flink standalone能不能用于生产 flink key

Architecture

Client连接其中的⼀个代理服务区然后发送查询请求给Proxy服务器，查询指定key所对应的状态数据，底层Flink按照KeyGroup的⽅式管理Keyed State，这些KeyGroup被分配给了所有的TaskMnager的服务。每个TaskManage服务多个KeyGroup状态的存储。为了找到查询key所在的KeyGroup所TaskManager服务，Proxy服务会去询问JobManager查询TaskManager的信息，然后直接访问TaskManager上的QueryableStateServer服务器获取状态数据，最后将获取的状态数据返回给Client端。

• QueryableStateClient- 运⾏在Flink集群以外，负责提交⽤户的查询给Flink集群。
• QueryableStateClientProxy- 运⾏在Flink集群中的TaskManager中的⼀个代理服务，负责接收客户端的查询，代理负责相应TaskManager获取请求的state，并将其state返回给客户端。
• QueryableStateServer -运⾏在Flink集群中的TaskManager中服务，仅仅负责读取当前TaskManage主机上存储到状态数据。

The client connects to one of the proxies and sends a request for the state associated with a specifickey, k . As stated in Working with State , keyed state is organized in Key Groups , and eachTaskManager is assigned a number of these key groups. To discover which TaskManager is responsible for the key group holding k , the proxy will ask the JobManager . Based on the answer, the proxy will then query the QueryableStateServer running on that TaskManager for the state associated with k , and forward the response back to the client.

激活 Queryable State

1. 将Flink的 opt/flink-queryable-state-runtime_2.11-1.10.0.jar 拷贝到Flink的
   lib/ ⽬录。

[root@CentOS flink-1.10.0]# cp opt/flink-queryable-state-runtime_2.11-1.10.0.jar lib/

2. 在Flink的flink-conf.yaml配置⽂件中添加以下配置：

queryable-state.enable: true

3. 重启Flink服务，为了校验服务是否开启你可以查看task manager⽇志，可以看到 "Started the Queryable State Proxy Server @ ..." .

[root@CentOS flink-1.10.0]# ./bin/start-cluster.sh
Starting cluster.
Starting standalonesession daemon on host CentOS.
Starting taskexecutor daemon on host CentOS.

查看TaskManager启动⽇志

Making State Queryable

为了使State对外界可⻅，需要使⽤以下命令显式地使其可查询：

• 创建QueryableStateStream，该QueryableStateStream充当⼀个Sink的输出，仅仅是将数据存储到state中。
• 或者stateDescriptor.setQueryable(String queryableStateName)⽅法使得我们的状态可查询。

Queryable State Stream

⽤户可以调⽤keyedstream的.asQueryableState(stateName, stateDescriptor) ⽅法，提供⼀个可以查询状态。

// ValueState
QueryableStateStream asQueryableState(
 String queryableStateName,
 ValueStateDescriptor stateDescriptor)
// Shortcut for explicit ValueStateDescriptor variant
QueryableStateStream asQueryableState(String queryableStateName)
// FoldingState
QueryableStateStream asQueryableState(
 String queryableStateName,
 FoldingStateDescriptor stateDescriptor)
// ReducingState
QueryableStateStream asQueryableState(
 String queryableStateName,
 ReducingStateDescriptor stateDescriptor)

返回的QueryableStateStream可以看作是⼀个Sink，因为⽆法对QueryableStateStream进⼀步转换。在内部，QueryableStateStream被转换为运算符，该运算符使⽤所有传⼊记录来更新可查询状态实例。更新逻辑由asQueryableState调⽤中提供的StateDescriptor的类型隐含。在类似以下的程序中，keyedstream的所有记录将通过ValueState.update（value）⽤于更新状态实例：

stream.keyBy(0).asQueryableState("query-name")

