实时数仓之Flink实现版本维表数据的Redis全局缓存

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.api.common.state.{BroadcastState, MapStateDescriptor}
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction
import org.apache.flink.util.Collector
 
object RedisBroadcastCache {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val dataStream = env.fromElements(("a", 1), ("b", 2))
    val broadcastStream = env.fromElements("a", "b")
 
    val mapStateDescriptor = new MapStateDescriptor[String, String]("BroadcastState", classOf[String], classOf[String])
 
    dataStream
      .keyBy(_._1)
      .connect(broadcastStream.broadcast(mapStateDescriptor))
      .process(new BroadcastProcessFunction[String, (String, Int), String] {
        override def processElement(value: (String, Int), ctx: BroadcastProcessFunction[String, (String, Int), String]#ReadOnlyContext, out: Collector[String]): Unit = {
          // 获取广播状态
          val broadcastState: BroadcastState[String, String] = ctx.getBroadcastState(mapStateDescriptor)
          // 从Redis获取数据并更新到广播状态
          val dataFromRedis = getDataFromRedis(broadcastState.get(value._1))
          broadcastState.put(value._1, dataFromRedis)
          // 处理业务逻辑
          out.collect(s"${value._1} -> ${dataFromRedis}")
        }
 
        override def processBroadcastElement(value: String, ctx: BroadcastProcessFunction[String, (String, Int), String]#Context, out: Collector[String]): Unit = {
          // 当广播数据有更新时，可以在这里实现逻辑
        }
 
        // 模拟从Redis获取数据的方法
        def getDataFromRedis(key: String): String = {
          // 假设这里从Redis获取数据
          "version_data"
        }
      })
      .print()
 
    env.execute("Flink Redis Broadcast Cache Example")
  }
}

这个代码示例展示了如何在Flink程序中使用BroadcastProcessFunction来处理数据流，并利用广播状态来缓存Redis中的版本数据。在processElement方法中，它从广播状态获取缓存的版本数据，如果不存在，则从模拟的Redis获取数据，并更新到广播状态。这样，后续的数据处理可以复用这些版本数据，从而提高系统的性能。