Did you try inputs.repartition(1).foreachRDD(..)?

Thanks
Best Regards

On Fri, Jul 17, 2015 at 9:51 PM, PAULI, KEVIN CHRISTIAN [AG-Contractor/1000] <kevin.christian.pauli@monsanto.com> wrote:
Spark newbie here, using Spark 1.3.1.

I’m consuming a stream and trying to pipe the data from the entire window to R for analysis.  The R algorithm needs the entire dataset from the stream (everything in the window) in order to function properly; it can’t be broken up.

So I tried doing a coalesce(1) before calling pipe(), but it still seems to be breaking up the data and invoking R, but it still seems to to be breaking up the data and invoking R multiple times with small pieces of data.  Is there some other approach I should try?

Here’s a small snippet:

    val inputs: DStream[String] = MQTTUtils.createStream(ssc, mqttBrokerUrl, inputsTopic, StorageLevel.MEMORY_AND_DISK_SER)
      .window(duration)
    inputs.foreachRDD {
      windowRdd => {
        if (windowRdd.count() > 0) processWindow(windowRdd)
      }
    }

...

  def processWindow(windowRdd: RDD[String]) = {
    // call R script to process data
    windowRdd.coalesce(1)
    val outputsRdd: RDD[String] = windowRdd.pipe(SparkFiles.get(Paths.get(rScript).getFileName.toString))
    outputsRdd.cache()

    if (outputsRdd.count() > 0) processOutputs(outputsRdd)
  }

...



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