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在做维表关联如果要求低延时，即维表数据的变更能够被立刻感知到，所以就要求在查询时没有缓存策略，直接查询数据库维表信息。
本篇以实时查询redis为例，要求redis 客户端支持异步查询，可以使用io.lettuce包，支持redis不同模式：单点模式、sentinel模式、集群模式，需要在pom中引入：

<dependency><groupId>io.lettuce</groupId><artifactId>lettuce-core</artifactId><version>5.0.5.RELEASE</version>
</dependency>
<dependency><groupId>io.netty</groupId><artifactId>netty-all</artifactId><version>4.1.24.Final</version></dependency>

关于其不同模式的用法可以参考：https://juejin.im/post/5d8eb73ff265da5ba5329c66
里面做了比较详细的说明，为方便测试使用单点模式，仍以广告业务为例，根据广告位ID从redis里面查询对位的广告主ID。

Redis中数据准备：

hmset 1 aid 1 cid 1
hmset 2 aid 1 cid 2

使用hash结构，key表示广告位ID、aid表示广告主ID、cid表示广告计划ID

定义RichAsyncFunction类型的RedisSide，异步查询Redis

class RedisSide extends RichAsyncFunction[AdData, AdData] {private var redisClient: RedisClient = _private var connection: StatefulRedisConnection[String, String] = _private var async: RedisAsyncCommands[String, String] = _override def open(parameters: Configuration): Unit = {val redisUri = "redis://localhost"redisClient = RedisClient.create(redisUri)connection = redisClient.connect()async = connection.async()}override def asyncInvoke(input: AdData, resultFuture: ResultFuture[AdData]): Unit = {val tid = input.tId.toStringasync.hgetall(tid).thenAccept(new Consumer[util.Map[String, String]]() {override def accept(t: util.Map[String, String]): Unit = {if (t == null || t.size() == 0) {resultFuture.complete(util.Arrays.asList(input))return}t.foreach(x => {if ("aid".equals(x._1)) {val aid = x._2.toIntvar newData = AdData(aid, input.tId, input.clientId, input.actionType, input.time)resultFuture.complete(util.Arrays.asList(newData))}})}})}//关闭资源override def close(): Unit = {if (connection != null) connection.close()if (redisClient != null) redisClient.shutdown()}}

主流程

case class AdData(aId: Int, tId: Int, clientId: String, actionType: Int, time: Long)object Demo1 {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironmentenv.setParallelism(1)val kafkaConfig = new Properties();kafkaConfig.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");kafkaConfig.put(ConsumerConfig.GROUP_ID_CONFIG, "test1");val consumer = new FlinkKafkaConsumer[String]("topic1", new SimpleStringSchema(), kafkaConfig);val ds = env.addSource(consumer).map(x => {val a: Array[String] = x.split(",")AdData(0, a(0).toInt, a(1), a(2).toInt, a(3).toLong) //默认给0})val redisSide: AsyncFunction[AdData, AdData] = new RedisSideAsyncDataStream.unorderedWait(ds, redisSide, 5L, SECONDS, 1000).print()env.execute("Demo1")}
}

测试验证
生产数据：

1,clientId1,1,1571646006000
3,clientId1,1,1571646006000

输出：

AdData(1,1,clientId1,1,1571646006000)
AdData(0,3,clientId1,1,1571646006000)

验证完毕，也算是补上维表系列里面的空缺。

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