小编给大家分享一下flink中如何实现有状态stateful的计算，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
import org.apache.flink.api.common.functions.RichFlatMapFunctionimport org.apache.flink.api.common.state.ValueStateimport org.apache.flink.util.Collectorimport org.apache.flink.configuration.Configurationimport org.apache.flink.api.common.state.ValueStateDescriptorimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironment class CountWindowAverage extends RichFlatMapFunction[(Long, Double), (Long, Double)] { private var sum: ValueState[(Long, Double)] = _ override def flatMap(input: (Long, Double), out: Collector[(Long, Double)]): Unit = { // access the state value val tmpCurrentSum = sum.value // If it hasn't been used before, it will be null val currentSum = if (tmpCurrentSum != null) { tmpCurrentSum } else { (0L, 0d) } // update the count val newSum = (currentSum._1 + 1, currentSum._2 + input._2) // update the state sum.update(newSum) // if the count reaches 2, emit the average and clear the state if (newSum._1 >= 2) { out.collect((input._1, newSum._2 / newSum._1)) //将状态清除 //sum.clear() } } override def open(parameters: Configuration): Unit = { sum = getRuntimeContext.getState( new ValueStateDescriptor[(Long, Double)]("average", classOf[(Long, Double)]) ) }}import org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.api.scala._object ECountWindowAverage { def main(args: Array[String]): Unit = { val env = StreamExecutionEnvironment.getExecutionEnvironment env.fromCollection(List( (1L, 3d), (1L, 5d), (1L, 7d), (1L, 4d), (1L, 2d) )).keyBy(_._1) .flatMap(new CountWindowAverage()) .print() // the printed output will be (1,4) and (1,5) env.execute("ExampleManagedState") }}以上是“flink中如何实现有状态stateful的计算”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注编程网行业资讯频道！
