Flink KeyedProcessFunction 排序

Question

我是 Flink 的新手，正在尝试了解 Flink 如何在并行性下的 KeyedProcessFunction 抽象中命令对 processElement() 的调用。考虑这个产生部分和流的例子：

package sample

import org.apache.flink.api.common.state.{ValueState, ValueStateDescriptor}
import org.apache.flink.streaming.api.functions.KeyedProcessFunction
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment, createTypeInformation}
import org.apache.flink.util.Collector

object Playground {
  case class Record(groupId: String, score: Int) {}

  def main(args: Array[String]): Unit = {
    // 1. Create the environment
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.createLocalEnvironment()
    env.setParallelism(10)

    // 2. Source
    val record1 = Record("groupX", 1)
    val record2 = Record("groupX", 2)
    val record3 = Record("groupX", 3)
    val records: DataStream[Record] = env.fromElements(record1, record2, record3, record1, record2, record3)

    // 3. Application Logic
    val partialSums: DataStream[Int] = records
      .keyBy(record => record.groupId)
      .process(new KeyedProcessFunction[String, Record, Int] {
        // Store partial sum of score for Records seen
        lazy val partialSum: ValueState[Int] = getRuntimeContext.getState(
          new ValueStateDescriptor[Int]("partialSum", classOf[Int]))

        // Ingest new record
        override
        def processElement(value: Record,
                           ctx: KeyedProcessFunction[String, Record, Int]#Context,
                           out: Collector[Int]): Unit =
        {
          val currentSum: Int = partialSum.value()
          partialSum.update(currentSum + value.score)
          out.collect(partialSum.value())
        }
      })

    // 4. Sink
    partialSums.print()

    // 5. Build JobGraph and execute
    env.execute("sample-job")
  }
}

我希望它的输出是流：1, 3, 6, 7, 9, 12。确实是，在这里。

假设情况总是如此是否安全，尤其是从具有大量并行性的源读取时？

Answer 1

在您的示例中，每个键中的顺序得到保证。由于只有一把钥匙，您将始终获得 1, 3, 6, 7, 9, 12.

当您从并行度大于 1 的源读取时，各种源实例将相互竞争。当来自两个或多个源的流被连接（例如，通过 keyBy、union、rebalance 等）时，结果是不确定的（但来自每个源的事件将保持其相对顺序）。

例如，如果您有

stream X: 1, 2, 3, 4
stream Y: a, b, c, d

然后将这两个流放在一起，您可能会得到

1, 2, 3, 4, a, b, c, d，或a, b, 1, 2, 3, c, 4, d，等等

Flink KeyedProcessFunction 排序

Flink KeyedProcessFunction Ordering

apache-flink

flink-streaming