Flink DataSet API 之 Accumulators & Counters(累加器)

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基本介绍

1、Accumulator即累加器，与Mapreduce counter的应用场景差不多，都能很好地观察task在运行期间的数据变化。可以在Flink job任务中的算子函数中操作累加器，但是只能在任务执行结束之后才能获得累加器的最终结果。

2、Counter是一个具体的累加器(Accumulator)实现。例如：IntCounter, LongCounter 和 DoubleCounter

用法

1：创建累加器：      private IntCounter numLines = new IntCounter();

2：注册累加器：      getRuntimeContext().addAccumulator("num-lines", this.numLines);

3：使用累加器：             this.numLines.add(1);

4：获取累加器的结果：  myJobExecutionResult.getAccumulatorResult("num-lines")

使用Demo

import org.apache.flink.api.common.accumulators.IntCounter
import org.apache.flink.api.common.functions.RichMapFunction
import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.configuration.Configuration

object BatchDemoCounter {
  def main(args: Array[String]): Unit = {
    val env = ExecutionEnvironment.getExecutionEnvironment

    import org.apache.flink.api.scala._

    val data = env.fromElements("a","b","c","d")

    val res = data.map(new RichMapFunction[String, String] {
      val numLines = new IntCounter()//定义
      override def open(parameters: Configuration): Unit = {
        super.open(parameters)
        getRuntimeContext.addAccumulator("num-lines", numLines)// 注册
      }

      override def map(in: String): String = {
        this.numLines.add(1)// 使用
        in
      }
    }).setParallelism(4)
    res.writeAsText("d:/sshdata/count").setParallelism(1)
    val jobResult = env.execute("BatchDemoCounter")
    val num = jobResult.getAccumulatorResult[Int]("num-lines")// 获取
    println(num)
  }
}