object FlinkWordCountQueryableStream {
 def main(args: Array[String]): Unit = {
 val env = StreamExecutionEnvironment.getExecutionEnvironment
 //间隔5s执⾏⼀次checkpoint 精准⼀次
 env.enableCheckpointing(5000,CheckpointingMode.EXACTLY_ONCE)
 //设置检查点超时 4s
 env.getCheckpointConfig.setCheckpointTimeout(4000)
 //开启本次检查点 与上⼀次完成的检查点时间间隔不得⼩于 2s 优先级⾼于 checkpoint interval
 env.getCheckpointConfig.setMinPauseBetweenCheckpoints(2000)
 //如果检查点失败，任务宣告退出 setFailOnCheckpointingErrors(true)
 env.getCheckpointConfig.setTolerableCheckpointFailureNumber(0)
 //设置如果任务取消，系统该如何处理检查点数据
 //RETAIN_ON_CANCELLATION:如果取消任务的时候，没有加--savepoint，系统会保留检查点数据
 //DELETE_ON_CANCELLATION:取消任务，⾃动是删除检查点（不建议使⽤）

env.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.R
ETAIN_ON_CANCELLATION)
 var rsd=new ReducingStateDescriptor[(String,Int)]("reducestate",new
ReduceFunction[(String, Int)] {
 override def reduce(v1: (String, Int), v2: (String, Int)): (String, Int) = {
 (v1._1,(v1._2+v2._2))
 }
 },createTypeInformation[(String,Int)])
 env.socketTextStream("CentOS", 9999)
 .flatMap(line => line.split("\\s+"))
 .map(word => (word, 1))
 .keyBy(0)
 .asQueryableState("wordcount", rsd)//状态名字，后期查询需要
 //5.执⾏流计算任务
 env.execute("Stream WordCount")
 }
}

Managed Keyed State

class WordCountMapFunction extends RichMapFunction[(String,Int),(String,Int)]{
 var vs:ValueState[Int]=_
 override def open(parameters: Configuration): Unit = {
 //1.创建对应状态描述符
 val vsd = new ValueStateDescriptor[Int]("wordcount", createTypeInformation[Int])
 vsd.setQueryable("query-wc")
 //2.获取RuntimeContext
 var context: RuntimeContext = getRuntimeContext
 //3.获取指定类型状态
 vs=context.getState(vsd)
 }
 override def map(value: (String, Int)): (String, Int) = {
 //获取历史值
 val historyData = vs.value()
 //更新状态
 vs.update(historyData+value._2)
 //返回最新值
 (value._1,vs.value())
 }
}

object FlinkWordCountQueryable {
 def main(args: Array[String]): Unit = {
 //1.创建流计算执⾏环境
 val env = StreamExecutionEnvironment.getExecutionEnvironment
 //2.创建DataStream - 细化
 val text = env.socketTextStream("CentOS", 9999)
 //3.执⾏DataStream的转换算⼦
 val counts = text.flatMap(line=>line.split("\\s+"))
 .map(word=>(word,1))
 .keyBy(0)
 .map(new WordCountMapFunction)
 //4.将计算的结果在控制打印
 counts.print()
 //5.执⾏流计算任务
 env.execute("Stream WordCount")
 }
}

Querying State

• 引入依赖：

<dependency>
 <groupId>org.apache.flink</groupId>
 <artifactId>flink-core</artifactId>
 <version>1.10.0</version>
</dependency>
<dependency>
 <groupId>org.apache.flink</groupId>
 <artifactId>flink-queryable-state-client-java</artifactId>
 <version>1.10.0</version>
</dependency>

• 查询代码如下：

//链接proxy服务器
 val client = new QueryableStateClient("CentOS", 9069)
 var jobID=JobID.fromHexString("dc60cd61dc2d591014c062397e3bd6b9")
 var queryName="wordcount" //状态名字
 var queryKey="this" //⽤户需要查询的key
 var rsd=new ReducingStateDescriptor[(String,Int)]("reducestate",new
ReduceFunction[(String, Int)] {
 override def reduce(v1: (String, Int), v2: (String, Int)): (String, Int) = {
 (v1._1,(v1._2+v2._2))
 }
 },createTypeInformation[(String,Int)])
 val resultFuture = client.getKvState(jobID, queryName, queryKey,
createTypeInformation[String], rsd)

 //同步获取结果
 val state: ReducingState[(String, Int)] = resultFuture.get()
 println("结果："+state.get())
client.shutdownAndWait()

异步获取结果

resultFuture.thenAccept(new Consumer[ReducingState[(String, Int)]] {
 override def accept(t: ReducingState[(String, Int)]): Unit = {
 println("结果："+t.get())
 }
})
Thread.sleep(10000)
client.shutdownAndWait()