Thanks much Amit, Sebastian. It worked.
Regards,
~Vinti
On Sat, Feb 27, 2016 at 12:44 PM, Amit Assudani <aassud...@impetus.com>
wrote:
> Your context is not being created using checkpoints, use get or create,
>
> From: Vinti Maheshwari <vinti.u...@gmail.com>
> Date: Saturday, February 27, 2016 at 3:28 PM
> To: user <user@spark.apache.org>
> Subject: Spark streaming not remembering previous state
>
> Hi All,
>
> I wrote spark streaming program with stateful transformation.
> It seems like my spark streaming application is doing computation
> correctly with check pointing.
> But i terminate my program and i start it again, it's not reading the
> previous checkpointing data and staring from the beginning. Is it the
> expected behaviour?
>
> Do i need to change anything in my program so that it will remember the
> previous data and start computation from there?
>
> Thanks in advance.
>
> For reference my program:
>
>
> def main(args: Array[String]): Unit = {
> val conf = new SparkConf().setAppName("HBaseStream")
> val sc = new SparkContext(conf)
> val ssc = new StreamingContext(sc, Seconds(5))
> val inputStream = ssc.socketTextStream("ttsv-vccp-01.juniper.net", 9999)
>
> ssc.checkpoint("hdfs://ttsv-lab-vmdb-01.englab.juniper.net:8020/user/spark/checkpoints_dir")
> inputStream.print(1)
> val parsedStream = inputStream
> .map(line => {
> val splitLines = line.split(",")
> (splitLines(1), splitLines.slice(2,
> splitLines.length).map((_.trim.toLong)))
> })
> import breeze.linalg.{DenseVector => BDV}
> import scala.util.Try
>
> val state: DStream[(String, Array[Long])] = parsedStream.updateStateByKey(
> (current: Seq[Array[Long]], prev: Option[Array[Long]]) => {
> prev.map(_ +: current).orElse(Some(current))
> .flatMap(as => Try(as.map(BDV(_)).reduce(_ + _).toArray).toOption)
> })
> state.checkpoint(Duration(10000))
> state.foreachRDD(rdd => rdd.foreach(Blaher.blah))
>
> // Start the computation
> ssc.start()
> // Wait for the computation to terminate
> ssc.awaitTermination()
>
> }
> }
>
>
> Regards,
>
> ~Vinti
>
>
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