）读取kafka数据，kafka数据为嵌套json格式

package com.test

import org.apache.flink.streaming.api.scala._
import org.apache.flink.table.api._
import org.apache.flink.table.api.bridge.scala._
import org.apache.flink.types.Row



object realtime_data_to_kafka_mysql {

  def main(args: Array[String]): Unit = {

    //创建flink流处理环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    val settings = EnvironmentSettings
      .newInstance()
      .useBlinkPlanner()
      .inStreamingMode()
      .build()
    val tableEnv = StreamTableEnvironment.create(env, settings)
	
	//创建作业名称
	tableEnv.getConfig().getConfiguration().setString("pipeline.name","sink_to_kafka_mysql")

    //创建kafka数据源
    //1.result_list为数组结构，包含2个row，每个row包含2个字段
    tableEnv.executeSql("" +
      "create table input_table (" +
      "device string," +
      "id string," +
      "`type` string," +
      "result_list array>)"+
      "with (" +
      "'connector'='kafka'," +
      "'topic'='topic_name'," +
      "'properties.bootstrap.servers'='192.168.xx.xx1:9092,192.168.xx.xx2:9092'," +
      "'properties.group.id'='testGroup'," +
      "'scan.startup.mode'='latest-offset'," +
      "'format'='json'" +
      ")"
    )

    //1.result_list为数组结构，且sql访问数组的下标从1开始，这里取出第一个row
    val inputTable = tableEnv.sqlQuery("" +
      "select " +
      "device,id,`type`,result_list[1].x1 as x1, result_list[1].y1 as y1 " +
      "from input_table")
      
    //注册临时表
    tableEnv.createTemporaryView("input_table",inputTable)

    //原始数据存入mysql数据库中
    tableEnv.executeSql(
      "create table realtime_raw_tmp(" +
        "device string," +
        "id string," +
        "`type` string," +
        "x1 string," +
        "y1 string" 
        ") with (" +
        "'connector'='jdbc'," +
        "'driver'='com.mysql.jdbc.Driver'," +
        "'url'='jdbc:mysql://192.168.xx.xxx:3306/数据库名'," +
        "'table-name'='realtime_raw_table'," + //提前在mysql数据库中建好realtime_raw_table表
        "'username'='用户名'," +
        "'password'='密码')"
    )
    tableEnv.executeSql(
      "insert into realtime_raw_tmp " +
      "select * from  input_table")


    //写入kafka
    //创建kafka Sink
    tableEnv.executeSql(
      "create table output_table(" +
        "device string," +
        "id string," +
        "`type` string," +
        "x1 string," +
        "y1 string)" +
        "with (" +
        "'connector'='kafka'," +
        "'topic'='topic_1'," + //在kafka中创建新topic_1
        "'properties.bootstrap.servers'='192.168.xx.xx1:9092,192.168.xx.xx2:9092'," +
        "'format'='json'," +
        "'sink.partitioner'='round-robin'" +
        ")"
    )

    //向kafka sink写入数据
    tableEnv.executeSql("insert into output_table select * from " + inputTable)

  }

}