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！
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import org.apache.flink.api.common.functions.RichFlatMapFunction
import org.apache.flink.api.common.state.ValueState
import org.apache.flink.util.Collector
import org.apache.flink.configuration.Configuration
import org.apache.flink.api.common.state.ValueStateDescriptor
import 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.StreamExecutionEnvironment
import 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()
/*.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，欢迎关注创新互联行业资讯频道！
