【1】引入第三方Bahir提供的Flink-redis相关依赖包

    org.apache.bahir
    flink-connector-redis_2.11
    1.0

【2】Flink连接Redis并输出Sink处理结果

package com.zzx.flink
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.redis.RedisSink
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig
import org.apache.flink.streaming.connectors.redis.common.mapper.{RedisCommand, RedisCommandDescription, RedisMapper}
object RedisSinkTest {
  def main(args: Array[String]): Unit = {
    // 创建一个流处理执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //从文件中读取数据并转换为 类
    val inputStreamFromFile: DataStream[String] = env.readTextFile("E:\\Project\\flink\\src\\main\\resources\\wordcount.txt")
    //转换  SensorReading为用户自定义的类，是从文件转换而来的
    val dataStream: DataStream[SensorReading] = inputStreamFromFile
      .map( data => {
        var dataArray = data.split(",")
        SensorReading(dataArray(0),dataArray(1).toLong,dataArray(2).toDouble)
      })
    //定义一个 redis 的配置类 继承了FlinkJedisConfigBase 正是 SensorReading需要传入的参数，底层将有些数据保存成了状态数据。
    val conf = new FlinkJedisPoolConfig.Builder().setHost("192.168.52.131").setPort(6379).setPassword("zzx").build()
    //定义 RedisMapper 数据保存的类型
    val myMapper = new RedisMapper[SensorReading] {
      //定义保存数据到 redis的命令，hset table key value
      override def getCommandDescription: RedisCommandDescription = {
        // hset tablesname
        new RedisCommandDescription(RedisCommand.HSET , "sensor_temp")
      }
      //设置key
      override def getKeyFromData(data: SensorReading): String = data.id
      //设置value
      override def getValueFromData(data: SensorReading): String = data.temperature.toString
    }
    dataStream.addSink(new RedisSink[SensorReading](conf,myMapper))
    env.execute("Redis Sink test")
  }
}

查看源码可知RedisSink是继承自RichSinkFunction类

public class RedisSink extends RichSinkFunction {

【3】查看Redis输出信息