SparkStreaming updateStateByKey 保存記錄信息

object SparkStreaming_StateFul {

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    val conf = new SparkConf().setMaster("local[2]")
      .setAppName(this.getClass.getSimpleName)
      .set("spark.executor.memory", "2g")
      .set("spark.cores.max", "8")
      .setJars(Array("E:\\ScalaSpace\\Spark_Streaming\\out\\artifacts\\Spark_Streaming.jar"))
    val context = new SparkContext(conf)

    val updateFunc = (values : Seq[Int],state : Option[Int]) => {
      val currentCount  = values.foldLeft(0)(_+_)
      val previousCount = state.getOrElse(0) 查看是否存在，若是存在直接獲取
      Some(currentCount + previousCount)
    }

    //step1 create streaming context
    val ssc = new StreamingContext(context,Seconds(10))
    ssc.checkpoint(".")


    //step2 create a networkInputStream on get ip:port and count the words in input stream of \n delimited text
    val lines = ssc.socketTextStream("218.193.154.79",12345)

    val data = lines.flatMap(_.split(" "))
    val wordDstream = data.map(x => (x,1))

    //使用updateStateByKey 來更新狀態
    val stateDstream = wordDstream.updateStateByKey[Int](updateFunc)

    stateDstream.print()
    ssc.start()
    ssc.awaitTermination()
  }
}

ssc.checkPoint 若是在集羣上運行會報出以下的錯誤：

org.apache.spark.SparkException: Checkpoint RDD ReliableCheckpointRDD[9] at print at SparkStreaming_StateFul.scala:43(0) has different number of partitions from original RDD MapPartitionsRDD[8] at updateStateByKey at SparkStreaming_StateFul.scala:41(2)
	at org.apache.spark.rdd.ReliableRDDCheckpointData.doCheckpoint(ReliableRDDCheckpointData.scala:73)
	at org.apache.spark.rdd.RDDCheckpointData.checkpoint(RDDCheckpointData.scala:74)

這是由於沒有將文件保存到hdfs環境中致使的

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