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From "deenar.toraskar" <deenar.toras...@db.com>
Subject Re: converting DStream[String] into RDD[String] in spark streaming
Date Sun, 22 Mar 2015 08:43:42 GMT
Sean

Dstream.saveAsTextFiles internally calls foreachRDD and saveAsTextFile for
each interval

  def saveAsTextFiles(prefix: String, suffix: String = "") {
    val saveFunc = (rdd: RDD[T], time: Time) => {
      val file = rddToFileName(prefix, suffix, time)
      rdd.saveAsTextFile(file)
    }
    this.foreachRDD(saveFunc)
  }

    val sparkConf = new SparkConf().setAppName("TwitterRawJSON")
    val ssc = new StreamingContext(sparkConf, Seconds(30))
    stream.saveAsTextFiles("hdfs://localhost:9000/twitterRawJSON")

1) if there are no sliding window calls in this streaming context, will
there just one file written per interval?
2) if there is a sliding window call in the same context, such as

    val hashTags = stream.flatMap(json =>
DataObjectFactory.createStatus(json).getText.split("
").filter(_.startsWith("#")))
    
    val topCounts60 = hashTags.map((_, 1)).reduceByKeyAndWindow(_ + _,
Seconds(600))
                     .map{case (topic, count) => (count, topic)}
                     .transform(_.sortByKey(false))

will the some files get written multiples time (as long as the interval is
in the batch)

Deenar

>>DStream.foreachRDD gives you an RDD[String] for each interval of 
course. I don't think it makes sense to say a DStream can be converted 
into one RDD since it is a stream. The past elements are inherently 
not supposed to stick around for a long time, and future elements 
aren't known. You may consider saving each RDD[String] to HDFS, and 
then simply loading it from HDFS as an RDD[String]. 



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