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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